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Artificial Intelligence Algorithm for Remote Sensing Imagery Processing
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Artificial Intelligence Algorithm for Remote Sensing Imagery Processing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 78170

Special Issue Editors

Dr. Mercedes E. Paoletti

E-Mail Website1 Website2
Guest Editor
Department of computer technology and communications, Polytechnic School of Cáceres, University of Extremadura, 10003 Cáceres, Spain
Interests: hyperspectral remote sensing; deep learning; Graphics Processing Units (GPUs); High Performance Computing (HPC) techniques
Special Issues, Collections and Topics in MDPI journals
Dr. Juan M. Haut

E-Mail Website1 Website2 Website3
Guest Editor
Department of Computer Technology and Communications, Polytechnic School of Cáceres, University of Extremadura, 10003 Cáceres, Spain
Interests: hyperspectral image analysis; machine (deep) learning; neural networks; multisensor data fusion; high performance computing; cloud computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the last decades, significant efforts have been made in the Remote Sensing field in order to obtain rich and accurate information about the earth’s surface. The impressive advances in computer technology, in terms of hardware devices and software desing, have enabled the launch of multiple earth observation missions, which are currently collecting huge amounts of data daily. These raw data captures the matter-energy interactions at the earth’s surface and are characterised by their great variety in terms of typology (e.g. lidar and radar data or optical and thermal imaging), acquisition platforms (e.g. unnamed aerial vehicles or UAV, traditional aerial platforms and satelites) and spatial, spectral and temporal resolutions (from high to low spatial resolution data, from single band pachromatic images to hyperspectral images with hundreds of spectral channels, and revisit times for the same observation area from hours to days). The opportunities of using remote sensing data to contribute to economic and social activities is highly attractive, as they collect rich information over large spatial areas, which enables the detailed characterization of natural features, different materials and physical objects on the ground. Indeed, the current literature on the use and exploitation of these data has proved that they are truly useful in different decision-making tasks, such as precision agriculture, natural resource management, urban planning, risk prevention, disasters management, national defense, and homeland security, among many other application areas. 

However, the raw data obtained by remote sensors must be properly processed in order to exploit the information contained in them, refining the data to the end-user level. In this sense, this data processing has to deal with several challenges and limitations, such as high data complexity, noise data due to sensor limitations or uncontrolled atmospheric changes, low spatial resolutions, spectral mixtures, redundancies and correlations between spectral bands, lack of labelled samples, cloud occlusions, high data dimensionality, high intra-class variability and inter-class similarity… To face these issues, the implementation of new, more powerful processing tools is absolutely mandatory, which must be able to  extract the relevant information contained into remote sensing data in a reliable and efficient way. 

In this regard, artificial intelligence (AI) techniques have had significant successes in multiple fields related to data processing, such as speech recognition and computer vision. These methods provide interesting procedures to automaticaly process large amounts of data and conduct data-drive decisions in an accurate way. Moreover, the increasing capabilities of computer systems have promoted a great evolution of these algorithms, from traditional pattern recognition methods to complex machine and task-driven deep learning models, which are achieving unprecedented results. In particular, many AI-based algorithms are achieving dramatic improvements in many remote sensing analysis, such as unmixing, data classification, object/target or anomaly/change detection, data super-resolution, data fusion, cloud removal, denoising, spectral reduction… However, the implementation of these algorithms to remote data processing must address the characteristics, challenges and limitations imposed by this kind of data, and therefore remains a challenging task. 

This Special Issue invites manuscripts that present new AI approaches or improved AI-based algorithms for processing the information contained into remote sensing data. As this is a broad area, there are no constraints regarding the field of application. In this sense, the aim of this special issue will focus on presenting the current state of AI methods for the analysis of remote sensing data in several fields of application.

