Borrowing least squares analysis from spectral unmixing to classify plastics in SWIR hyperspectral images

2020 ◽  
Author(s):  
Mehrube Mehrubeoglu ◽  
Austin Van Sickle ◽  
Lifford McLauchlan
Keyword(s):  
Least Squares ◽  
Spectral Unmixing ◽  
Hyperspectral Images

scholarly journals Wetland Mapping Using HJ-1A/B Hyperspectral Images and an Adaptive Sparse Constrained Least Squares Linear Spectral Mixture Model

2021 ◽  
Vol 13 (4) ◽  
pp. 751
Author(s):  
Xiaodong Na ◽  
Xingmei Li ◽  
Wenliang Li ◽  
Changshan Wu
Keyword(s):  
Least Squares ◽  
Mixture Model ◽  
Least Squares Method ◽  
Accuracy Assessment ◽  
Spectral Unmixing ◽  
Hyperspectral Images ◽  
Permafrost Degradation ◽  
Constrained Least Squares ◽  
Vegetation Communities ◽  
Spectral Mixture

In this study, we proposed an adaptive sparse constrained least squares linear spectral mixture model (SCLS-LSMM) to map wetlands in a sophisticated scene. It includes three procedures: (1) estimating the abundance based on sparse constrained least squares method with all endmembers in the spectral library, (2) selecting “active” endmember combinations for each pixel based on the estimated abundances and (3) estimating abundances based on the linear spectral unmixing algorithm only with the adaptively selected endmember combinations. The performances of the proposed SCLS-LSMM on wetland vegetation communities mapping were compared with the traditional full constrained least squares linear spectral mixture model (FCLS-LSMM) using HJ-1A/B hyperspectral images. The accuracy assessment results showed that the proposed SCLS-LSMM obtained a significantly better performance with a systematic error (SE) of –0.014 and a root-mean-square error (RMSE) of 0.087 for Reed marsh, and a SE of 0.004 and a RMSE of 0.059 for Weedy meadow, compared with the traditional FCLS-LSMM. The proposed methods improved the unmixing accuracies of wetlands’ vegetation communities and have the potential to understand the process of wetlands’ degradation under the impacts of climate changes and permafrost degradation.

scholarly journals Correction: Zhang, G., et al. Shadow Detection and Restoration for Hyperspectral Images Based on Nonlinear Spectral Unmixing. Remote Sensing 2020, 12, 3985

2021 ◽  
Vol 13 (3) ◽  
pp. 473
Author(s):  
Guichen Zhang ◽  
Daniele Cerra ◽  
Rupert Müller
Keyword(s):  
Remote Sensing ◽  
Spectral Unmixing ◽  
Shadow Detection ◽  
Hyperspectral Images

The authors would like to make the following correction of [...]

scholarly journals Parameterized Nonlinear Least Squares for Unsupervised Nonlinear Spectral Unmixing

2019 ◽  
Vol 11 (2) ◽  
pp. 148 ◽  
Author(s):  
Risheng Huang ◽  
Xiaorun Li ◽  
Haiqiang Lu ◽  
Jing Li ◽  
Liaoying Zhao
Keyword(s):  
Least Squares ◽  
Optimization Problem ◽  
Nonnegative Matrix Factorization ◽  
Optimization Problems ◽  
State Of The Art ◽  
Synthetic Data ◽  
Nonnegative Matrix ◽  
Nonlinear Least Squares ◽  
Hyperspectral Data ◽  
Spectral Unmixing

This paper presents a new parameterized nonlinear least squares (PNLS) algorithm for unsupervised nonlinear spectral unmixing (UNSU). The PNLS-based algorithms transform the original optimization problem with respect to the endmembers, abundances, and nonlinearity coefficients estimation into separate alternate parameterized nonlinear least squares problems. Owing to the Sigmoid parameterization, the PNLS-based algorithms are able to thoroughly relax the additional nonnegative constraint and the nonnegative constraint in the original optimization problems, which facilitates finding a solution to the optimization problems . Subsequently, we propose to solve the PNLS problems based on the Gauss–Newton method. Compared to the existing nonnegative matrix factorization (NMF)-based algorithms for UNSU, the well-designed PNLS-based algorithms have faster convergence speed and better unmixing accuracy. To verify the performance of the proposed algorithms, the PNLS-based algorithms and other state-of-the-art algorithms are applied to synthetic data generated by the Fan model and the generalized bilinear model (GBM), as well as real hyperspectral data. The results demonstrate the superiority of the PNLS-based algorithms.

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