Results 111 to 120 of about 1,349 (217)

Weighted sparse graph based dimensionality reduction for hyperspectral images [PDF]

, 2016
Dimensionality reduction (DR) is an important and helpful preprocessing step for hyperspectral image (HSI) classification. Recently, sparse graph embedding (SGE) has been widely used in the DR of HSIs.
Zhang, Hongyan, Zhang, Liangpei, Liao, Wenzhi, Wenzhi Liao, Wei He, Hongyan Zhang, He, Wei, Wilfried Philips, Philips, Wilfried, Liangpei Zhang   +9 more
core   +1 more source

mHC-HSI: Clustering-Guided Hyper-Connection Mamba for Hyperspectral Image Classification

CoRR
Recently, DeepSeek has invented the manifold-constrained hyper-connection (mHC) approach which has demonstrated significant improvements over the traditional residual connection in deep learning models \cite{xie2026mhc}. Nevertheless, this approach has not been tailor-designed for improving hyperspectral image (HSI) classification.
Yimin Zhu 0002, Zack Dewis, Quinn Ledingham, Saeid Taleghanidoozdoozan, Mabel Heffring, Zhengsen Xu, Motasem Alkayid, Megan Greenwood, Lincoln Linlin Xu   +8 more
openaire   +2 more sources

Spectral Superresolution Using Transformer with Convolutional Spectral Self-Attention

Remote Sensing
Hyperspectral images (HSI) find extensive application across numerous domains of study. Spectral superresolution (SSR) refers to reconstructing HSIs from readily available RGB images using the mapping relationships between RGB images and HSIs.
Xiaomei Liao, Lirong He, Jiayou Mao, Meng Xu   +3 more
doaj   +1 more source

SpectralKAN: Kolmogorov-Arnold Network for Hyperspectral Images Change Detection [PDF]

It has been verified that deep learning methods, including convolutional neural networks (CNNs), graph neural networks (GNNs), and transformers, can accurately extract features from hyperspectral images (HSIs). These algorithms perform exceptionally well
Rong, Xianhui, Yu, Xiaohan, Wang, Jian, Zhang, Yonggang, Wang, Yanheng, Sha, Jianjun, Gao, Lianru, Gao, Yongsheng   +7 more
core   +1 more source

Hyperspectral imaging (HSI) for intraoperative tumor cell classification [PDF]

Laryngo-Rhino-Otologie, 2019
R Beck, H Köhler, F Goltz, A Dietz, C Chalopin, I Gockel   +5 more
openaire   +1 more source

SSHFormer: Optimizing Spectral Reconstruction with a Spatial–Spectral Hybrid Transformer

Remote Sensing
Reconstructing hyperspectral images (HSIs) from RGB images is an effective technique to overcome the high cost of spectrometers. Recently, Transformers have shown potential in capturing long-range dependencies for spectral reconstruction.
Ang Gao, Yubo Dong, Danhua Liu, Anqi Li, Zhenyuan Lin, Yuyan Li   +5 more
doaj   +1 more source

Unsupervised hyperspectral noise estimation and restoration via interband-invariant representation learning [PDF]

Hyperspectral images (HSIs) acquired from different imaging platforms are inevitably contaminated by multiple types of noise. However, the existing supervised learning based denoising methods often show poor generalizability on data with complex ...
Heiskanen, Janne, Zhaozhi Luo, Petri Pellikka, Luo, Zhaozhi, Zhong, Yanfei, Wang, Xinyu, Janne Heiskanen, Xinyu Wang, Yanfei Zhong, Pellikka, Petri   +9 more
core   +1 more source

NR-IQA for UAV hyperspectral image based on distortion constructing, feature screening, and machine learning

International Journal of Applied Earth Observations and Geoinformation
Assessing the quality of UAV-HSIs (Unmanned aerial vehicle hyperspectral images) is crucial for evaluating sensor performance, identifying distortion types, and measuring data inversion accuracy.
Wenzhong Tian, Arturo Sanchez-Azofeifa, Za Kan, Qingzhan Zhao, Guoshun Zhang, Yuzhen Wu, Kai Jiang   +6 more
doaj   +1 more source

Enhanced visualization of hyperspectral images [PDF]

, 2010
: We present an enhanced visualization algorithm for hyperspectral images (HSIs). The visualization is based on the projection onto color matching functions of the human vision system.
Mahmood, Zahid, Scheunders, Paul
core  

Investigation of feature extraction algorithms and techniques for hyperspectral images. [PDF]

, 2017
Doctor of Philosophy (Computer Engineering). University of KwaZulu-Natal. Durban, 2017.Hyperspectral images (HSIs) are remote-sensed images that are characterized by very high spatial and spectral dimensions and nd applications, for example, in land ...
Adebanjo, Hannah Morenike.
core