Dr. Mercedes E. Paoletti
Dr. Juan M. Haut
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial Intelligence
  • Remote Sensing
  • data analysis
  • machine learning, deep learning

Published Papers (17 papers)

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22 pages, 3919 KiB  
Article
RS-DARTS: A Convolutional Neural Architecture Search for Remote Sensing Image Scene Classification
by Zhen Zhang, Shanghao Liu, Yang Zhang and Wenbo Chen
Remote Sens. 2022, 14(1), 141; https://doi.org/10.3390/rs14010141 - 29 Dec 2021
Cited by 24 | Viewed by 3585
Abstract
Due to the superiority of convolutional neural networks, many deep learning methods have been used in image classification. The enormous difference between natural images and remote sensing images makes it difficult to directly utilize or modify existing CNN models for remote sensing scene [...] Read more.
Due to the superiority of convolutional neural networks, many deep learning methods have been used in image classification. The enormous difference between natural images and remote sensing images makes it difficult to directly utilize or modify existing CNN models for remote sensing scene classification tasks. In this article, a new paradigm is proposed that can automatically design a suitable CNN architecture for scene classification. A more efficient search framework, RS-DARTS, is adopted to find the optimal network architecture. This framework has two phases. In the search phase, some new strategies are presented, making the calculation process smoother, and better distinguishing the optimal and other operations. In addition, we added noise to suppress skip connections in order to close the gap between trained and validation processing and ensure classification accuracy. Moreover, a small part of the neural network is sampled to reduce the redundancy in exploring the network space and speed up the search processing. In the evaluation phase, the optimal cell architecture is stacked to construct the final network. Extensive experiments demonstrated the validity of the search strategy and the impressive classification performance of RS-DARTS on four public benchmark datasets. The proposed method showed more effectiveness than the manually designed CNN model and other methods of neural architecture search. Especially, in terms of search cost, RS-DARTS consumed less time than other NAS methods. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing)
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22 pages, 7275 KiB  
Article
Semi-Autonomous Learning Algorithm for Remote Image Object Detection Based on Aggregation Area Instance Refinement
by Bei Cheng, Zhengzhou Li, Hui Li, Zhiquan Ding and Tianqi Qin
Remote Sens. 2021, 13(24), 5065; https://doi.org/10.3390/rs13245065 - 14 Dec 2021
Viewed by 1630
Abstract
Semi-autonomous learning for object detection has attracted more and more attention in recent years, which usually tends to find only one object instance with the highest score in each image. However, this strategy usually highlights the most representative part of the object instead [...] Read more.
Semi-autonomous learning for object detection has attracted more and more attention in recent years, which usually tends to find only one object instance with the highest score in each image. However, this strategy usually highlights the most representative part of the object instead of the whole object, which may lead to the loss of a lot of important information. To solve this problem, a novel end-to-end aggregate-guided semi-autonomous learning residual network is proposed to perform object detection. Firstly, a progressive modified residual network (MRN) is applied to the backbone network to make the detector more sensitive to the boundary features of the object. Then, an aggregate-based region-merging strategy (ARMS) is designed to select high-quality instances by selecting aggregation areas and merging these regions. The ARMS selects the aggregation areas that are highly related to the object through association coefficient, and then evaluates the aggregation areas through a similarity coefficient and fuses them to obtain high-quality object instance areas. Finally, a regression-locating branch is further developed to refine the location of the object, which can be optimized jointly with regional classification. Extensive experiments demonstrate that the proposed method is superior to state-of-the-art methods. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing)
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21 pages, 24683 KiB  
Article
Application of Supervised Machine Learning Technique on LiDAR Data for Monitoring Coastal Land Evolution
by Maurizio Barbarella, Alessandro Di Benedetto and Margherita Fiani
Remote Sens. 2021, 13(23), 4782; https://doi.org/10.3390/rs13234782 - 25 Nov 2021
Cited by 5 | Viewed by 2112
Abstract
Machine Learning (ML) techniques are now being used very successfully in predicting and supporting decisions in multiple areas such as environmental issues and land management. These techniques have also provided promising results in the field of natural hazard assessment and risk mapping. The [...] Read more.
