scholarly journals An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Youkyung Han ◽  
Anjin Chang ◽  
Seokkeun Choi ◽  
Honglyun Park ◽  
Jaewan Choi
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Hyperspectral Imagery ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Images ◽  
Sensing Data ◽  
Detection Algorithms ◽  
Change Detection Algorithms ◽  
Spectral Profiles

Multitemporal hyperspectral remote sensing data have the potential to detect altered areas on the earth’s surface. However, dissimilar radiometric and geometric properties between the multitemporal data due to the acquisition time or position of the sensors should be resolved to enable hyperspectral imagery for detecting changes in natural and human-impacted areas. In addition, data noise in the hyperspectral imagery spectrum decreases the change-detection accuracy when general change-detection algorithms are applied to hyperspectral images. To address these problems, we present an unsupervised change-detection algorithm based on statistical analyses of spectral profiles; the profiles are generated from a synthetic image fusion method for multitemporal hyperspectral images. This method aims to minimize the noise between the spectra corresponding to the locations of identical positions by increasing the change-detection rate and decreasing the false-alarm rate without reducing the dimensionality of the original hyperspectral data. Using a quantitative comparison of an actual dataset acquired by airborne hyperspectral sensors, we demonstrate that the proposed method provides superb change-detection results relative to the state-of-the-art unsupervised change-detection algorithms.

scholarly journals Gudalur Spectral Target Detection (GST-D): A New Benchmark Dataset and Engineered Material Target Detection in Multi-Platform Remote Sensing Data

2020 ◽  
Vol 12 (13) ◽  
pp. 2145 ◽  
Author(s):  
Sudhanshu Shekhar Jha ◽  
Rama Rao Nidamanuri
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Satellite Imagery ◽  
Hyperspectral Imagery ◽  
Remote Sensing Data ◽  
Benchmark Dataset ◽  
Remote Sensing Imagery ◽  
Sensing Data ◽  
Detection Algorithms ◽  
Reference Target

Target detection in remote sensing imagery, mapping of sparsely distributed materials, has vital applications in defense security and surveillance, mineral exploration, agriculture, environmental monitoring, etc. The detection probability and the quality of retrievals are functions of various parameters of the sensor, platform, target–background dynamics, targets’ spectral contrast, and atmospheric influence. Generally, target detection in remote sensing imagery has been approached using various statistical detection algorithms with an assumption of linearity in the image formation process. Knowledge on the image acquisition geometry, and spectral features and their stability across different imaging platforms is vital for designing a spectral target detection system. We carried out an integrated target detection experiment for the detection of various artificial target materials. As part of this work, we acquired a benchmark multi-platform hyperspectral and multispectral remote sensing dataset named as ‘Gudalur Spectral Target Detection (GST-D)’ dataset. Positioning artificial targets on different surface backgrounds, we acquired remote sensing data by terrestrial, airborne, and space-borne sensors on 20th March 2018. Various statistical and subspace detection algorithms were applied on the benchmark dataset for the detection of targets, considering the different sources of reference target spectra, background, and the spectral continuity across the platforms. We validated the detection results using the receiver operation curve (ROC) for different cases of detection algorithms and imaging platforms. Results indicate, for some combinations of algorithms and imaging platforms, consistent detection of specific material targets with a detection rate of about 80% at a false alarm rate between 10−2 to 10−3. Target detection in satellite imagery using reference target spectra from airborne hyperspectral imagery match closely with the satellite imagery derived reference spectra. The ground-based in-situ reference spectra offer a quantifiable detection in airborne or satellite imagery. However, ground-based hyperspectral imagery has also provided an equivalent target detection in the airborne and satellite imagery paving the way for rapid acquisition of reference target spectra. The benchmark dataset generated in this work is a valuable resourcefor addressing intriguing questions in target detection using hyperspectral imagery from a realistic landscape perspective.

Natural and artificial target recognition by hyperspectral remote sensing data

2002 ◽  
Author(s):  
Bing Zhang ◽  
Liangyun Liu ◽  
Yongchao Zhao ◽  
Genxing Xu ◽  
Lanfen Zheng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Target Recognition ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Sensing Data

scholarly journals Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection

2008 ◽  
Vol 46 (6) ◽  
pp. 1822-1835 ◽  
Author(s):  
G. Camps-Valls ◽  
L. Gomez-Chova ◽  
J. Munoz-Mari ◽  
J.L. Rojo-Alvarez ◽  
M. Martinez-Ramon
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Remote Sensing Data ◽  
Data Classification ◽  
Sensing Data
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