Results 111 to 120 of about 2,924,189 (218)

Enhancing Binary Change Detection in Hyperspectral Images Using an Efficient Dimensionality Reduction Technique Within Adversarial Learning

Remote Sensing
Detecting binary changes in co-registered bitemporal hyperspectral images (HSIs) using deep learning methods is challenging due to the high dimensionality of spectral data and significant variations between images.
Amel Oubara, Falin Wu, Guoxin Qu, Reza Maleki, Gongliu Yang   +4 more
doaj   +1 more source

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

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   +3 more sources

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

Spatial analysis for colon biopsy classification from hyperspectral imagery [PDF]

, 2008
Automatic classification of histology images, the objective of our research, is aimed at supporting the pathologists in their diagnosis. In this paper, we present a comparative study between 3D spectral/spatial analysis (SSA) and 2D spatial analysis (SA ...
Rajpoot, Nasir M. (Nasir Mahmood), Masood, Khalid   +1 more
core   +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

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

Light cancellation for hyperspectral material classification

embargoed_20271016This thesis work addresses the problem of hyperspectral reconstruction and illumination estimation to enhance material classification capabilities.
ALOISI, AMERIGO
core  

Improving the resolution of mineralogical maps through the use of ANN with hyperspectral datasets of different resolution

Various applications in satellite remote sensing require high spatial and high spectral resolution. Imaging spectrometers rarely meet these two requirements simultaneously. Hyperspectral sensors acquire high-spectral-resolution and low-spatial-resolution
Mall, Urs, Surkov, Yehor
core   +2 more sources