Machine Learning (ML) techniques are now being used very successfully in predicting and supporting decisions in multiple areas such as environmental issues and land management. These techniques have also provided promising results in the field of natural hazard assessment and risk mapping. The aim of this work is to apply the Supervised ML technique to train a model able to classify a particular gravity-driven coastal hillslope geomorphic model (slope-over-wall) involving most of the soft rocks of Cilento (southern Italy). To train the model, only geometric data have been used, namely morphometric feature maps computed on a Digital Terrain Model (DTM) derived from Light Detection and Ranging (LiDAR) data. Morphometric maps were computed using third-order polynomials, so as to obtain products that best describe landforms. Not all morphometric parameters from literature were used to train the model, the most significant ones were chosen by applying the Neighborhood Component Analysis (NCA) method. Different models were trained and the main indicators derived from the confusion matrices were compared. The best results were obtained using the Weighted k-NN model (accuracy score = 75%). Analysis of the Receiver Operating Characteristic (ROC) curves also shows that the discriminating capacity of the test reached percentages higher than 95%. The model, resulting more accurate in the training area, will be extended to similar areas along the Tyrrhenian coastal land. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing)
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14 pages, 5410 KiB  
Article
MSST-Net: A Multi-Scale Adaptive Network for Building Extraction from Remote Sensing Images Based on Swin Transformer
by Wei Yuan and Wenbo Xu
Remote Sens. 2021, 13(23), 4743; https://doi.org/10.3390/rs13234743 - 23 Nov 2021
Cited by 33 | Viewed by 3421
Abstract
The segmentation of remote sensing images by deep learning technology is the main method for remote sensing image interpretation. However, the segmentation model based on a convolutional neural network cannot capture the global features very well. A transformer, whose self-attention mechanism can supply [...] Read more.
The segmentation of remote sensing images by deep learning technology is the main method for remote sensing image interpretation. However, the segmentation model based on a convolutional neural network cannot capture the global features very well. A transformer, whose self-attention mechanism can supply each pixel with a global feature, makes up for the deficiency of the convolutional neural network. Therefore, a multi-scale adaptive segmentation network model (MSST-Net) based on a Swin Transformer is proposed in this paper. Firstly, a Swin Transformer is used as the backbone to encode the input image. Then, the feature maps of different levels are decoded separately. Thirdly, the convolution is used for fusion, so that the network can automatically learn the weight of the decoding results of each level. Finally, we adjust the channels to obtain the final prediction map by using the convolution with a kernel of 1 × 1. By comparing this with other segmentation network models on a WHU building data set, the evaluation metrics, mIoU, F1-score and accuracy are all improved. The network model proposed in this paper is a multi-scale adaptive network model that pays more attention to the global features for remote sensing segmentation. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing)
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19 pages, 3017 KiB  
Article
Adaptable Convolutional Network for Hyperspectral Image Classification
by Mercedes E. Paoletti and Juan M. Haut
Remote Sens. 2021, 13(18), 3637; https://doi.org/10.3390/rs13183637 - 11 Sep 2021
Cited by 5 | Viewed by 2132
Abstract
Nowadays, a large number of remote sensing instruments are providing a massive amount of data within the frame of different Earth Observation missions. These instruments are characterized by the wide variety of data they can collect, as well as the impressive volume of [...] Read more.
Nowadays, a large number of remote sensing instruments are providing a massive amount of data within the frame of different Earth Observation missions. These instruments are characterized by the wide variety of data they can collect, as well as the impressive volume of data and the speed at which it is acquired. In this sense, hyperspectral imaging data has certain properties that make it difficult to process, such as its large spectral dimension coupled with problematic data variability. To overcome these challenges, convolutional neural networks have been proposed as classification models because of their ability to extract relevant spectral–spatial features and learn hidden patterns, along their great architectural flexibility. Their high performance relies on the convolution kernels to exploit the spatial relationships. Thus, filter design is crucial for the correct performance of models. Nevertheless, hyperspectral data may contain objects with different shapes and orientations, preventing filters from “seeing everything possible” during the decision making. To overcome this limitation, this paper proposes a novel adaptable convolution model based on deforming kernels combined with deforming convolution layers to fit their effective receptive field to the input data. The proposed adaptable convolutional network (named DKDCNet) has been evaluated over two well-known hyperspectral scenes, demonstrating that it is able to achieve better results than traditional strategies with similar computational cost for HSI classification. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing)
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25 pages, 3461 KiB  
Article
Efficient Transformer for Remote Sensing Image Segmentation
by Zhiyong Xu, Weicun Zhang, Tianxiang Zhang, Zhifang Yang and Jiangyun Li
Remote Sens. 2021, 13(18), 3585; https://doi.org/10.3390/rs13183585 - 09 Sep 2021
Cited by 99 | Viewed by 11052
Abstract
Semantic segmentation for remote sensing images (RSIs) is widely applied in geological surveys, urban resources management, and disaster monitoring. Recent solutions on remote sensing segmentation tasks are generally addressed by CNN-based models and transformer-based models. In particular, transformer-based architecture generally struggles with two [...] Read more.
