Results 31 to 40 of about 4,082 (191)

Fusing Spatial Attention with Spectral-Channel Attention Mechanism for Hyperspectral Image Classification via Encoder–Decoder Networks

Remote Sensing, 2022
In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification. However, feature extraction on hyperspectral data still faces numerous challenges.
Jun Sun, Junbo Zhang, Xuesong Gao, Mantao Wang, Dinghua Ou, Xiaobo Wu, Dejun Zhang   +6 more
doaj   +1 more source

Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures

Algorithms, 2020
Hyperspectral image classification has been increasingly used in the field of remote sensing. In this study, a new clustering framework for large-scale hyperspectral image (HSI) classification is proposed.
Mohamed Ismail, Milica Orlandić
doaj   +1 more source

Hyperspectral Image Classification With Spectral and Spatial Graph Using Inductive Representation Learning Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Convolutional neural networks (CNN) have achieved excellent performance for the hyperspectral image (HSI) classification problem due to better extracting spectral and spatial information.
Pan Yang, Lei Tong, Bin Qian, Zheng Gao, Jing Yu, Chuangbai Xiao   +5 more
doaj   +1 more source

HYPERSPECTRAL IMAGE CLASSIFICATION USING MULTI-LAYER PERCEPTRON MIXER (MLP-MIXER) [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023
The classifying of hyperspectral images (HSI) is a difficult task given the high dimensionality of the space, the huge number of spectral bands, and the small number of labeled data.
A. Jamali, M. Mahdianpari, M. Mahdianpari, A. Abdul Rahman   +3 more
doaj   +1 more source

Noise Robust Hyperspectral Image Classification With MNF-Based Edge Preserving Features

Image Analysis and Stereology, 2023
Hyperspectral image (HSI) classification is an important topic in remote sensing. In this paper, we improve the principal component analysis (PCA)-based edge preserving features (EPFs) for HSI classification. We select to use minimum noise fraction (MNF)
Guangyi Chen, Adam Krzyzak, Shen-en Qian
doaj   +1 more source

Multi-level graph learning network for hyperspectral image classification

, 2022
Graph Convolutional Network (GCN) has emerged as a new technique for hyperspectral image (HSI) classification. However, in current GCN-based methods, the graphs are usually constructed with manual effort and thus is separate from the classification task,
Wan, Sheng, Zhong, Shengwei, Yang, Jian, Gong, Chen, Zhan, Yibing, Pan, Shirui, Yang, Jie   +6 more
core   +1 more source

Graph Convolutional Enhanced Discriminative Broad Learning System for Hyperspectral Image Classification

IEEE Access, 2022
Recently, broad learning system (BLS) have demonstrated excellent performance in hyperspectral images (HSI) classification. However, due to the complex geometric structure and spatial layout of HSI, the linear sparse features in broad learning system are
Tuya
doaj   +1 more source

MFFCG – Multi feature fusion for hyperspectral image classification using graph attention network

, 2023
Classification methods that are based on hyperspectral images (HSIs) are playing an increasingly significant role in the processes of target detection, environmental management, and mineral mapping as a result of the fast development of hyperspectral ...
Wu, Guilu, Ullah Bazai, Sibghat, NEIRA MOLINA, HAROLD ROBERTO, Marjan, Shah, Aslam Bhatti, Uzair, Huang, Mengxing, Baryalai, Mehmood, Tang, Hao   +7 more
core   +1 more source

An Unsupervised Cascade Fusion Network for Radiometrically-Accurate Vis-NIR-SWIR Hyperspectral Sharpening

Remote Sensing, 2022
Hyperspectral sharpening has been considered an important topic in many earth observation applications. Many studies have been performed to solve the Visible-Near-Infrared (Vis-NIR) hyperpectral sharpening problem, but there is little research related to
Sihan Huang, David Messinger
doaj   +1 more source

Skin Tone in Hyperspectral Imaging and Its Implications for Fairness in AI. [PDF]

J Biophotonics
This study investigates whether skin tone is systematically encoded in hyperspectral imaging (HSI) data and how this influences AI‐based classification. The results show differences in classification performance across skin tones when using both unsupervised and supervised learning methods, indicating the presence of potential bias. ABSTRACT Artificial
van de Weerd LS, van de Berg NJ, Rijstenberg LL, van de Laar RLO, van Doorn HC, van Beekhuizen HJ.   +5 more
europepmc   +2 more sources