Research

16 pages, 2220 KiB

Open AccessArticle

DMCH: A Deep Metric and Category-Level Semantic Hashing Network for Retrieval in Remote Sensing

by Haiyan Huang, Qimin Cheng, Zhenfeng Shao, Xiao Huang and Liyuan Shao

Remote Sens. 2024, 16(1), 90; https://doi.org/10.3390/rs16010090 - 25 Dec 2023

Viewed by 720

The effectiveness of hashing methods in big data retrieval has been proved due to their merit in computational and storage efficiency. Recently, encouraged by the strong discriminant capability of deep learning in image representation, various deep hashing methodologies have emerged to enhance retrieval [...] Read more.

The effectiveness of hashing methods in big data retrieval has been proved due to their merit in computational and storage efficiency. Recently, encouraged by the strong discriminant capability of deep learning in image representation, various deep hashing methodologies have emerged to enhance retrieval performance. However, maintaining the semantic richness inherent in remote sensing images (RSIs), characterized by their scene intricacy and category diversity, remains a significant challenge. In response to this challenge, we propose a novel two-stage deep metric and category-level semantic hashing network termed DMCH. First, it introduces a novel triple-selection strategy during the semantic metric learning process to optimize the utilization of triple-label information. Moreover, it inserts a hidden layer to enhance the latent correlation between similar hash codes via a designed category-level classification loss. In addition, it employs additional constraints to keep bit-uncorrelation and bit-balance of generated hash codes. Furthermore, a progressive coarse-to-fine hash code sorting scheme is used for superior fine-grained retrieval and more effective hash function learning. Experiment results on three datasets illustrate the effectiveness and superiority of the proposed method. Full article

(This article belongs to the Special Issue Recent Advances in High Resolution Remote Sensing Image Processing and Analysis: Methodology and Application)

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18 pages, 2846 KiB

Open AccessArticle

Enhancing Object Detection in Remote Sensing: A Hybrid YOLOv7 and Transformer Approach with Automatic Model Selection

by Mahmoud Ahmed, Naser El-Sheimy, Henry Leung and Adel Moussa

Remote Sens. 2024, 16(1), 51; https://doi.org/10.3390/rs16010051 - 21 Dec 2023

Viewed by 1252

Abstract

In the remote sensing field, object detection holds immense value for applications such as land use classification, disaster monitoring, and infrastructure planning, where accurate and efficient identification of objects within images is essential for informed decision making. However, achieving object localization with high [...] Read more.

In the remote sensing field, object detection holds immense value for applications such as land use classification, disaster monitoring, and infrastructure planning, where accurate and efficient identification of objects within images is essential for informed decision making. However, achieving object localization with high precision can be challenging even if minor errors exist at the pixel level, which can significantly impact the ground distance measurements. To address this critical challenge, our research introduces an innovative hybrid approach that combines the capabilities of the You Only Look Once version 7 (YOLOv7) and DEtection TRansformer (DETR) algorithms. By bridging the gap between local receptive field and global context, our approach not only enhances overall object detection accuracy, but also promotes precise object localization, a key requirement in the field of remote sensing. Furthermore, a key advantage of our approach is the introduction of an automatic selection module which serves as an intelligent decision-making component. This module optimizes the selection process between YOLOv7 and DETR, and further improves object detection accuracy. Finally, we validate the improved performance of our new hybrid approach through empirical experimentation, and thus confirm its contribution to the field of target recognition and detection in remote sensing images. Full article

(This article belongs to the Special Issue Recent Advances in High Resolution Remote Sensing Image Processing and Analysis: Methodology and Application)

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17 pages, 7735 KiB

Open AccessArticle

Efficient Wheat Lodging Detection Using UAV Remote Sensing Images and an Innovative Multi-Branch Classification Framework

by Kai Zhang, Rundong Zhang, Ziqian Yang, Jie Deng, Ahsan Abdullah, Congying Zhou, Xuan Lv, Rui Wang and Zhanhong Ma

Remote Sens. 2023, 15(18), 4572; https://doi.org/10.3390/rs15184572 - 17 Sep 2023

Viewed by 923

Abstract

Wheat lodging has a significant impact on yields and quality, necessitating the accurate acquisition of lodging information for effective disaster assessment and damage evaluation. This study presents a novel approach for wheat lodging detection in large and heterogeneous fields using UAV remote sensing [...] Read more.