Semantic segmentation for remote sensing images (RSIs) is widely applied in geological surveys, urban resources management, and disaster monitoring. Recent solutions on remote sensing segmentation tasks are generally addressed by CNN-based models and transformer-based models. In particular, transformer-based architecture generally struggles with two main problems: a high computation load and inaccurate edge classification. Therefore, to overcome these problems, we propose a novel transformer model to realize lightweight edge classification. First, based on a Swin transformer backbone, a pure Efficient transformer with mlphead is proposed to accelerate the inference speed. Moreover, explicit and implicit edge enhancement methods are proposed to cope with object edge problems. The experimental results evaluated on the Potsdam and Vaihingen datasets present that the proposed approach significantly improved the final accuracy, achieving a trade-off between computational complexity (Flops) and accuracy (Efficient-L obtaining 3.23% mIoU improvement on Vaihingen and 2.46% mIoU improvement on Potsdam compared with HRCNet_W48). As a result, it is believed that the proposed Efficient transformer will have an advantage in dealing with remote sensing image segmentation problems. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing)
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24 pages, 3630 KiB  
Article
Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification
by Feng Zhao, Junjie Zhang, Zhe Meng and Hanqiang Liu
Remote Sens. 2021, 13(17), 3396; https://doi.org/10.3390/rs13173396 - 26 Aug 2021
Cited by 17 | Viewed by 2389
Abstract
Recently, with the extensive application of deep learning techniques in the hyperspectral image (HSI) field, particularly convolutional neural network (CNN), the research of HSI classification has stepped into a new stage. To avoid the problem that the receptive field of naive convolution is [...] Read more.
Recently, with the extensive application of deep learning techniques in the hyperspectral image (HSI) field, particularly convolutional neural network (CNN), the research of HSI classification has stepped into a new stage. To avoid the problem that the receptive field of naive convolution is small, the dilated convolution is introduced into the field of HSI classification. However, the dilated convolution usually generates blind spots in the receptive field, resulting in discontinuous spatial information obtained. In order to solve the above problem, a densely connected pyramidal dilated convolutional network (PDCNet) is proposed in this paper. Firstly, a pyramidal dilated convolutional (PDC) layer integrates different numbers of sub-dilated convolutional layers is proposed, where the dilated factor of the sub-dilated convolution increases exponentially, achieving multi-sacle receptive fields. Secondly, the number of sub-dilated convolutional layers increases in a pyramidal pattern with the depth of the network, thereby capturing more comprehensive hyperspectral information in the receptive field. Furthermore, a feature fusion mechanism combining pixel-by-pixel addition and channel stacking is adopted to extract more abstract spectral–spatial features. Finally, in order to reuse the features of the previous layers more effectively, dense connections are applied in densely pyramidal dilated convolutional (DPDC) blocks. Experiments on three well-known HSI datasets indicate that PDCNet proposed in this paper has good classification performance compared with other popular models. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing)
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24 pages, 26957 KiB  
Article
CscGAN: Conditional Scale-Consistent Generation Network for Multi-Level Remote Sensing Image to Map Translation
by Yuanyuan Liu, Wenbin Wang, Fang Fang, Lin Zhou, Chenxing Sun, Ying Zheng and Zhanlong Chen
Remote Sens. 2021, 13(10), 1936; https://doi.org/10.3390/rs13101936 - 15 May 2021
Cited by 3 | Viewed by 2760
Abstract
Automatic remote sensing (RS) image to map translation is a crucial technology for intelligent tile map generation. Although existing methods based on a generative network (GAN) generated unannotated maps at a single level, they have limited capacity in handling multi-resolution map generation at [...] Read more.
