Remote Sensing Image Classification and Semantic Segmentation
With the rapid growth in remote sensing imaging technology, vast amounts of remote sensing data are generated, which is significant for land-monitoring systems, and agriculture, etc., for Earth, Mars, etc. In recent decades, deep learning techniques have had a significant effect on remote sensing da...
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| フォーマット: | Online |
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| 言語: | 英語 |
| 出版事項: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| 主題: | |
| オンライン･アクセス: | ONIX_20240906_9783725813650_116 |
| タグ: |
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| _version_ | 1869529024898793472 |
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| collection | Directory of Open Access Books |
| description | With the rapid growth in remote sensing imaging technology, vast amounts of remote sensing data are generated, which is significant for land-monitoring systems, and agriculture, etc., for Earth, Mars, etc. In recent decades, deep learning techniques have had a significant effect on remote sensing data processing, especially in image classification and semantic segmentation. However, several challenges still exist due to the limited annotations, the complexity of large-scale areas, and other specific problems, which make it more difficult in real-world applications. Therefore, novel deep neural networks combined with meta-learning, attention mechanisms, or other new transformer technologies need to be given more attention in remote sensing. It is also necessary to develop lightweight, explainable, and robust networks. Moreover, this Special Issue aims to develop state-of-the-art deep networks for more accurate remote sensing image classification and semantic segmentation, which also aims to achieve an efficient cross-domain performance through a lightweight network design. |
| format | Online |
| id | doab-20.500.12854ir-143754 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1437542024-09-06T08:18:45Z Remote Sensing Image Classification and Semantic Segmentation Li, Jiaojiao Du, Qian Chanussot, Jocelyn Li, Wei Xi, Bobo Song, Rui Li, Yunsong Mars terrain segmentation semantic segmentation planetary exploration transformer channel attention module hybrid structure 3D convolutional neural network noisy hyperspectral image Tucker tensor decomposition spectral–spatial feature extraction high-resolution remote sensing self-attention context modeling feature alignment remote sensing adapter active–passive remote sensing canopy height model (CHM) classification random forest (RF) spectral reconstruction convolutional transformer hyperspectral unmixing multi-head self-attention hyperspectral image context information convolutional neural network attention module model compression neural network pruning frequency domain lightweight deep neural networks remote sensing image classification deep space exploration planetary rover rock segmentation double-branch sea–land segmentation GF-6 CNN global context information fine-grained feature feature fusion polarimetric synthetic aperture radar (PolSAR) image classification complex-valued convolutional neural network complex-valued max pooling complex-valued nonlinear activation complex-valued cross-entropy meta-learning cross-domain segmentation few-shot semantic segmentation satellite imagery scene segmentation deep generative models mine waste rock leaching waste dumps physical stability closure planning semantic segmentation in foggy scenes unsupervised domain adaptation UDA self-training label correction self-distillation contrastive learning sample rebalancing hyperspectral LiDAR fusion classification remote sensing scene classification few-shot learning data augmentation feature distortion segment anything model (SAM) semantic road scene segmentation image semantic segmentation instruction set architecture (ISA) field programmable gate array (FPGA) spacecraft component images land cover classification SAR and optical images attention mechanism multi-scale feature fusion high-resolution remote sensing images ASPP module local attention network model activation function point cloud semantic segmentation multi-spatial feature encoding multi-head attention pooling cloud shadow segmentation convolution neural network deep learning polarimetric synthetic aperture radar (PolSAR) reflection symmetric decomposition (RSD) data input scheme land classification polarimetric scattering characteristics thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science With the rapid growth in remote sensing imaging technology, vast amounts of remote sensing data are generated, which is significant for land-monitoring systems, and agriculture, etc., for Earth, Mars, etc. In recent decades, deep learning techniques have had a significant effect on remote sensing data processing, especially in image classification and semantic segmentation. However, several challenges still exist due to the limited annotations, the complexity of large-scale areas, and other specific problems, which make it more difficult in real-world applications. Therefore, novel deep neural networks combined with meta-learning, attention mechanisms, or other new transformer technologies need to be given more attention in remote sensing. It is also necessary to develop lightweight, explainable, and robust networks. Moreover, this Special Issue aims to develop state-of-the-art deep networks for more accurate remote sensing image classification and semantic segmentation, which also aims to achieve an efficient cross-domain performance through a lightweight network design. 2024-09-06T08:18:40Z 2024-09-06T08:18:40Z 2024 book ONIX_20240906_9783725813650_116 9783725813650 9783725813667 https://directory.doabooks.org/handle/20.500.12854/143754 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/9476 https://mdpi.com/books/pdfview/book/9476 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-1366-7 10.3390/books978-3-7258-1366-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725813650 9783725813667 open access |