Wheat lodging has a significant impact on yields and quality, necessitating the accurate acquisition of lodging information for effective disaster assessment and damage evaluation. This study presents a novel approach for wheat lodging detection in large and heterogeneous fields using UAV remote sensing images. A comprehensive dataset spanning an area of 2.3117 km² was meticulously collected and labeled, constituting a valuable resource for this study. Through a comprehensive comparison of algorithmic models, remote sensing data types, and model frameworks, this study demonstrates that the Deeplabv3+ model outperforms various other models, including U-net, Bisenetv2, FastSCN, RTFormer, Bisenetv2, and HRNet, achieving a noteworthy F1 score of 90.22% for detecting wheat lodging. Intriguingly, by leveraging RGB image data alone, the current model achieves high-accuracy rates in wheat lodging detection compared to models trained with multispectral datasets at the same resolution. Moreover, we introduce an innovative multi-branch binary classification framework that surpasses the traditional single-branch multi-classification framework. The proposed framework yielded an outstanding F1 score of 90.30% for detecting wheat lodging and an accuracy of 86.94% for area extraction of wheat lodging, surpassing the single-branch multi-classification framework by an improvement of 7.22%. Significantly, the present comprehensive experimental results showcase the capacity of UAVs and deep learning to detect wheat lodging in expansive areas, demonstrating high efficiency and cost-effectiveness under heterogeneous field conditions. This study offers valuable insights for leveraging UAV remote sensing technology to identify post-disaster damage areas and assess the extent of the damage. Full article

(This article belongs to the Special Issue Recent Advances in High Resolution Remote Sensing Image Processing and Analysis: Methodology and Application)

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21 pages, 5071 KiB

Open AccessArticle

C2S-RoadNet: Road Extraction Model with Depth-Wise Separable Convolution and Self-Attention

by Anchao Yin, Chao Ren, Zhiheng Yan, Xiaoqin Xue, Ying Zhou, Yuanyuan Liu, Jiakai Lu and Cong Ding

Remote Sens. 2023, 15(18), 4531; https://doi.org/10.3390/rs15184531 - 14 Sep 2023

Cited by 1 | Viewed by 850

Abstract

In order to effectively utilize acquired remote sensing imagery and improve the completeness of information extraction, we propose a new road extraction model called C2S-RoadNet. C2S-RoadNet was designed to enhance the feature extraction capability by combining depth-wise separable convolution with lightweight asymmetric self-attention [...] Read more.

In order to effectively utilize acquired remote sensing imagery and improve the completeness of information extraction, we propose a new road extraction model called C2S-RoadNet. C2S-RoadNet was designed to enhance the feature extraction capability by combining depth-wise separable convolution with lightweight asymmetric self-attention based on encoder and decoder structures. C2S-RoadNet is able to establish long-distance dependencies and fully utilize global information, and it better extracts road information. Based on the lightweight asymmetric self-attention network, a multi-scale adaptive weight module was designed to aggregate information at different scales. The use of adaptive weights can fully harness features at different scales to improve the model’s extraction performance. The strengthening of backbone information plays an important role in the extraction of road main branch information, which can effectively improve the integrity of road information. Compared with existing deep learning algorithms based on encoder–decoder, experimental results on various public road datasets show that the C2S-RoadNet model can produce more complete road extraction, especially when faced with scenarios involving occluded roads or complex lighting conditions. On the Massachusetts road dataset, the PA, F1 score, and IoU reached 98%, 77%, and 72%, respectively. Furthermore, on the DeepGlobe dataset, the PA, F1 score, and IoU reached 98%, 78%, and 64%, respectively. The objective performance evaluation indicators also significantly improved on the LSRV dataset, and the PA, F1 score, and IoU reached 96%, 82%, and 71%, respectively. Full article

