Results 21 to 30 of about 13,575,612 (179)
Improved sparse representation using adaptive spatial support for effective target detection in hyperspectral imagery [PDF]
, 2013 With increasing applications of hyperspectral imagery (HSI) in agriculture, mineralogy, military, and other fields, one of the fundamental tasks is accurate detection of the target of interest.
Li, Xiaohui +3 more
core +4 more sources
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 +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 +3 more
doaj +1 more source
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
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
MambaHSI: Spatial–Spectral Mamba for Hyperspectral Image Classification [PDF]
IEEE Transactions on Geoscience and Remote SensingTransformer has been extensively explored for hyperspectral image (HSI) classification. However, transformer poses challenges in terms of speed and memory usage because of its quadratic computational complexity. Recently, the Mamba model has emerged as a
Ya-Peng Li +4 more
semanticscholar +1 more source
O129 CLASSIFICATION OF BARRETT’S CARCINOMA SPECIMENS BY HYPERSPECTRAL IMAGING (HSI)
Diseases of the Esophagus, 2019 Abstract Aim Hyperspectral imaging (HSI) technology combines imaging with spectroscopy and can be used for the classification of malignant and non-malignant cells. Thereby HSI combined with artificial intelligent algorithms can be used to predict tumor cells in in Barrett’s carcinoma specimens.
Thieme René +5 more
openaire +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 +13 more
core +1 more source
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
SPECIAL: Zero-Shot Hyperspectral Image Classification With CLIP [PDF]
IEEE Transactions on Geoscience and Remote SensingHyperspectral image (HSI) classification aims to categorize each pixel in an HSI into a specific land cover class, which is crucial for applications such as remote sensing, environmental monitoring, and agriculture.
Li Pang +5 more
semanticscholar +1 more source