Results 151 to 160 of about 13,575,612 (179)

Channel-Layer-Oriented Lightweight Spectral–Spatial Network for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing
Hyperspectral image (HSI) classification is commonly influenced by convolution neural networks (CNNs). However, the large number of parameters and computational complexity associated with CNNs can limit their practical application, particularly when ...
Chun-Chao Li, Behnood Rasti, Xuebin Tang, Pu-Hong Duan, Jun Li, Yuan-Xi Peng   +5 more
semanticscholar   +1 more source

GraphGST: Graph Generative Structure-Aware Transformer for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing
Transformer holds significance in deep learning (DL) research. Node embedding (NE) and positional encoding (PE) are usually two indispensable components in a Transformer.
Meng-Ying Jiang, Yuan-Chao Su, Lian-Ru Gao, A. Plaza, Xi-Le Zhao, Xu Sun, Guizhong Liu   +6 more
semanticscholar   +1 more source

How to Learn More? Exploring Kolmogorov-Arnold Networks for Hyperspectral Image Classification

Remote Sensing
Convolutional neural networks (CNNs) and vision transformers (ViTs) have shown excellent capability in complex hyperspectral image (HSI) classification.
Ali Jamali, Swalpa Kumar Roy, D. Hong, Bing Lu, Pedram Ghamisi   +4 more
semanticscholar   +1 more source

HyperMamba: A Spectral-Spatial Adaptive Mamba for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing
Transformers have significantly advanced hyperspectral image (HSI) classification through their proficiency in modeling long sequences. However, the high dimensionality of HSIs poses a particular challenge for Transformers due to their quadratic ...
Qiang Liu, Jun Yue, Yi Fang, Shao-Bo Xia, Le-Yuan Fang   +4 more
semanticscholar   +1 more source

Bridging CNN and Transformer With Cross-Attention Fusion Network for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing
Feature representation is crucial for hyperspectral image (HSI) classification. However, existing convolutional neural network (CNN)-based methods are limited by the convolution kernel and only focus on local features, which causes it to ignore the ...
Fu-Lin Xu, Shao-Hui Mei, Ge Zhang, Nan Wang, Qian Du   +4 more
semanticscholar   +1 more source

Masked Self-Distillation Domain Adaptation for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing
Deep learning-based unsupervised domain adaptation (UDA) has shown potential in cross-scene hyperspectral image (HSI) classification. However, existing methods often experience reduced feature discriminability during domain alignment due to the ...
Zhuoqun Fang, Wenqiang He, Zhao-Kui Li, Qian Du, Qiusheng Chen   +4 more
semanticscholar   +1 more source

Dual Branch Masked Transformer for Hyperspectral Image Classification

IEEE Geoscience and Remote Sensing Letters
Transformer has been widely used in hyperspectral image (HSI) classification tasks because of its ability to capture long-range dependencies. However, most Transformer-based classification methods lack the extraction of local information or do not ...
K. Li, Yu-Shi Chen, Lin Huang
semanticscholar   +1 more source

Cross-Domain Few-Shot Learning Based on Decoupled Knowledge Distillation for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing
Existing cross-domain few-shot learning (FSL) methods for hyperspectral image (HSI) classification have garnered widespread attention due to their excellent performance in recognizing novel classes.
Shou Feng, Hongzhe Zhang, Bobo Xi, Chun-Hui Zhao, Yun-Song Li, Jocelyn Chanussot   +5 more
semanticscholar   +1 more source

SPFormer: Self-Pooling Transformer for Few-Shot Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing
Transformer has shown great potential in extracting global features, and it can achieve better classification performance with a large number of training samples compared with other deep learning (DL) models.
Ziyu Li, Zhao-Hui Xue, Qi Xu, Ling Zhang, Tian-Zhi Zhu, Mengxue Zhang   +5 more
semanticscholar   +1 more source

Spatial-Spectral Transformer for Hyperspectral Image Classification

Remote Sensing, 2021
Xin He, Yushi Chen, Zhouhan Lin
exaly