Automatic remote sensing (RS) image to map translation is a crucial technology for intelligent tile map generation. Although existing methods based on a generative network (GAN) generated unannotated maps at a single level, they have limited capacity in handling multi-resolution map generation at different levels. To address the problem, we proposed a novel conditional scale-consistent generation network (CscGAN) to simultaneously generate multi-level tile maps from multi-scale RS images, using only a single and unified model. Specifically, the CscGAN first uses the level labels and map annotations as prior conditions to guide hierarchical feature learning with different scales. Then, a multi-scale discriminator and two multi-scale generators are introduced to describe both high-resolution and low-resolution representations, aiming to improve the similarity of generated maps and thus produce high-quality multi-level tile maps. Meanwhile, a level classifier is designed for further exploring the characteristics of tile maps at different levels. Moreover, the CscGAN is optimized by jointly multi-scale adversarial loss, level classification loss, and scale-consistent loss in an end-to-end manner. Extensive experiments on multiple datasets and study areas demonstrate that the CscGAN outperforms the state-of-the-art methods in multi-level map translation, with great robustness and efficiency. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing)
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22 pages, 29182 KiB  
Article
Generative Adversarial Learning in YUV Color Space for Thin Cloud Removal on Satellite Imagery
by Xue Wen, Zongxu Pan, Yuxin Hu and Jiayin Liu
Remote Sens. 2021, 13(6), 1079; https://doi.org/10.3390/rs13061079 - 12 Mar 2021
Cited by 24 | Viewed by 3348
Abstract
Clouds are one of the most serious disturbances when using satellite imagery for ground observations. The semi-translucent nature of thin clouds provides the possibility of 2D ground scene reconstruction based on a single satellite image. In this paper, we propose an effective framework [...] Read more.
Clouds are one of the most serious disturbances when using satellite imagery for ground observations. The semi-translucent nature of thin clouds provides the possibility of 2D ground scene reconstruction based on a single satellite image. In this paper, we propose an effective framework for thin cloud removal involving two aspects: a network architecture and a training strategy. For the network architecture, a Wasserstein generative adversarial network (WGAN) in YUV color space called YUV-GAN is proposed. Unlike most existing approaches in RGB color space, our method performs end-to-end thin cloud removal by learning luminance and chroma components independently, which is efficient at reducing the number of unrecoverable bright and dark pixels. To preserve more detailed features, the generator adopts a residual encoding–decoding network without down-sampling and up-sampling layers, which effectively competes with a residual discriminator, encouraging the accuracy of scene identification. For the training strategy, a transfer-learning-based method was applied. Instead of using either simulated or scarce real data to train the deep network, adequate simulated pairs were used to train the YUV-GAN at first. Then, pre-trained convolutional layers were optimized by real pairs to encourage the applicability of the model to real cloudy images. Qualitative and quantitative results on RICE1 and Sentinel-2A datasets confirmed that our YUV-GAN achieved state-of-the-art performance compared with other approaches. Additionally, our method combining the YUV-GAN with a transfer-learning-based training strategy led to better performance in the case of scarce training data. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing)
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21 pages, 8976 KiB  
Article
Integrating Weighted Feature Fusion and the Spatial Attention Module with Convolutional Neural Networks for Automatic Aircraft Detection from SAR Images
by Jielan Wang, Hongguang Xiao, Lifu Chen, Jin Xing, Zhouhao Pan, Ru Luo and Xingmin Cai
Remote Sens. 2021, 13(5), 910; https://doi.org/10.3390/rs13050910 - 28 Feb 2021
Cited by 28 | Viewed by 2985
Abstract
The automatic detection of aircrafts from SAR images is widely applied in both military and civil fields, but there are still considerable challenges. To address the high variety of aircraft sizes and complex background information in SAR images, a new fast detection framework [...] Read more.