(This article belongs to the Special Issue Recent Advances in High Resolution Remote Sensing Image Processing and Analysis: Methodology and Application)

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22 pages, 5055 KiB

Open AccessArticle

Road Extraction from High-Resolution Remote Sensing Images via Local and Global Context Reasoning

by Jie Chen, Libo Yang, Hao Wang, Jingru Zhu, Geng Sun, Xiaojun Dai, Min Deng and Yan Shi

Remote Sens. 2023, 15(17), 4177; https://doi.org/10.3390/rs15174177 - 25 Aug 2023

Cited by 1 | Viewed by 1375

Abstract

Road extraction from high-resolution remote sensing images is a critical task in image understanding and analysis, yet it poses significant challenges because of road occlusions caused by vegetation, buildings, and shadows. Deep convolutional neural networks have emerged as the leading approach for road [...] Read more.

Road extraction from high-resolution remote sensing images is a critical task in image understanding and analysis, yet it poses significant challenges because of road occlusions caused by vegetation, buildings, and shadows. Deep convolutional neural networks have emerged as the leading approach for road extraction because of their exceptional feature representation capabilities. However, existing methods often yield incomplete and disjointed road extraction results. To address this issue, we propose CR-HR-RoadNet, a novel high-resolution road extraction network that incorporates local and global context reasoning. In this work, we introduce a road-adapted high-resolution network as the feature encoder, effectively preserving intricate details of narrow roads and spatial information. To capture multi-scale local context information and model the interplay between roads and background environments, we integrate multi-scale features with residual learning in a specialized multi-scale feature representation module. Moreover, to enable efficient long-range dependencies between different dimensions and reason the correlation between various road segments, we employ a lightweight coordinate attention module as a global context-aware algorithm. Extensive quantitative and qualitative experiments on three datasets demonstrate that CR-HR-RoadNet achieves superior extraction accuracy across various road datasets, delivering road extraction results with enhanced completeness and continuity. The proposed method holds promise for advancing road extraction in challenging remote sensing scenarios and contributes to the broader field of deep-learning-based image analysis for geospatial applications. Full article

(This article belongs to the Special Issue Recent Advances in High Resolution Remote Sensing Image Processing and Analysis: Methodology and Application)

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19 pages, 5322 KiB

Open AccessArticle

Learning Adversarially Robust Object Detector with Consistency Regularization in Remote Sensing Images

by Yang Li, Yuqiang Fang, Wanyun Li, Bitao Jiang, Shengjin Wang and Zhi Li

Remote Sens. 2023, 15(16), 3997; https://doi.org/10.3390/rs15163997 - 11 Aug 2023

Cited by 1 | Viewed by 1125

Abstract

Object detection in remote sensing has developed rapidly and has been applied in many fields, but it is known to be vulnerable to adversarial attacks. Improving the robustness of models has become a key issue for reliable application deployment. This paper proposes a [...] Read more.

Object detection in remote sensing has developed rapidly and has been applied in many fields, but it is known to be vulnerable to adversarial attacks. Improving the robustness of models has become a key issue for reliable application deployment. This paper proposes a robust object detector for remote sensing images (RSIs) to mitigate the performance degradation caused by adversarial attacks. For remote sensing objects, multi-dimensional convolution is utilized to extract both specific features and consistency features from clean images and adversarial images dynamically and efficiently. This enhances the feature extraction ability and thus enriches the context information used for detection. Furthermore, regularization loss is proposed from the perspective of image distribution. This can separate consistent features from the mixed distributions for reconstruction to assure detection accuracy. Experimental results obtained using different datasets (HRSC, UCAS-AOD, and DIOR) demonstrate that the proposed method effectively improves the robustness of detectors against adversarial attacks. Full article

(This article belongs to the Special Issue Recent Advances in High Resolution Remote Sensing Image Processing and Analysis: Methodology and Application)

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24 pages, 20605 KiB

Open AccessArticle

Towards Feature Decoupling for Lightweight Oriented Object Detection in Remote Sensing Images

by Chenwei Deng, Donglin Jing, Yuqi Han, Zhiyuan Deng and Hong Zhang

Remote Sens. 2023, 15(15), 3801; https://doi.org/10.3390/rs15153801 - 30 Jul 2023

Cited by 2 | Viewed by 1322

Abstract

Recently, the improvement of detection performance always relies on deeper convolutional layers and complex convolutional structures in remote sensing images, which significantly increases the storage space and computational complexity of the detector. Although previous work has designed various novel lightweight convolutions, when these [...] Read more.

