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Keywords = hyperspectal imaging

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28 pages, 115558 KB  
Article
A Knowledge-Based Strategy for Interpretation of SWIR Hyperspectral Images of Rocks
by Frank J. A. van Ruitenbeek, Wim H. Bakker, Harald M. A. van der Werff, Christoph A. Hecker, Kim A. A. Hein and Wijnand van Eijndthoven
Remote Sens. 2025, 17(15), 2555; https://doi.org/10.3390/rs17152555 - 23 Jul 2025
Viewed by 1922
Abstract
Strategies to interpret short-wave infrared hyperspectral images of rocks involve the application of analysis and classification steps that guide the extraction of geological and mineralogical information with the aim of creating mineral maps. Pre-existing strategies often rely on the use of statistical measures [...] Read more.
Strategies to interpret short-wave infrared hyperspectral images of rocks involve the application of analysis and classification steps that guide the extraction of geological and mineralogical information with the aim of creating mineral maps. Pre-existing strategies often rely on the use of statistical measures between reference and image spectra that are scene dependent. Therefore, classification thresholds based on statistical measures to create mineral maps are also scene dependent. This is problematic because thresholds must be adjusted between images to produce mineral maps of the same accuracy. We developed an innovative, knowledge-based strategy to perform mineralogical analyses and create classifications that overcome this problem by using physics-based wavelength positions of absorption features that are invariant between scenes as the main sources of mineral information. The strategy to interpret short-wave infrared hyperspectral images of rocks is implemented using the open source Hyperspectral Python package (HypPy) and demonstrated on a series of hyperspectral images of hydrothermally altered rock samples. The results show how expert knowledge can be embedded into a standardized processing chain to develop reproducible mineral maps without relying on statistical matching criteria. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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20 pages, 7162 KB  
Article
A Standardized Procedure to Build a Spectral Library for Hazardous Chemicals Mixed in River Flow Using Hyperspectral Image
by Yeonghwa Gwon, Dongsu Kim, Hojun You, Su-Han Nam and Young Do Kim
Remote Sens. 2023, 15(2), 477; https://doi.org/10.3390/rs15020477 - 13 Jan 2023
Cited by 10 | Viewed by 3951
Abstract
The occurrence of natural disasters as a consequence of accidental hazardous chemical spills remains a concern. The inadequate, or delayed, initial response may fail to mitigate their impact; hence, imminent monitoring of responses in the initial stage is critical. Classical contact-type measurement methods, [...] Read more.
The occurrence of natural disasters as a consequence of accidental hazardous chemical spills remains a concern. The inadequate, or delayed, initial response may fail to mitigate their impact; hence, imminent monitoring of responses in the initial stage is critical. Classical contact-type measurement methods, however, sometimes miss solvent chemicals and invoke risks for operators during field operation. Remote sensing methods are an alternative method as non-contact, spatially distributable, efficient and continuously operatable features. Herein, we tackle challenges posed by the increasingly available UAV-based hyperspect ral images in riverine environments to identify the presence of hazardous chemical solvents in rivers, which are less investigated in the absence of direct measurement strategies. We propose a referable standard procedure for a unique spectral library based on pre-scanning hyperspectral sensors with respect to representative hazardous chemicals registered on the national hazardous chemical list. We utilized the hyperspectral images to identify 18 types of hazardous chemicals injected into the river in an outdoor environment, where a dedicated hyperspectral ground imaging system mounted with a hyperspectral camera was designed and applied. Finally, we tested the efficiency of the library to recognize unknown chemicals, which showed >70% success rate. Full article
(This article belongs to the Topic Advances in Environmental Remote Sensing)
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16 pages, 23892 KB  
Technical Note
Hyperspectral Anomaly Detection Based on Wasserstein Distance and Spatial Filtering
by Xiaoyu Cheng, Maoxing Wen, Cong Gao and Yueming Wang
Remote Sens. 2022, 14(12), 2730; https://doi.org/10.3390/rs14122730 - 7 Jun 2022
Cited by 12 | Viewed by 3156
Abstract
Since anomaly targets in hyperspectral images (HSIs) with high spatial resolution appear as connected areas instead of single pixels or subpixels, both spatial and spectral information of HSIs can be exploited for a hyperspectal anomaly detection (AD) task. This article proposes a hyperspectral [...] Read more.
Since anomaly targets in hyperspectral images (HSIs) with high spatial resolution appear as connected areas instead of single pixels or subpixels, both spatial and spectral information of HSIs can be exploited for a hyperspectal anomaly detection (AD) task. This article proposes a hyperspectral AD method based on Wasserstein distance (WD) and spatial filtering (called AD-WDSF). Based on the assumption that both background and anomaly targets obey the multivariate Gaussian distribution, background and anomaly target distributions are estimated in the local regions of HSIs. Subsequently, the anomaly intensity of test pixels centered in the local regions are determined via measuring the WD between background and anomaly target distributions. Lastly, spatial filters, i.e., guided filter (GF), total variation curvature filter (TVCF), and Maxtree filter, are exploited to further refine detection results. Experimental results conducted on two real hyperspectral data sets demonstrate that the proposed method achieves competitive detection performance compared with the state-of-the-art AD methods. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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18 pages, 2162 KB  
Article
Spectral-Spatial Attention Networks for Hyperspectral Image Classification
by Xiaoguang Mei, Erting Pan, Yong Ma, Xiaobing Dai, Jun Huang, Fan Fan, Qinglei Du, Hong Zheng and Jiayi Ma
Remote Sens. 2019, 11(8), 963; https://doi.org/10.3390/rs11080963 - 23 Apr 2019
Cited by 247 | Viewed by 18209
Abstract
Many deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN), have been successfully applied to extracting deep features for hyperspectral tasks. Hyperspectral image classification allows distinguishing the characterization of land covers by utilizing their abundant information. Motivated by [...] Read more.
Many deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN), have been successfully applied to extracting deep features for hyperspectral tasks. Hyperspectral image classification allows distinguishing the characterization of land covers by utilizing their abundant information. Motivated by the attention mechanism of the human visual system, in this study, we propose a spectral-spatial attention network for hyperspectral image classification. In our method, RNN with attention can learn inner spectral correlations within a continuous spectrum, while CNN with attention is designed to focus on saliency features and spatial relevance between neighboring pixels in the spatial dimension. Experimental results demonstrate that our method can fully utilize the spectral and spatial information to obtain competitive performance. Full article
(This article belongs to the Special Issue Robust Multispectral/Hyperspectral Image Analysis and Classification)
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