A spectral correction method for multi-scattering effects in close range hyperspectral imagery of vegetation scenes: application to nitrogen content assessment in wheat
Sign Up to like & getrecommendations! Published in 2018 at "Precision Agriculture"
DOI: 10.1007/s11119-018-9613-2
Abstract: In-field hyperspectral imagery is a promising tool for crop phenotyping or monitoring. In association with partial least square regression (PLS-R), it allows building high spatial resolution maps of the chemical content of plant leaves. However,… read more here.
Keywords: method multi; nitrogen content; content; multi scattering ... See more keywords
Mapping acidic mine waste with seasonal airborne hyperspectral imagery at varying spatial scales
Sign Up to like & getrecommendations! Published in 2017 at "Environmental Earth Sciences"
DOI: 10.1007/s12665-017-6763-x
Abstract: Airborne imaging spectrometer (also known as hyperspectral) remote sensing has been widely used to characterize mineralogy on mine waste surfaces, which is useful for predicting potential sources of acidity and metal leaching. The most successful… read more here.
Keywords: varying spatial; mine waste; imagery varying; mine ... See more keywords
Spectral-spatial classification of hyperspectral imagery with cooperative game
Sign Up to like & getrecommendations! Published in 2018 at "Isprs Journal of Photogrammetry and Remote Sensing"
DOI: 10.1016/j.isprsjprs.2017.10.006
Abstract: Abstract Spectral-spatial classification is known to be an effective way to improve classification performance by integrating spectral information and spatial cues for hyperspectral imagery. In this paper, a game-theoretic spectral-spatial classification algorithm (GTA) using a… read more here.
Keywords: game; classification; spectral spatial; cooperative game ... See more keywords
Hyperspectral imagery classification with cascaded support vector machines and multi-scale superpixel segmentation
Sign Up to like & getrecommendations! Published in 2020 at "International Journal of Remote Sensing"
DOI: 10.1080/01431161.2020.1723172
Abstract: ABSTRACT Hyperspectral imagery (HSI) classification is a rapidly growing and highly active research area in the field of hyperspectral community. The method that combines both spatial and spectral information for hyperspectral image classification has made… read more here.
Keywords: classification; information; support vector; vector machines ... See more keywords
Adaptive collaborative graph for discriminant analysis of hyperspectral imagery
Sign Up to like & getrecommendations! Published in 2020 at "European Journal of Remote Sensing"
DOI: 10.1080/22797254.2020.1735947
Abstract: ABSTRACT Recently, sparse graph-based discriminant analysis (SGDA) and collaborative graph-based discriminant analysis (CGDA) have been developed for dimensionality reduction of hyperspectral imagery. In SGDA or CGDA, a graph is constructed by -norm minimization-based representation or… read more here.
Keywords: collaborative graph; representation; graph; discriminant analysis ... See more keywords
Cluster-Based Spectral-Spatial Segmentation of Hyperspectral Imagery
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DOI: 10.1109/access.2020.3011668
Abstract: In this paper, we develop and test a three-stage algorithm for performing unsupervised segmentation of hyperspectral imagery. Each stage of the algorithm leverages modified clustering methods which incorporate both the spatial and spectral information present… read more here.
Keywords: stage algorithm; cluster based; segmentation hyperspectral; segmentation ... See more keywords
HSI-IPNet: Hyperspectral Imagery Inpainting by Deep Learning With Adaptive Spectral Extraction
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2020.3012443
Abstract: Feature representation is the key to the hyperspectral images (HSI) inpainting. Existing works mainly focus on using spectral and temporal auxiliary images to inpainting the corrupted region, which were proved to be low robust for… read more here.
Keywords: hsi ipnet; ipnet hyperspectral; imagery inpainting; inpainting deep ... See more keywords
A Practical Temperature and Emissivity Separation Framework With Reanalysis Atmospheric Profiles for Hyper-Cam Airborne Thermal Infrared Hyperspectral Imagery
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2021.3136194
Abstract: Compared with land surface temperature (LST) and land surface emissivity (LSE) retrieval from single-band or multispectral thermal infrared (TIR) data, TIR hyperspectral imagery allows us to obtain accurate LST and LSE through the use of… read more here.
Keywords: hyper cam; framework; hyperspectral imagery; emissivity ... See more keywords
Shadow-Aware Nonlinear Spectral Unmixing for Hyperspectral Imagery
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2022.3188896
Abstract: In hyperspectral imagery, differences in ground surface structures cause a large variation in the optical scattering in sunlit and (partly) shadowed pixels. The complexity of the scene demands a general spectral mixture model that can… read more here.
Keywords: proposed model; hyperspectral imagery; model; illumination ... See more keywords
DSSNet: A Simple Dilated Semantic Segmentation Network for Hyperspectral Imagery Classification
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2019.2960528
Abstract: Deep learning-based methods have presented a promising performance in the task of hyperspectral imagery classification (HSIC). However, recent methods usually are considered HSIC as a patchwise image classification problem and addressed it by giving a… read more here.
Keywords: hyperspectral imagery; classification; segmentation network; semantic segmentation ... See more keywords
Target Detection in Hyperspectral Imagery Using Atmospheric-Spectral Modeling and Deep Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3215576
Abstract: Target detection (TD) in spectral imagery is an evolving analytical perspective with broader application potential. The perceived distinctness of the spectral signatures of the materials of interest is exploited for detecting targets in hyperspectral imagery.… read more here.
Keywords: detection hyperspectral; target detection; deep learning; hyperspectral imagery ... See more keywords