The automatic detection of aircrafts from SAR images is widely applied in both military and civil fields, but there are still considerable challenges. To address the high variety of aircraft sizes and complex background information in SAR images, a new fast detection framework based on convolution neural networks is proposed, which achieves automatic and rapid detection of aircraft with high accuracy. First, the airport runway areas are detected to generate the airport runway mask and rectangular contour of the whole airport are generated. Then, a new deep neural network proposed in this paper, named Efficient Weighted Feature Fusion and Attention Network (EWFAN), is used to detect aircrafts. EWFAN integrates the weighted feature fusion module, the spatial attention mechanism, and the CIF loss function. EWFAN can effectively reduce the interference of negative samples and enhance feature extraction, thereby significantly improving the detection accuracy. Finally, the airport runway mask is applied to the detected results to reduce false alarms and produce the final aircraft detection results. To evaluate the performance of the proposed framework, large-scale Gaofen-3 SAR images with 1 m resolution are utilized in the experiment. The detection rate and false alarm rate of our EWFAN algorithm are 95.4% and 3.3%, respectively, which outperforms Efficientdet and YOLOv4. In addition, the average test time with the proposed framework is only 15.40 s, indicating satisfying efficiency of automatic aircraft detection. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing)
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32 pages, 10384 KiB  
Article
AVILNet: A New Pliable Network with a Novel Metric for Small-Object Segmentation and Detection in Infrared Images
by Ikhwan Song and Sungho Kim
Remote Sens. 2021, 13(4), 555; https://doi.org/10.3390/rs13040555 - 04 Feb 2021
Cited by 11 | Viewed by 3085
Abstract
Infrared small-object segmentation (ISOS) has a persistent trade-off problem—that is, which came first, recall or precision? Constructing a fine balance between of them is, au fond, of vital importance to obtain the best performance in real applications, such as surveillance, tracking, and many [...] Read more.
Infrared small-object segmentation (ISOS) has a persistent trade-off problem—that is, which came first, recall or precision? Constructing a fine balance between of them is, au fond, of vital importance to obtain the best performance in real applications, such as surveillance, tracking, and many fields related to infrared searching and tracking. F1-score may be a good evaluation metric for this problem. However, since the F1-score only depends upon a specific threshold value, it cannot reflect the user’s requirements according to the various application environment. Therefore, several metrics are commonly used together. Now we introduce F-area, a novel metric for a panoptic evaluation of average precision and F1-score. It can simultaneously consider the performance in terms of real application and the potential capability of a model. Furthermore, we propose a new network, called the Amorphous Variable Inter-located Network (AVILNet), which is of pliable structure based on GridNet, and it is also an ensemble network consisting of the main and its sub-network. Compared with the state-of-the-art ISOS methods, our model achieved an AP of 51.69%, F1-score of 63.03%, and F-area of 32.58% on the International Conference on Computer Vision 2019 ISOS Single dataset by using one generator. In addition, an AP of 53.6%, an F1-score of 60.99%, and F-area of 32.69% by using dual generators, with beating the existing best record (AP, 51.42%; F1-score, 57.04%; and F-area, 29.33%). Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing)
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13 pages, 2698 KiB  
Article
A Novel Deeplabv3+ Network for SAR Imagery Semantic Segmentation Based on the Potential Energy Loss Function of Gibbs Distribution
by Yingying Kong, Yanjuan Liu, Biyuan Yan, Henry Leung and Xiangyang Peng
Remote Sens. 2021, 13(3), 454; https://doi.org/10.3390/rs13030454 - 28 Jan 2021
Cited by 14 | Viewed by 3762
Abstract
Synthetic aperture radar (SAR) provides rich information about the Earth’s surface under all-weather and day-and-night conditions, and is applied in many relevant fields. SAR imagery semantic segmentation, which can be a final product for end users and a fundamental procedure to support other [...] Read more.
