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Remote Sensing
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Intelligent Remote Sensing Data Interpretation
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Intelligent Remote Sensing Data Interpretation

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

Deadline for manuscript submissions: 15 July 2024 | Viewed by 7661

Special Issue Editors

Dr. Yonghao Xu

E-Mail Website
Guest Editor
Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria
Interests: remote sensing image synthesis from text; AI security for earth observation; remote sensing image interpretation; natural hazard monitoring; cross-domain semantic segmentation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pedram Ghamisi

grade E-Mail Website
Guest Editor
1.Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf (HZDR), D-09599 Freiberg, Germany
2. Institute of Advanced Research in Artificial Intelligence (IARAI), 1030 Wien, Austria
Interests: hyperspectral image interpretation; multisensor and multitemporal data fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the successful launch of an increasing number of remote sensing satellites, the amount of Earth observation data is showing an explosive growth trend. Such a massive amount of data also makes it more challenging to achieve fast, accurate, and automated remote sensing data interpretation.

To address this challenge and boost the development of advanced artificial intelligence algorithms for the interpretation of remote sensing data, we would like to invite you to contribute to this Special Issue, which will gather new insights and contributions to the study of Intelligent Remote Sensing Data Interpretation. Original research articles and reviews are welcome. Topics can be related, but are not limited to, the following:

  • Intelligent interpretation algorithms for tasks such as scene classification, object detection, change detection, and semantic segmentation;
  • Earth-observation-oriented machine learning techniques such as weakly supervised learning, zero- and few-shot learning, and domain adaptation and transfer learning;
  • Advanced processing methods for hyperspectral/multispectral/RGB/LiDAR/synthetic aperture radar (SAR) data;
  • Multisensor and multitemporal remote sensing data fusion;
  • Vision and language models for Earth observation;
  • Artificial intelligence techniques for social good;
  • Intelligent natural hazard monitoring.

Dr. Yonghao Xu
Dr. Pedram Ghamisi
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

  • remote sensing data interpretation
  • remote sensing data processing
  • artificial intelligence
  • machine learning
  • deep learning
  • scene classification
  • object detection
  • change detection
  • semantic segmentation
  • natural hazard monitoring

Published Papers (5 papers)

