Results 121 to 130 of about 13,575,612 (179)

ReSC-Net: Hyperspectral Image Classification Based on Attention-Enhanced Residual Module and Spatial-Channel Attention

IEEE Transactions on Geoscience and Remote Sensing
Hyperspectral image (HSI) classification is a key technique in remote sensing. Despite the increasing availability of high-quality HSI data, obtaining a large number of labeled samples remains challenging in certain cases.
Liangpei Zhang, Bo Du, Chuan Fu
exaly   +2 more sources

HSI-Mixer: Hyperspectral Image Classification Using the Spectral–Spatial Mixer Representation From Convolutions

IEEE Geoscience and Remote Sensing Letters, 2022
Hongbo Liang, Wenxing Bao, Xiangfei Shen, Xiaowu Zhang   +3 more
openaire   +3 more sources

HSI-GCN: hyperspectral image classification algorithm based on Gabor convolutional networks

World Journal of Engineering, 2021
Purpose Hyperspectral imaging (HSI) systems are becoming potent technologies for computer vision tasks due to the rich information they uncover, where each substance exhibits a distinct spectral distribution. Although the high spectral dimensionality of the data empowers feature learning, the joint spatial–spectral ...
Houari Youcef Moudjib, Duan Haibin, Baochang Zhang, Mohammed Salah Ahmed Ghaleb   +3 more
openaire   +1 more source

ITER: Image-to-Pixel Representation for Weakly Supervised HSI Classification

IEEE Transactions on Image Processing, 2023
Recent years have witnessed the superiority of deep learning-based algorithms in the field of HSI classification. However, a prerequisite for the favorable performance of these methods is a large number of refined pixel-level annotations.
Jia-Qi Yang, Bo Du, Di Wang, L. Zhang
semanticscholar   +1 more source

Dual Convolutional Neural Networks for Hyperspectral Satellite Images Classification (DCNN-HSI)

2020
Hyperspectral Satellite Images (HSI) presents a very interesting technology for mapping, environmental protection, and security. HSI is very rich in spectral and spatial characteristics, which are non-linear and highly correlated which makes classification difficult.
Maissa Hamouda, Med Salim Bouhlel
openaire   +2 more sources

Spatial–Spectral Enhancement and Fusion Network for Hyperspectral Image Classification With Few Labeled Samples

IEEE Transactions on Geoscience and Remote Sensing
Deep learning has shown great potential in hyperspectral image (HSI) classification. However, training these models usually requires a large amount of labeled data. Since the collection of pixel-level annotations for HSIs is laborious and time-consuming,
Shuang Liu, Chuan Fu, Yu-Le Duan, Xiaopan Wang, Fu-Lin Luo   +4 more
semanticscholar   +1 more source

Spectral–Spatial Feature Tokenization Transformer for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing, 2022
In hyperspectral image (HSI) classification, each pixel sample is assigned to a land-cover category. In the recent past, convolutional neural network (CNN)-based HSI classification methods have greatly improved performance due to their superior ability ...
Le Sun, Guang-Rui Zhao, Yu-Hui Zheng, Ze-Bin Wu   +3 more
semanticscholar   +1 more source

MCTGCL: Mixed CNN–Transformer for Mars Hyperspectral Image Classification With Graph Contrastive Learning

IEEE Transactions on Geoscience and Remote Sensing
Hyperspectral image (HSI) classification has been extensively studied in the context of Earth observation. However, its application in Mars exploration remains limited. Although convolutional neural networks (CNNs) have proven effective in HSI processing,
Bobo Xi, Yun Zhang, Jiao-Jiao Li, Tie Zheng, Xunfeng Zhao, Hai-Tao Xu, Chang-Bin Xue, Yun-Song Li, Jocelyn Chanussot   +8 more
semanticscholar   +1 more source

Hyperspectral Image Classification via Cascaded Spatial Cross-Attention Network

IEEE Transactions on Image Processing
In hyperspectral images (HSIs), different land cover (LC) classes have distinct reflective characteristics at various wavelengths. Therefore, relying on only a few bands to distinguish all LC classes often leads to information loss, resulting in poor ...
Bo Zhang, Ya-Xiong Chen, Sheng-Wu Xiong, Xiao-Qiang Lu   +3 more
semanticscholar   +1 more source

MambaMoE: Mixture-of-Spectral-Spatial-Experts State Space Model for Hyperspectral Image Classification

Information Fusion
Mamba-based models have recently demonstrated significant potential in hyperspectral image (HSI) classification, primarily due to their ability to perform contextual modeling with linear computational complexity.
Yi-Chu Xu, Di Wang, Hongzan Jiao, Le-Fei Zhang, Liang-Pei Zhang   +4 more
semanticscholar   +1 more source