Recently, the improvement of detection performance always relies on deeper convolutional layers and complex convolutional structures in remote sensing images, which significantly increases the storage space and computational complexity of the detector. Although previous work has designed various novel lightweight convolutions, when these convolutional structures are applied to remote sensing detection tasks, the inconsistency between features and targets as well as between features and tasks in the detection architecture is often ignored: (1) The features extracted by convolution sliding in a fixed direction make it difficult to effectively model targets with arbitrary direction distribution, which leads to the detector needing more parameters to encode direction information and the network parameters being highly redundant; (2) The detector shares features from the backbone, but the classification task requires rotation-invariant features while the regression task requires rotation-sensitive features. This inconsistency in the task can lead to inefficient convolutional structures. Therefore, this paper proposed a detector that uses the Feature Decoupling for Lightweight Oriented Object Detection (FDLO-Det). Specifically, we constructed a rotational separable convolution that extracts rotational equivariant features while significantly compressing network parameters and computational complexity through highly shared parameters. Next, we introduced an orthogonal polarization transformation module that decomposes rotational equivariant features in both horizontal and vertical orthogonal directions, and used polarization functions to filter out the required features for classification and regression tasks, effectively improving detector performance. Extensive experiments on DOTA, HRSC2016, and UCAS-AOD show that the proposed detector can achieve the best performance and achieve an effective balance between computational complexity and detection accuracy. Full article

(This article belongs to the Special Issue Recent Advances in High Resolution Remote Sensing Image Processing and Analysis: Methodology and Application)

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30 pages, 7722 KiB

Open AccessArticle

MS-AGAN: Road Extraction via Multi-Scale Information Fusion and Asymmetric Generative Adversarial Networks from High-Resolution Remote Sensing Images under Complex Backgrounds

by Shaofu Lin, Xin Yao, Xiliang Liu, Shaohua Wang, Hua-Min Chen, Lei Ding, Jing Zhang, Guihong Chen and Qiang Mei

Remote Sens. 2023, 15(13), 3367; https://doi.org/10.3390/rs15133367 - 30 Jun 2023

Cited by 1 | Viewed by 1740

Abstract

Extracting roads from remote sensing images is of significant importance for automatic road network updating, urban planning, and construction. However, various factors in complex scenes (e.g., high vegetation coverage occlusions) may lead to fragmentation in the extracted road networks and also affect the [...] Read more.

Extracting roads from remote sensing images is of significant importance for automatic road network updating, urban planning, and construction. However, various factors in complex scenes (e.g., high vegetation coverage occlusions) may lead to fragmentation in the extracted road networks and also affect the robustness of road extraction methods. This study proposes a multi-scale road extraction method with asymmetric generative adversarial learning (MS-AGAN). First, we design an asymmetric GAN with a multi-scale feature encoder to better utilize the context information in high-resolution remote sensing images (HRSIs). Atrous spatial pyramid pooling (ASPP) and feature fusion are integrated into the asymmetric encoder–decoder structure to avoid feature redundancy caused by multi-level cascading operations and enhance the generator network’s ability to extract fine-grained road information at the pixel level. Second, to maintain road connectivity, topologic features are considered in the pixel segmentation process. A linear structural similarity loss (