| spellingShingle | Mars terrain segmentation semantic segmentation planetary exploration transformer channel attention module hybrid structure 3D convolutional neural network noisy hyperspectral image Tucker tensor decomposition spectral–spatial feature extraction high-resolution remote sensing self-attention context modeling feature alignment remote sensing adapter active–passive remote sensing canopy height model (CHM) classification random forest (RF) spectral reconstruction convolutional transformer hyperspectral unmixing multi-head self-attention hyperspectral image context information convolutional neural network attention module model compression neural network pruning frequency domain lightweight deep neural networks remote sensing image classification deep space exploration planetary rover rock segmentation double-branch sea–land segmentation GF-6 CNN global context information fine-grained feature feature fusion polarimetric synthetic aperture radar (PolSAR) image classification complex-valued convolutional neural network complex-valued max pooling complex-valued nonlinear activation complex-valued cross-entropy meta-learning cross-domain segmentation few-shot semantic segmentation satellite imagery scene segmentation deep generative models mine waste rock leaching waste dumps physical stability closure planning semantic segmentation in foggy scenes unsupervised domain adaptation UDA self-training label correction self-distillation contrastive learning sample rebalancing hyperspectral LiDAR fusion classification remote sensing scene classification few-shot learning data augmentation feature distortion segment anything model (SAM) semantic road scene segmentation image semantic segmentation instruction set architecture (ISA) field programmable gate array (FPGA) spacecraft component images land cover classification SAR and optical images attention mechanism multi-scale feature fusion high-resolution remote sensing images ASPP module local attention network model activation function point cloud semantic segmentation multi-spatial feature encoding multi-head attention pooling cloud shadow segmentation convolution neural network deep learning polarimetric synthetic aperture radar (PolSAR) reflection symmetric decomposition (RSD) data input scheme land classification polarimetric scattering characteristics thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science Remote Sensing Image Classification and Semantic Segmentation |
| title | Remote Sensing Image Classification and Semantic Segmentation |
| title_full | Remote Sensing Image Classification and Semantic Segmentation |
| title_fullStr | Remote Sensing Image Classification and Semantic Segmentation |
| title_full_unstemmed | Remote Sensing Image Classification and Semantic Segmentation |
| title_short | Remote Sensing Image Classification and Semantic Segmentation |
| title_sort | remote sensing image classification and semantic segmentation |
| topic | Mars terrain segmentation semantic segmentation planetary exploration transformer channel attention module hybrid structure 3D convolutional neural network noisy hyperspectral image Tucker tensor decomposition spectral–spatial feature extraction high-resolution remote sensing self-attention context modeling feature alignment remote sensing adapter active–passive remote sensing canopy height model (CHM) classification random forest (RF) spectral reconstruction convolutional transformer hyperspectral unmixing multi-head self-attention hyperspectral image context information convolutional neural network attention module model compression neural network pruning frequency domain lightweight deep neural networks remote sensing image classification deep space exploration planetary rover rock segmentation double-branch sea–land segmentation GF-6 CNN global context information fine-grained feature feature fusion polarimetric synthetic aperture radar (PolSAR) image classification complex-valued convolutional neural network complex-valued max pooling complex-valued nonlinear activation complex-valued cross-entropy meta-learning cross-domain segmentation few-shot semantic segmentation satellite imagery scene segmentation deep generative models mine waste rock leaching waste dumps physical stability closure planning semantic segmentation in foggy scenes unsupervised domain adaptation UDA self-training label correction self-distillation contrastive learning sample rebalancing hyperspectral LiDAR fusion classification remote sensing scene classification few-shot learning data augmentation feature distortion segment anything model (SAM) semantic road scene segmentation image semantic segmentation instruction set architecture (ISA) field programmable gate array (FPGA) spacecraft component images land cover classification SAR and optical images attention mechanism multi-scale feature fusion high-resolution remote sensing images ASPP module local attention network model activation function point cloud semantic segmentation multi-spatial feature encoding multi-head attention pooling cloud shadow segmentation convolution neural network deep learning polarimetric synthetic aperture radar (PolSAR) reflection symmetric decomposition (RSD) data input scheme land classification polarimetric scattering characteristics thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | Mars terrain segmentation semantic segmentation planetary exploration transformer channel attention module hybrid structure 3D convolutional neural network noisy hyperspectral image Tucker tensor decomposition spectral–spatial feature extraction high-resolution remote sensing self-attention context modeling feature alignment remote sensing adapter active–passive remote sensing canopy height model (CHM) classification random forest (RF) spectral reconstruction convolutional transformer hyperspectral unmixing multi-head self-attention hyperspectral image context information convolutional neural network attention module model compression neural network pruning frequency domain lightweight deep neural networks remote sensing image classification deep space exploration planetary rover rock segmentation double-branch sea–land segmentation GF-6 CNN global context information fine-grained feature feature fusion polarimetric synthetic aperture radar (PolSAR) image classification complex-valued convolutional neural network complex-valued max pooling complex-valued nonlinear activation complex-valued cross-entropy meta-learning cross-domain segmentation few-shot semantic segmentation satellite imagery scene segmentation deep generative models mine waste rock leaching waste dumps physical stability closure planning semantic segmentation in foggy scenes unsupervised domain adaptation UDA self-training label correction self-distillation contrastive learning sample rebalancing hyperspectral LiDAR fusion classification remote sensing scene classification few-shot learning data augmentation feature distortion segment anything model (SAM) semantic road scene segmentation image semantic segmentation instruction set architecture (ISA) field programmable gate array (FPGA) spacecraft component images land cover classification SAR and optical images attention mechanism multi-scale feature fusion high-resolution remote sensing images ASPP module local attention network model activation function point cloud semantic segmentation multi-spatial feature encoding multi-head attention pooling cloud shadow segmentation convolution neural network deep learning polarimetric synthetic aperture radar (PolSAR) reflection symmetric decomposition (RSD) data input scheme land classification polarimetric scattering characteristics thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | ONIX_20240906_9783725813650_116 |