Results 111 to 120 of about 4,082 (191)

Self- and Cross-Attention Enhanced Transformer for Visible and Thermal Infrared Hyperspectral Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Visible hyperspectral image (V-HSI) and thermal infrared hyperspectral image (TI-HSI) have been crucial data sources for land cover classification. V-HSI can directly provide information of land surface, such as shape, color, texture, and other features.
Enyu Zhao, Yongfang Su, Nianxin Qu, Yulei Wang, Caixia Gao, Jian Zeng   +5 more
doaj   +1 more source

Lightweight Hyperspectral Image Classification with Lifelong Learning

, 2023
With the increasing number of Hyperspectral Image (HSI) classification scenarios and frequent updates to classification categories, there is a growing demand for new models and updates to existing ones.
Jing Bai (16388306), Zhu Xiao (16388309), Zhongrui Wang (9205625), Anran Yuan (16388289), Licheng Jiao (12301451), Jianqing Li (16388312)   +5 more
core   +1 more source

Spatial-Aware Network for Hyperspectral Image Classification

, 2021
Deep learning is now receiving widespread attention in hyperspectral image (HSI) classification. However, due to the imbalance between a huge number of weights and limited training samples, many problems and difficulties have arisen from the use of deep ...
Yicong Zhou, Yantao Wei
core   +1 more source

Spectral–Spatial Discriminant Feature Learning for Hyperspectral Image Classification

, 2019
Sparse representation classification (SRC) is being widely applied to target detection in hyperspectral images (HSI). However, due to the problem in HSI that high-dimensional data contain redundant information, SRC methods may fail to achieve high ...
Chunhua Dong, Masoud Naghedolfeizi, Dawit Aberra, Xiangyan Zeng   +3 more
core   +1 more source

Joint Alternate Small Convolution and Feature Reuse for Hyperspectral Image Classification

ISPRS International Journal of Geo-Information, 2018
A hyperspectral image (HSI) contains fine and rich spectral information and spatial information of ground objects, which has great potential in applications.
Hongmin Gao, Yao Yang, Chenming Li, Hui Zhou, Xiaoyu Qu   +4 more
doaj   +1 more source

Structure Extraction With Total Variation for Hyperspectral Image Classification

IEEE Access, 2019
This paper proposes a novel structure extraction approach that is able to achieve high classification accuracy and low computing burden to hyperspectral image (HSI) classification based on total variation (SETV).
Qiaoqiao Li, Haibo Wang, Guoyue Chen, Kazuki Saruta, Yuki Terata   +4 more
doaj   +1 more source

Improving Hyperspectral Image Classification with Compact Multi-Branch Deep Learning

Remote Sensing
The progress in hyperspectral image (HSI) classification owes much to the integration of various deep learning techniques. However, the inherent 3D cube structure of HSIs presents a unique challenge, necessitating an innovative approach for the efficient
Md. Rashedul Islam, Md. Touhid Islam, Md Palash Uddin, Anwaar Ulhaq   +3 more
doaj   +1 more source

A Joint Network of 3D-2D CNN Feature Hierarchy and Pyramidal Residual Model for Hyperspectral Image Classification

IEEE Access
Since convolutional neural networks (CNN) can extract deeper features from hyperspectral images, they show good classification performance in the hyperspectral image (HSI) classification task. However, the performance of many CNN models is constrained by
Hongwei Wei, Yufan Wang, Yu Sun, Jianfeng Zheng, Xiaodong Yu   +4 more
doaj   +1 more source

An Experimental Study for the Effects of Noise on Hyperspectral Imagery Classification

Image Analysis and Stereology
Hyperspectral image (HSI) classification is a very important topic in remote sensing. There are many published methods for HSI classification in the literature. Nevertheless, it is not clear which method is the most robust to noise in HSI data cubes. In
Guangyi Chen, Adam Krzyzak, Shen-en Qian
doaj   +1 more source

AMHFN: Aggregation Multi-Hierarchical Feature Network for Hyperspectral Image Classification

Remote Sensing
Deep learning methods like convolution neural networks (CNNs) and transformers are successfully applied in hyperspectral image (HSI) classification due to their ability to extract local contextual features and explore global dependencies, respectively ...
Xiaofei Yang, Yuxiong Luo, Zhen Zhang, Dong Tang, Zheng Zhou, Haojin Tang   +5 more
doaj   +1 more source