L_{S S I M}

) is introduced into the loss function of MS-AGAN, which guides MS-AGAN to generate more accurate segmentation results. Finally, to fairly evaluate the performance of deep models under complex backgrounds, the Bayesian error rate (BER) is introduced into the field of road extraction for the first time. Experiments are conducted via Gaofen-2 (GF-2) high-resolution remote sensing images with high vegetation coverage in the Daxing District of Beijing, China, and the public DeepGlobe dataset. The performance of MS-AGAN is compared with a list of advanced models, including RCFSNet, CoANet, UNet, DeepLabV3+, and DiResNet. The final results show that (1) with respect to road extraction performance, the Recall, F1, and IoU values of MS-AGAN on the Daxing dataset are 2.17%, 0.04%, and 2.63% higher than the baselines. On DeepGlobe, the Recall, F1, and IoU of MS-AGAN improve by 1.12%, 0.42%, and 0.25%, respectively. (2) On road connectivity, the Conn index of MS-AGAN from the Daxing dataset is 46.39%, with an improvement of 0.62% over the baselines, and the Conn index of MS-AGAN on DeepGlobe is 70.08%, holding an improvement of 1.73% over CoANet. The quantitative and qualitative analyses both demonstrate the superiority of MS-AGAN in preserving road connectivity. (3) In particular, the BER of MS-AGAN is 20.86% over the Daxing dataset with a 0.22% decrease compared to the best baselines and 11.77% on DeepGlobe with a 0.85% decrease compared to the best baselines. The proposed MS-AGAN provides an efficient, cost-effective, and reliable method for the dynamic updating of road networks via HRSIs. Full article

(This article belongs to the Special Issue Recent Advances in High Resolution Remote Sensing Image Processing and Analysis: Methodology and Application)

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18 pages, 2976 KiB

Open AccessArticle

Modified Dynamic Routing Convolutional Neural Network for Pan-Sharpening

by Kai Sun, Jiangshe Zhang, Junmin Liu, Shuang Xu, Xiangyong Cao and Rongrong Fei

Remote Sens. 2023, 15(11), 2869; https://doi.org/10.3390/rs15112869 - 31 May 2023

Cited by 1 | Viewed by 1074

Abstract

Based on deep learning, various pan-sharpening models have achieved excellent results. However, most of them adopt simple addition or concatenation operations to merge the information of low spatial resolution multi-spectral (LRMS) images and panchromatic (PAN) images, which may cause a loss of detailed [...] Read more.

Based on deep learning, various pan-sharpening models have achieved excellent results. However, most of them adopt simple addition or concatenation operations to merge the information of low spatial resolution multi-spectral (LRMS) images and panchromatic (PAN) images, which may cause a loss of detailed information. To tackle this issue, inspired by capsule networks, we propose a plug-and-play layer named modified dynamic routing layer (MDRL), which modifies the information transmission mode of capsules to effectively fuse LRMS images and PAN images. Concretely, the lower-level capsules are generated by applying transform operation to the features of LRMS images and PAN images, which preserve the spatial location information. Then, the dynamic routing algorithm is modified to adaptively select the lower-level capsules to generate the higher-level capsule features to represent the fusion of LRMS images and PAN images, which can effectively avoid the loss of detailed information. In addition, the previous addition and concatenation operations are illustrated as special cases of our MDRL. Based on MIPSM with addition operations and DRPNN with concatenation operations, two modified dynamic routing models named MDR–MIPSM and MDR–DRPNN are further proposed for pan-sharpening. Extensive experimental results demonstrate that the proposed method can achieve remarkable spectral and spatial quality. Full article

(This article belongs to the Special Issue Recent Advances in High Resolution Remote Sensing Image Processing and Analysis: Methodology and Application)

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23 pages, 27433 KiB

Open AccessArticle

Land Subsidence in a Coastal City Based on SBAS-InSAR Monitoring: A Case Study of Zhuhai, China

by Huimin Sun, Hongxia Peng, Min Zeng, Simiao Wang, Yujie Pan, Pengcheng Pi, Zixuan Xue, Xinwen Zhao, Ao Zhang and Fengmei Liu

Remote Sens. 2023, 15(9), 2424; https://doi.org/10.3390/rs15092424 - 05 May 2023

Cited by 5 | Viewed by 2034

Abstract

The superimposed effects of sea level rise caused by global warming and land subsidence seriously threaten the sustainable development of coastal cities. In recent years, an important coastal city in China, Zhuhai, has been suffering from severe and widespread land subsidence; however, the [...] Read more.

