scholarly journals Comparison of Volume and Distance Constraint on Hyperspectral Unmixing

2017 ◽  
Author(s):  
Zeheng Li ◽  
Fuxiang Wang ◽  
Xiurui Geng
Keyword(s):  
Distance Constraint ◽  
Hyperspectral Unmixing

The Dynamic Measurement for Long Baseline of InSAR Based on Distance Constraint

2013 ◽  
Vol 34 (7) ◽  
pp. 1589-1595
Author(s):  
Jing Wang ◽  
Mao-sheng Xiang ◽  
Li-deng Wei ◽  
Hai-liang Wang ◽  
Xi-rui Sun ◽  
...  
Keyword(s):  
Dynamic Measurement ◽  
Distance Constraint ◽  
Long Baseline

Spectral-Spatial Hyperspectral Unmixing in Transformed Domains

2020 ◽  
Author(s):  
Chenguang Xu ◽  
Shaoquan Zhang ◽  
Chengzhi Deng ◽  
Zhaoming Wu ◽  
Jiaheng Yang ◽  
...  
Keyword(s):  
Hyperspectral Unmixing

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.

Endmember independence constrained hyperspectral unmixing via nonnegative tensor factorization

2021 ◽  
Vol 216 ◽  
pp. 106657
Author(s):  
Jin-Ju Wang ◽  
Ding-Cheng Wang ◽  
Ting-Zhu Huang ◽  
Jie Huang ◽  
Xi-Le Zhao ◽  
...  
Keyword(s):  
Tensor Factorization ◽  
Nonnegative Tensor ◽  
Nonnegative Tensor Factorization ◽  
Hyperspectral Unmixing

SateLoc: A Virtual Fingerprinting Approach to Outdoor LoRa Localization Using Satellite Images

2021 ◽  
Vol 17 (4) ◽  
pp. 1-28
Author(s):  
Yuxiang Lin ◽  
Wei Dong ◽  
Yi Gao ◽  
Tao Gu
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Satellite Images ◽  
Low Cost ◽  
Tracking Error ◽  
Location Based Services ◽  
Distance Constraint ◽  
Combination Strategy ◽  
Land Cover Types ◽  
High Resolution Satellite Images

With the increasing relevance of the Internet of Things and large-scale location-based services, LoRa localization has been attractive due to its low-cost, low-power, and long-range properties. However, existing localization approaches based on received signal strength indicators are either easily affected by signal fading of different land-cover types or labor intensive. In this work, we propose SateLoc, a LoRa localization system that utilizes satellite images to generate virtual fingerprints. Specifically, SateLoc first uses high-resolution satellite images to identify land-cover types. With the path loss parameters of each land-cover type, SateLoc can automatically generate a virtual fingerprinting map for each gateway. We then propose a novel multi-gateway combination strategy, which is weighted by the environmental interference of each gateway, to produce a joint likelihood distribution for localization and tracking. We implement SateLoc with commercial LoRa devices without any hardware modification, and evaluate its performance in a 227,500-m urban area. Experimental results show that SateLoc achieves a median localization error of 43.5 m, improving more than 50% compared to state-of-the-art model-based approaches. Moreover, SateLoc can achieve a median tracking error of 37.9 m with the distance constraint of adjacent estimated locations. More importantly, compared to fingerprinting-based approaches, SateLoc does not require the labor-intensive fingerprint acquisition process.

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