Synthetic aperture radar (SAR) provides rich information about the Earth’s surface under all-weather and day-and-night conditions, and is applied in many relevant fields. SAR imagery semantic segmentation, which can be a final product for end users and a fundamental procedure to support other applications, is one of the most difficult challenges. This paper proposes an encoding-decoding network based on Deeplabv3+ to semantically segment SAR imagery. A new potential energy loss function based on the Gibbs distribution is proposed here to establish the semantic dependence among different categories through the relationship among different cliques in the neighborhood system. This paper introduces an improved channel and spatial attention module to the Mobilenetv2 backbone to improve the recognition accuracy of small object categories in SAR imagery. The experimental results show that the proposed method achieves the highest mean intersection over union (mIoU) and global accuracy (GA) with the least running time, which verifies the effectiveness of our method. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing)
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26 pages, 28935 KiB  
Article
Structured Object-Level Relational Reasoning CNN-Based Target Detection Algorithm in a Remote Sensing Image
by Bei Cheng, Zhengzhou Li, Bitong Xu, Xu Yao, Zhiquan Ding and Tianqi Qin
Remote Sens. 2021, 13(2), 281; https://doi.org/10.3390/rs13020281 - 14 Jan 2021
Cited by 21 | Viewed by 2633
Abstract
Deep learning technology has been extensively explored by existing methods to improve the performance of target detection in remote sensing images, due to its powerful feature extraction and representation abilities. However, these methods usually focus on the interior features of the target, but [...] Read more.
Deep learning technology has been extensively explored by existing methods to improve the performance of target detection in remote sensing images, due to its powerful feature extraction and representation abilities. However, these methods usually focus on the interior features of the target, but ignore the exterior semantic information around the target, especially the object-level relationship. Consequently, these methods fail to detect and recognize targets in the complex background where multiple objects crowd together. To handle this problem, a diversified context information fusion framework based on convolutional neural network (DCIFF-CNN) is proposed in this paper, which employs the structured object-level relationship to improve the target detection and recognition in complex backgrounds. The DCIFF-CNN is composed of two successive sub-networks, i.e., a multi-scale local context region proposal network (MLC-RPN) and an object-level relationship context target detection network (ORC-TDN). The MLC-RPN relies on the fine-grained details of objects to generate candidate regions in the remote sensing image. Then, the ORC-TDN utilizes the spatial context information of objects to detect and recognize targets by integrating an attentional message integrated module (AMIM) and an object relational structured graph (ORSG). The AMIM is integrated into the feed-forward CNN to highlight the useful object-level context information, while the ORSG builds the relations between a set of objects by processing their appearance features and geometric features. Finally, the target detection method based on DCIFF-CNN effectively represents the interior and exterior information of the target by exploiting both the multiscale local context information and the object-level relationships. Extensive experiments are conducted, and experimental results demonstrate that the proposed DCIFF-CNN method improves the target detection and recognition accuracy in complex backgrounds, showing superiority to other state-of-the-art methods. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing)
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25 pages, 6240 KiB  
Article
Adaptive Weighting Feature Fusion Approach Based on Generative Adversarial Network for Hyperspectral Image Classification
by Hongbo Liang, Wenxing Bao and Xiangfei Shen
Remote Sens. 2021, 13(2), 198; https://doi.org/10.3390/rs13020198 - 08 Jan 2021
Cited by 16 | Viewed by 2982
Abstract
Recently, generative adversarial network (GAN)-based methods for hyperspectral image (HSI) classification have attracted research attention due to their ability to alleviate the challenges brought by having limited labeled samples. However, several studies have demonstrated that existing GAN-based HSI classification methods are limited in [...] Read more.
Recently, generative adversarial network (GAN)-based methods for hyperspectral image (HSI) classification have attracted research attention due to their ability to alleviate the challenges brought by having limited labeled samples. However, several studies have demonstrated that existing GAN-based HSI classification methods are limited in redundant spectral knowledge and cannot extract discriminative characteristics, thus affecting classification performance. In addition, GAN-based methods always suffer from the model collapse, which seriously hinders their development. In this study, we proposed a semi-supervised adaptive weighting feature fusion generative adversarial network (AWF2-GAN) to alleviate these problems. We introduced unlabeled data to address the issue of having a small number of samples. First, to build valid spectral–spatial feature engineering, the discriminator learns both the dense global spectrum and neighboring separable spatial context via well-designed extractors. Second, a lightweight adaptive feature weighting component is proposed for feature fusion; it considers four predictive fusion options, that is, adding or concatenating feature maps with similar or adaptive weights. Finally, for the mode collapse, the proposed AWF2-GAN combines supervised central loss and unsupervised mean minimization loss for optimization. Quantitative results on two HSI datasets show that our AWF2-GAN achieves superior performance over state-of-the-art GAN-based methods. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing)
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23 pages, 8782 KiB  
Article
HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images
by Zhiyong Xu, Weicun Zhang, Tianxiang Zhang and Jiangyun Li
Remote Sens. 2021, 13(1), 71; https://doi.org/10.3390/rs13010071 - 27 Dec 2020
Cited by 89 | Viewed by 8874
Abstract
Semantic segmentation is a significant method in remote sensing image (RSIs) processing and has been widely used in various applications. Conventional convolutional neural network (CNN)-based semantic segmentation methods are likely to lose the spatial information in the feature extraction stage and usually pay [...] Read more.