The superimposed effects of sea level rise caused by global warming and land subsidence seriously threaten the sustainable development of coastal cities. In recent years, an important coastal city in China, Zhuhai, has been suffering from severe and widespread land subsidence; however, the characteristics, triggers, and vulnerability assessment of ground subsidence in Zhuhai are still unclear. Therefore, we used the SBAS-InSAR technique to process 51 Sentinel-1A images to monitor the land subsidence in Zhuhai during the period from August 2016 to June 2019. The results showed that there was extensive land subsidence in the study area, with a maximum rate of −109.75 mm/yr. The surface had sequentially undergone a process of minor uplift and decline fluctuation, sharp settlement, and stable subsidence. The distribution and evolution of land subsidence were controlled by tectonic fractures and triggered by the thickness of soft soil, the intensity of groundwater development, and the seasonal changes of atmospheric precipitation. The comprehensive index method and the analytic hierarchy process were applied to derive extremely high subsidence vulnerability in several village communities and some traffic arteries in Zhuhai. Our research provides a theoretical basis for urban disaster prevention in Zhuhai and the construction planning of coastal cities around the world. Full article

(This article belongs to the Special Issue Recent Advances in High Resolution Remote Sensing Image Processing and Analysis: Methodology and Application)

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26 pages, 2303 KiB

Open AccessArticle

A CNN-Transformer Network Combining CBAM for Change Detection in High-Resolution Remote Sensing Images

by Mengmeng Yin, Zhibo Chen and Chengjian Zhang

Remote Sens. 2023, 15(9), 2406; https://doi.org/10.3390/rs15092406 - 04 May 2023

Cited by 7 | Viewed by 2803

Abstract

Current deep learning-based change detection approaches mostly produce convincing results by introducing attention mechanisms to traditional convolutional networks. However, given the limitation of the receptive field, convolution-based methods fall short of fully modelling global context and capturing long-range dependencies, thus insufficient in discriminating [...] Read more.

Current deep learning-based change detection approaches mostly produce convincing results by introducing attention mechanisms to traditional convolutional networks. However, given the limitation of the receptive field, convolution-based methods fall short of fully modelling global context and capturing long-range dependencies, thus insufficient in discriminating pseudo changes. Transformers have an efficient global spatio-temporal modelling capability, which is beneficial for the feature representation of changes of interest. However, the lack of detailed information may cause the transformer to locate the boundaries of changed regions inaccurately. Therefore, in this article, a hybrid CNN-transformer architecture named CTCANet, combining the strengths of convolutional networks, transformer, and attention mechanisms, is proposed for high-resolution bi-temporal remote sensing image change detection. To obtain high-level feature representations that reveal changes of interest, CTCANet utilizes tokenizer to embed the features of each image extracted by convolutional network into a sequence of tokens, and the transformer module to model global spatio-temporal context in token space. The optimal bi-temporal information fusion approach is explored here. Subsequently, the reconstructed features carrying deep abstract information are fed to the cascaded decoder to aggregate with features containing shallow fine-grained information, through skip connections. Such an aggregation empowers our model to maintain the completeness of changes and accurately locate small targets. Moreover, the integration of the convolutional block attention module enables the smoothing of semantic gaps between heterogeneous features and the accentuation of relevant changes in both the channel and spatial domains, resulting in more impressive outcomes. The performance of the proposed CTCANet surpasses that of recent certain state-of-the-art methods, as evidenced by experimental results on two publicly accessible datasets, LEVIR-CD and SYSU-CD. Full article

(This article belongs to the Special Issue Recent Advances in High Resolution Remote Sensing Image Processing and Analysis: Methodology and Application)

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21 pages, 4310 KiB

Open AccessArticle

Smooth GIoU Loss for Oriented Object Detection in Remote Sensing Images

by Xiaoliang Qian, Niannian Zhang and Wei Wang

Remote Sens. 2023, 15(5), 1259; https://doi.org/10.3390/rs15051259 - 24 Feb 2023

Cited by 12 | Viewed by 2010

Abstract

Oriented object detection (OOD) can more accurately locate objects with an arbitrary direction in remote sensing images (RSIs) compared to horizontal object detection. The most commonly used bounding box regression (BBR) loss in OOD is smooth L1 loss, which requires the precondition that [...] Read more.