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Research

16 pages, 1748 KiB  
Article
Learning SAR-Optical Cross Modal Features for Land Cover Classification
by Yujun Quan, Rongrong Zhang, Jian Li, Song Ji, Hengliang Guo and Anzhu Yu
Remote Sens. 2024, 16(2), 431; https://doi.org/10.3390/rs16020431 - 22 Jan 2024
Viewed by 764
Abstract
Synthetic aperture radar (SAR) and optical images provide highly complementary ground information. The fusion of SAR and optical data can significantly enhance semantic segmentation inference results. However, the fusion methods for multimodal data remains a challenge for current research due to significant disparities [...] Read more.
Synthetic aperture radar (SAR) and optical images provide highly complementary ground information. The fusion of SAR and optical data can significantly enhance semantic segmentation inference results. However, the fusion methods for multimodal data remains a challenge for current research due to significant disparities in imaging mechanisms from diverse sources. Our goal was to bridge the significant gaps between optical and SAR images by developing a dual-input model that utilizes image-level fusion. To improve most existing state-of-the-art image fusion methods, which often assign equal weights to multiple modalities, we employed the principal component analysis (PCA) transform approach. Subsequently, we performed feature-level fusion on shallow feature maps, which retain rich geometric information. We also incorporated a channel attention module to highlight channels rich in features and suppress irrelevant information. This step is crucial due to the substantial similarity between SAR and optical images in shallow layers such as geometric features. In summary, we propose a generic multimodal fusion strategy that can be attached to most encoding–decoding structures for feature classification tasks, designed with two inputs. One input is the optical image, and the other is the three-band fusion data obtained by combining the PCA component of the optical image with the SAR. Our feature-level fusion method effectively integrates multimodal data. The efficiency of our approach was validated using various public datasets, and the results showed significant improvements when applied to several land cover classification models. Full article
(This article belongs to the Special Issue Intelligent Remote Sensing Data Interpretation)
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23 pages, 6052 KiB  
Article
DPSDA-Net: Dual-Path Convolutional Neural Network with Strip Dilated Attention Module for Road Extraction from High-Resolution Remote Sensing Images
by Like Zhao, Linfeng Ye, Mi Zhang, Huawei Jiang, Zhen Yang and Mingwang Yang
Remote Sens. 2023, 15(15), 3741; https://doi.org/10.3390/rs15153741 - 27 Jul 2023
Viewed by 1059
Abstract
Roads extracted from high-resolution remote sensing images are widely used in many fields, such as autonomous driving, road planning, disaster relief, etc. However, road extraction from high-resolution remote sensing images has certain deficiencies in connectivity and completeness due to obstruction by surrounding ground [...] Read more.
Roads extracted from high-resolution remote sensing images are widely used in many fields, such as autonomous driving, road planning, disaster relief, etc. However, road extraction from high-resolution remote sensing images has certain deficiencies in connectivity and completeness due to obstruction by surrounding ground objects, the influence of similar targets, and the slender structure of roads themselves. To address this issue, we propose a novel dual-path convolutional neural network with a strip dilated attention module, named DPSDA-Net, which adopts a U-shaped encoder–decoder structure, combining the powerful advantages of attention mechanism, dilated convolution, and strip convolution. The encoder utilizes ResNet50 as its basic architecture. A strip position attention mechanism is added between each residual block to strengthen the coherent semantic information of a road. A long-distance shortcut connection operation is introduced to preserve the spatial information characteristics of the original image during the downsampling process. At the same time, a pyramid dilated module with a strip convolution and attention mechanism is constructed between the encoder and decoder to enhance the network feature extraction ability and multi-scale extraction of road feature information, expand the model’s receptive field, and pay more attention to the global spatial semantic and connectivity information. To verify the reliability of the proposed model, road extraction was carried out on the Massachusetts dataset and the LRSNY dataset. The experimental results show that, compared with other typical road extraction methods, the proposed model achieved a higher F1 score and IOU. The DPSDA-Net model can comprehensively characterize the structural features of roads, extract roads more accurately, retain road details, and improve the connectivity and integrity of road extraction in remote sensing images. Full article
(This article belongs to the Special Issue Intelligent Remote Sensing Data Interpretation)
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Graphical abstract

19 pages, 2971 KiB  
Article
LiteST-Net: A Hybrid Model of Lite Swin Transformer and Convolution for Building Extraction from Remote Sensing Image
by Wei Yuan, Xiaobo Zhang, Jibao Shi and Jin Wang
Remote Sens. 2023, 15(8), 1996; https://doi.org/10.3390/rs15081996 - 10 Apr 2023
Cited by 5 | Viewed by 1842
Abstract
Extracting building data from remote sensing images is an efficient way to obtain geographic information data, especially following the emergence of deep learning technology, which results in the automatic extraction of building data from remote sensing images becoming increasingly accurate. A CNN (convolution [...] Read more.
Extracting building data from remote sensing images is an efficient way to obtain geographic information data, especially following the emergence of deep learning technology, which results in the automatic extraction of building data from remote sensing images becoming increasingly accurate. A CNN (convolution neural network) is a successful structure after a fully connected network. It has the characteristics of saving computation and translation invariance with improved local features, but it has difficulty obtaining global features. Transformers can compensate for the shortcomings of CNNs and more effectively obtain global features. However, the calculation number of transformers is excessive. To solve this problem, a Lite Swin transformer is proposed. The three matrices Q, K, and V of the transformer are simplified to only a V matrix, and the v of the pixel is then replaced by the v with the largest projection value on the pixel feature vector. In order to better integrate global features and local features, we propose the LiteST-Net model, in which the features extracted by the Lite Swin transformer and the CNN are added together and then sampled up step by step to fully utilize the global feature acquisition ability of the transformer and the local feature acquisition ability of the CNN. The comparison experiments on two open datasets are carried out using our proposed LiteST-Net and some classical image segmentation models. The results show that compared with other networks, all metrics of LiteST-Net are the best, and the predicted image is closer to the label. Full article
(This article belongs to the Special Issue Intelligent Remote Sensing Data Interpretation)
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Graphical abstract

