Results 131 to 140 of about 13,575,612 (179)

Hyperspectral Image Classification With Mamba

IEEE Transactions on Geoscience and Remote Sensing
Local and global spectral and spatial information is crucial for hyperspectral image (HSI) classification. However, modeling the global context has been challenging due to the limitations of receptive fields and quadratic complexity.
Zhao-Jie Pan, Chen-Yu Li, A. Plaza, Jocelyn Chanussot, D. Hong   +4 more
semanticscholar   +1 more source

S2DBFT: Spectral–Spatial Dual-Branch Fusion Transformer for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing
Convolutional neural network (CNN)- and Transformer-based models have achieved remarkable success in hyperspectral image (HSI) classification tasks due to their outstanding ability to extract spatial and spectral features.
Yi-Heng Zhang, Ziqiang Wang, Meng Huang, Ming Li, Jian Zhang, Shandong Wang, Jing-Lin Zhang, Heng Zhang   +7 more
semanticscholar   +1 more source

Subpixel Spectral Variability Network for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing
Deep learning-based frameworks have shown great potential in the field of hyperspectral image (HSI) classification owing to their superior modeling capabilities.
Zhu Han, Jin Yang, Lian-Ru Gao, Zhiqiang Zeng, Bing Zhang, Jocelyn Chanussot   +5 more
semanticscholar   +1 more source

Transductive Few-Shot Learning With Enhanced Spectral-Spatial Embedding for Hyperspectral Image Classification

IEEE Transactions on Image Processing
Few-shot learning (FSL) has been rapidly developed in the hyperspectral image (HSI) classification, potentially eliminating time-consuming and costly labeled data acquisition requirements.
Bobo Xi, Yun Zhang, Jiao-Jiao Li, Yan Huang, Yun-Song Li, Zan Li, Jocelyn Chanussot   +6 more
semanticscholar   +1 more source

DBMGNet: A Dual-Branch Mamba-GCN Network for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing
In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) excel at local feature modeling but are limited to Euclidean space. Transformers offer long-range dependence modeling but suffer from high computational complexity.
Hao Wang, Pei-Xian Zhuang, Xiao-Chen Zhang, Jiang-Yun Li   +3 more
semanticscholar   +1 more source

Community Structure Guided Network for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing
Recently, the hypergraph convolutional network (HGCN) has attracted increasing attention in hyperspectral image (HSI) classification. Compared to graph convolutional networks, HGCN has a stronger ability to mine nonlinear high-order correlations. However,
Qing-Wang Wang, Jiangbo Huang, Shunyuan Wang, Zhen Zhang, Tao Shen, Yan-Feng Gu   +5 more
semanticscholar   +1 more source

Mid-Range Convolutional Modulated Transformer Network for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing
Hyperspectral image (HSI) classification plays a major role in remote sensing. The combination of convolutional neural networks (CNNs) and transformers has garnered increasing attention in HSI classification, yielding impressive performance.
Yuyang Wang, Mingzhu Tai, Zigao Liu, Zhen-Qiu Shu, Feng-Chao Xiong, Zheng-Tao Yu   +5 more
semanticscholar   +1 more source

HYPERSPECTRAL IMAGE (HSI)-ASSISTED MULTI-LEVEL CROP CLASSIFICATION WITH SPINALNET-ENABLED FRACTIONAL LIGHT SPECTRUM OPTIMIZER

Biomedical Engineering: Applications, Basis and Communications
The accurate categorization of crop kinds with hyperspectral remote sensing imaging (HRSI) represents an imperative adaption in agriculture and is of huge emergence in estimating the yield of crops and monitoring growth. Amidst the deep models, convolutional neural networks (CNNs) tend to be the best technique for hyperspectral image (HSI ...
Deepak Dharrao, Sarika Deokate, Satpalsing Rajput, Srinivas Ambala   +3 more
openaire   +1 more source

DBMLLA: Double-Branch Mamba-Like Linear Attention Network for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing
Convolutional neural networks (CNNs) and transformers have made remarkable achievements in hyperspectral image classification (HSIC). Unfortunately, CNN-based methods struggle to capture the contextual dependencies between pixels in HSIs, while ...
Lian-Hui Liang, Peiyi Xie, Ying Zhang, Jia-Xin Li, Zhe Zhang, Jun Li, A. Plaza   +6 more
semanticscholar   +1 more source

Disease Detection and Classification Using Deep Learning-Enhanced Hyperspectral Imaging (HSI)

The Hyperspectral Imaging (HSI) has emerged as an effective disease detection and classifier that has a high-resolution spectral and spatial data, which accurately detects small biological changes. Combining with deep learning, HSI systems are automatically able to extract complex spectralxpsatial features of an image, enhancing diagnostic performance ...
Ayesha Farooqi, Nikhil Kumar Goyal
openaire   +1 more source