scholarly journals Contemporary Liquid Brine Exploration on Mars: From Spectral Unmixing to Subpixel Mapping

2019 ◽  
Vol 6 (3) ◽  
pp. 433-466
Author(s):  
Y. Zhong ◽  
D. He ◽  
B. Luo ◽  
L. Zhang
Keyword(s):  
Spectral Unmixing ◽  
Subpixel Mapping

scholarly journals Subpixel Mapping Method of Hyperspectral Images Based on Modified Binary Quantum Particle Swarm Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Shuhan Chen ◽  
Xiaorun Li ◽  
Liaoying Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Mapping Method ◽  
Spectral Unmixing ◽  
Optimization Strategy ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization ◽  
Subpixel Mapping

Subpixel mapping technology can determine the specific location of different objects in the mixed pixel and effectively solve the uncertainty of the ground features spatial distribution in traditional classification technology. Existing methods based on linear optimization encounter the premature and local convergence of the optimization algorithm. This paper proposes a subpixel mapping method based on modified binary quantum particle swarm optimization (MBQPSO) to solve the above issues. The initial subpixel mapping imagery is obtained according to spectral unmixing results. We focus mainly on the discretization of QPSO, which is implemented by modifying the discrete update process of particle location, to minimize the objective function, which is formulated based on different connected regional perimeter calculating methods. To reduce time complexity, a target optimization strategy of global iteration combined with local iteration is performed. The MBQPSO is tested on standard test functions and results show that MBQPSO has the best performance on global optimization and convergent rate. Then, we analyze the proposed algorithm qualitatively and quantitatively by simulated and real experiment; results show that the method combined with MBQPSO and objective function, which is formulated based on the gap length between region and background, has the best performance in accuracy and efficiency.

scholarly journals SUBPIXEL MAPPING OF HYPERSPECTRAL IMAGE BASED ON LINEAR SUBPIXEL FEATURE DETECTION AND OBJECT OPTIMIZATION

2018 ◽  
Vol IV-3 ◽  
pp. 161-165
Author(s):  
Zhaoxin Liu ◽  
Liaoying Zhao ◽  
Xiaorun Li ◽  
Shuhan Chen
Keyword(s):  
Template Matching ◽  
Feature Detection ◽  
Hyperspectral Image ◽  
Mapping Method ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Binary Particle Swarm Optimization ◽  
Mixed Pixels ◽  
Subpixel Mapping ◽  
Treat All

Owing to the limitation of spatial resolution of the imaging sensor and the variability of ground surfaces, mixed pixels are widesperead in hyperspectral imagery. The traditional subpixel mapping algorithms treat all mixed pixels as boundary-mixed pixels while ignoring the existence of linear subpixels. To solve this question, this paper proposed a new subpixel mapping method based on linear subpixel feature detection and object optimization. Firstly, the fraction value of each class is obtained by spectral unmixing. Secondly, the linear subpixel features are pre-determined based on the hyperspectral characteristics and the linear subpixel feature; the remaining mixed pixels are detected based on maximum linearization index analysis. The classes of linear subpixels are determined by using template matching method. Finally, the whole subpixel mapping results are iteratively optimized by binary particle swarm optimization algorithm. The performance of the proposed subpixel mapping method is evaluated via experiments based on simulated and real hyperspectral data sets. The experimental results demonstrate that the proposed method can improve the accuracy of subpixel mapping.

scholarly journals DLR HySU—A Benchmark Dataset for Spectral Unmixing

2021 ◽  
Vol 13 (13) ◽  
pp. 2559
Author(s):  
Daniele Cerra ◽  
Miguel Pato ◽  
Kevin Alonso ◽  
Claas Köhler ◽  
Mathias Schneider ◽  
...  
Keyword(s):  
Spectral Unmixing ◽  
Benchmark Dataset ◽  
Data Sets ◽  
Endmember Extraction ◽  
Imaging Spectrometer ◽  
Hyperspectral Unmixing ◽  
Processing Step ◽  
German Aerospace ◽  
Spectrometer Data ◽  
First Time

Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing methods. In this paper, we introduce the DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, acquired over German Aerospace Center (DLR) premises in Oberpfaffenhofen. The dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes, complemented by simultaneous ground-based reflectance measurements. The DLR HySU benchmark allows a separate assessment of all spectral unmixing main steps: dimensionality estimation, endmember extraction (with and without pure pixel assumption), and abundance estimation. Results obtained with traditional algorithms for each of these steps are reported. To the best of our knowledge, this is the first time that real imaging spectrometer data with accurately measured targets are made available for hyperspectral unmixing experiments. The DLR HySU benchmark dataset is openly available online and the community is welcome to use it for spectral unmixing and other applications.

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