24 pages, 43487 KiB  
Article
Interpretation of Latent Codes in InfoGAN with SAR Images
by Zhenpeng Feng, Miloš Daković, Hongbing Ji, Xianda Zhou, Mingzhe Zhu, Xiyang Cui and Ljubiša Stanković
Remote Sens. 2023, 15(5), 1254; https://doi.org/10.3390/rs15051254 - 24 Feb 2023
Viewed by 1638
Abstract
Generative adversarial networks (GANs) can synthesize abundant photo-realistic synthetic aperture radar (SAR) images. Some modified GANs (e.g., InfoGAN) are even able to edit specific properties of the synthesized images by introducing latent codes. It is crucial for SAR image synthesis since the targets [...] Read more.
Generative adversarial networks (GANs) can synthesize abundant photo-realistic synthetic aperture radar (SAR) images. Some modified GANs (e.g., InfoGAN) are even able to edit specific properties of the synthesized images by introducing latent codes. It is crucial for SAR image synthesis since the targets in real SAR images have different properties due to the imaging mechanism. Despite the success of the InfoGAN in manipulating properties, there still lacks a clear explanation of how these latent codes affect synthesized properties; thus, editing specific properties usually relies on empirical trials, which are unreliable and time-consuming. In this paper, we show that latent codes are almost disentangled to affect the properties of SAR images in a nonlinear manner. By introducing some property estimators for latent codes, we are able to decompose the complex causality between latent codes and different properties. Both qualitative and quantitative experimental results demonstrate that the property value can be computed by the property estimators; inversely, the required latent codes can be computed given the desired properties. Unlike the original InfoGAN, which only provides the visual trend between properties and latent codes, the properties of SAR images can be manipulated numerically by latent codes as users expect. Full article
(This article belongs to the Special Issue Intelligent Remote Sensing Data Interpretation)
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15 pages, 2246 KiB  
Article
Adversarial Remote Sensing Scene Classification Based on Lie Group Feature Learning
by Chengjun Xu, Jingqian Shu and Guobin Zhu
Remote Sens. 2023, 15(4), 914; https://doi.org/10.3390/rs15040914 - 07 Feb 2023
Cited by 3 | Viewed by 1326
Abstract
Convolutional Neural Networks have been widely used in remote sensing scene classification. Since this kind of model needs a large number of training samples containing data category information, a Generative Adversarial Network (GAN) is usually used to address the problem of lack of [...] Read more.
Convolutional Neural Networks have been widely used in remote sensing scene classification. Since this kind of model needs a large number of training samples containing data category information, a Generative Adversarial Network (GAN) is usually used to address the problem of lack of samples. However, GAN mainly generates scene data samples that do not contain category information. To address this problem, a novel supervised adversarial Lie Group feature learning network is proposed. In the case of limited data samples, the model can effectively generate data samples with category information. There are two main differences between our method and the traditional GAN. First, our model takes category information and data samples as the input of the model and optimizes the constraint of category information in the loss function, so that data samples containing category information can be generated. Secondly, the object scale sample generation strategy is introduced, which can generate data samples of different scales and ensure that the generated data samples contain richer feature information. After large-scale experiments on two publicly available and challenging datasets, it is found that our method can achieve better scene classification accuracy even with limited data samples. Full article
(This article belongs to the Special Issue Intelligent Remote Sensing Data Interpretation)
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