Semantic segmentation is a significant method in remote sensing image (RSIs) processing and has been widely used in various applications. Conventional convolutional neural network (CNN)-based semantic segmentation methods are likely to lose the spatial information in the feature extraction stage and usually pay little attention to global context information. Moreover, the imbalance of category scale and uncertain boundary information meanwhile exists in RSIs, which also brings a challenging problem to the semantic segmentation task. To overcome these problems, a high-resolution context extraction network (HRCNet) based on a high-resolution network (HRNet) is proposed in this paper. In this approach, the HRNet structure is adopted to keep the spatial information. Moreover, the light-weight dual attention (LDA) module is designed to obtain global context information in the feature extraction stage and the feature enhancement feature pyramid (FEFP) structure is promoted and employed to fuse the contextual information of different scales. In addition, to achieve the boundary information, we design the boundary aware (BA) module combined with the boundary aware loss (BAloss) function. The experimental results evaluated on Potsdam and Vaihingen datasets show that the proposed approach can significantly improve the boundary and segmentation performance up to 92.0% and 92.3% on overall accuracy scores, respectively. As a consequence, it is envisaged that the proposed HRCNet model will be an advantage in remote sensing images segmentation. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing)
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19 pages, 2301 KiB  
Article
Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks
by Jovan Kalajdjieski, Eftim Zdravevski, Roberto Corizzo, Petre Lameski, Slobodan Kalajdziski, Ivan Miguel Pires, Nuno M. Garcia and Vladimir Trajkovik
Remote Sens. 2020, 12(24), 4142; https://doi.org/10.3390/rs12244142 - 18 Dec 2020
Cited by 58 | Viewed by 6744
Abstract
Air pollution is becoming a rising and serious environmental problem, especially in urban areas affected by an increasing migration rate. The large availability of sensor data enables the adoption of analytical tools to provide decision support capabilities. Employing sensors facilitates air pollution monitoring, [...] Read more.
Air pollution is becoming a rising and serious environmental problem, especially in urban areas affected by an increasing migration rate. The large availability of sensor data enables the adoption of analytical tools to provide decision support capabilities. Employing sensors facilitates air pollution monitoring, but the lack of predictive capability limits such systems’ potential in practical scenarios. On the other hand, forecasting methods offer the opportunity to predict the future pollution in specific areas, potentially suggesting useful preventive measures. To date, many works tackled the problem of air pollution forecasting, most of which are based on sequence models. These models are trained with raw pollution data and are subsequently utilized to make predictions. This paper proposes a novel approach evaluating four different architectures that utilize camera images to estimate the air pollution in those areas. These images are further enhanced with weather data to boost the classification accuracy. The proposed approach exploits generative adversarial networks combined with data augmentation techniques to mitigate the class imbalance problem. The experiments show that the proposed method achieves robust accuracy of up to 0.88, which is comparable to sequence models and conventional models that utilize air pollution data. This is a remarkable result considering that the historic air pollution data is directly related to the output—future air pollution data, whereas the proposed architecture uses camera images to recognize the air pollution—which is an inherently much more difficult problem. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing)
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Review

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22 pages, 5900 KiB  
Review
Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review
by Saman Ghaffarian, João Valente, Mariska van der Voort and Bedir Tekinerdogan
Remote Sens. 2021, 13(15), 2965; https://doi.org/10.3390/rs13152965 - 28 Jul 2021
Cited by 80 | Viewed by 10892
Abstract
Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the [...] Read more.
Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when using the attention mechanism within the DL methods for image classification, image segmentation, change detection, and object detection using remote sensing images. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing)
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