Oriented object detection (OOD) can more accurately locate objects with an arbitrary direction in remote sensing images (RSIs) compared to horizontal object detection. The most commonly used bounding box regression (BBR) loss in OOD is smooth L1 loss, which requires the precondition that spatial parameters are independent of one another. This independence is an ideal that is not achievable in practice. To avoid this problem, various kinds of IoU-based BBR losses have been widely used in OOD; however, their relationships with IoUs are approximately linear. Consequently, the gradient value, i.e., the learning intensity, cannot be dynamically adjusted with the IoU in these cases, which restricts the accuracy of object location. To handle this problem, a novel BBR loss, named smooth generalized intersection over union (GIoU) loss, is proposed. The contributions it makes include two aspects. First of all, smooth GIoU loss can employ more appropriate learning intensities in the different ranges of GIoU values to address the above problem and the design scheme of smooth GIoU loss can be generalized to other IoU-based BBR losses. Secondly, the existing computational scheme of GIoU loss can be modified to fit OOD. The ablation study of smooth GIoU loss validates the effectiveness of its design scheme. Comprehensive comparisons performed on two RSI datasets demonstrate that the proposed smooth GIoU loss is superior to other BBR losses adopted by existing OOD methods and can be generalized for various kinds of OOD methods. Furthermore, the core idea of smooth GIoU loss can be generalized to other IoU-based BBR losses. Full article

(This article belongs to the Special Issue Recent Advances in High Resolution Remote Sensing Image Processing and Analysis: Methodology and Application)

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25 pages, 11314 KiB

Open AccessArticle

Application of a Novel Multiscale Global Graph Convolutional Neural Network to Improve the Accuracy of Forest Type Classification Using Aerial Photographs

by Huiqing Pei, Toshiaki Owari, Satoshi Tsuyuki and Yunfang Zhong

Remote Sens. 2023, 15(4), 1001; https://doi.org/10.3390/rs15041001 - 11 Feb 2023

Cited by 10 | Viewed by 2320

Abstract

The accurate classification of forest types is critical for sustainable forest management. In this study, a novel multiscale global graph convolutional neural network (MSG-GCN) was compared with random forest (RF), U-Net, and U-Net++ models in terms of the classification of natural mixed forest [...] Read more.

The accurate classification of forest types is critical for sustainable forest management. In this study, a novel multiscale global graph convolutional neural network (MSG-GCN) was compared with random forest (RF), U-Net, and U-Net++ models in terms of the classification of natural mixed forest (NMX), natural broadleaved forest (NBL), and conifer plantation (CP) using very high-resolution aerial photographs from the University of Tokyo Chiba Forest in central Japan. Our MSG-GCN architecture is novel in the following respects: The convolutional kernel scale of the encoder is unlike those of other models; local attention replaces the conventional U-Net++ skip connection; a multiscale graph convolutional neural block is embedded into the end layer of the encoder module; and various decoding layers are spliced to preserve high- and low-level feature information and to improve the decision capacity for boundary cells. The MSG-GCN achieved higher classification accuracy than other state-of-the-art (SOTA) methods. The classification accuracy in terms of NMX was lower compared with NBL and CP. The RF method produced severe salt-and-pepper noise. The U-Net and U-Net++ methods frequently produced error patches and the edges between different forest types were rough and blurred. In contrast, the MSG-GCN method had fewer misclassification patches and showed clear edges between different forest types. Most areas misclassified by MSG-GCN were on edges, while misclassification patches were randomly distributed in internal areas for U-Net and U-Net++. We made full use of artificial intelligence and very high-resolution remote sensing data to create accurate maps to aid forest management and facilitate efficient and accurate forest resource inventory taking in Japan. Full article

(This article belongs to the Special Issue Recent Advances in High Resolution Remote Sensing Image Processing and Analysis: Methodology and Application)

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