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Remote Sensing Image Classification and Semantic Segmentation
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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.
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Keywords
- 3D Convolutional Neural Network
- activation function
- active–passive remote sensing
- adapter
- ASPP module
- attention mechanism
- attention module
- canopy height model (CHM)
- channel attention module
- Classification
- closure planning
- cloud shadow segmentation
- CNN
- complex-valued convolutional neural network
- complex-valued cross-entropy
- complex-valued max pooling
- complex-valued nonlinear activation
- context information
- context modeling
- convolution neural network
- convolutional neural network
- convolutional transformer
- cross-domain segmentation
- data augmentation
- data input scheme
- deep generative models
- deep learning
- Deep space exploration
- double-branch
- feature alignment
- feature distortion
- feature fusion
- few-shot learning
- few-shot semantic segmentation
- Field Programmable Gate Array (FPGA)
- fine-grained feature
- frequency domain
- fusion classification
- GF-6
- global context information
- high-resolution remote sensing
- high-resolution remote sensing images
- hybrid structure
- hyperspectral
- hyperspectral image
- hyperspectral unmixing
- image semantic segmentation
- instruction set architecture (ISA)
- label correction
- land classification
- land cover classification
- leaching waste dumps
- LiDAR
- lightweight deep neural networks
- local attention network model
- Mars terrain segmentation
- meta-learning
- mine waste rock
- model compression
- multi-head attention pooling
- multi-head self-attention
- multi-scale feature fusion
- multi-spatial feature encoding
- neural network pruning
- noisy hyperspectral image
- physical stability
- planetary exploration
- planetary rover
- point cloud semantic segmentation
- polarimetric scattering characteristics
- polarimetric synthetic aperture radar (PolSAR)
- polarimetric synthetic aperture radar (PolSAR) image classification
- random forest (RF)
- reflection symmetric decomposition (RSD)
- Remote sensing
- remote sensing image classification
- remote sensing scene classification
- rock segmentation
- sample rebalancing
- SAR and optical images
- Satellite Imagery
- scene segmentation
- sea–land segmentation
- segment anything model (SAM)
- self-attention
- self-distillation contrastive learning
- self-training
- semantic road scene segmentation
- semantic segmentation
- semantic segmentation in foggy scenes
- spacecraft component images
- spectral reconstruction
- spectral–spatial feature extraction
- thema EDItEUR::U Computing and Information Technology
- thema EDItEUR::U Computing and Information Technology::UY Computer science
- transformer
- Tucker tensor decomposition
- UDA
- unsupervised domain adaptation
Links
DOI: 10.3390/books978-3-7258-1366-7Editions