Null Subspace Analysis for Spectral Unmixing in Hyperspectral Remote Sensing

2008 ◽  
Author(s):  
Wenfei Luo ◽  
Liang Zhong ◽  
Bing Zhang
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Spectral Unmixing ◽  
Subspace Analysis

scholarly journals Spectral unmixing approach in hyperspectral remote sensing: a tool for oil palm mapping

TecnoLógicas ◽  
2019 ◽  
Vol 22 (45) ◽  
pp. 129-143
Author(s):  
Hector Vargas ◽  
Ariolfo Camacho Velasco ◽  
Henry Arguello
Keyword(s):  
Remote Sensing ◽  
Oil Palm ◽  
Hyperspectral Image ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Spectral Unmixing ◽  
Spectral Variability ◽  
Computational Framework ◽  
Sensing Data ◽  
Spectral Bands

Oil palm plantations typically span large areas; therefore, remote sensing has become a useful tool for advanced oil palm monitoring. This work reviews and evaluates two approaches to analyze oil palm plantations based on hyperspectral remote sensing data: linear spectral unmixing and spectral variability. Moreover, a computational framework based on spectral unmixing for the estimation of fractional abundances of oil palm plantations is proposed in this study. Such approach also considers the spectral variability of hyperspectral image signatures. More specifically, the proposed computational framework modifies the linear mixing model by introducing a weighting vector, so that the spectral bands that contribute the least to the estimation of erroneous fractional abundances can be identified. This approach improves palm detection as it allows to differentiate them from other materials in terms of fractional abundances. Experimental results obtained from hyperspectral remote sensing data in the range 410-990 nm show improvements of 8.18 % in User Accuracy (Uacc) in the identification of oil palms by the proposed framework with respect to traditional unmixing methods. Thus, the proposed method achieved a 95% Uacc. This confirms the capabilities of the proposed computational framework and facilitates the management and monitoring of large areas of oil palm plantations.

scholarly journals Joint Local Block Grouping with Noise-Adjusted Principal Component Analysis for Hyperspectral Remote-Sensing Imagery Sparse Unmixing

2019 ◽  
Vol 11 (10) ◽  
pp. 1223 ◽  
Author(s):  
Ruyi Feng ◽  
Lizhe Wang ◽  
Yanfei Zhong
Keyword(s):  
Remote Sensing ◽  
Principal Component Analysis ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Hyperspectral Remote Sensing ◽  
Spectral Unmixing ◽  
Spatial Correlations ◽  
Local Block ◽  
Unmixing Model

Spatial regularized sparse unmixing has been proved as an effective spectral unmixing technique, combining spatial information and standard spectral signatures known in advance into the traditional spectral unmixing model in the form of sparse regression. In a spatial regularized sparse unmixing model, spatial consideration acts as an important role and develops from local neighborhood pixels to global structures. However, incorporating spatial relationships will increase the computational complexity, and it is inevitable that some negative influences obtained by inaccurate estimated abundances’ spatial correlations will reduce the accuracy of the algorithms. To obtain a more reliable and efficient spatial regularized sparse unmixing results, a joint local block grouping with noise-adjusted principal component analysis for hyperspectral remote-sensing imagery sparse unmixing is proposed in this paper. In this work, local block grouping is first utilized to gather and classify abundant spatial information in local blocks, and noise-adjusted principal component analysis is used to compress these series of classified local blocks and select the most significant ones. Then the representative spatial correlations are drawn and replace the traditional spatial regularization in the spatial regularized sparse unmixing method. Compared with total variation-based and non-local means-based sparse unmixing algorithms, the proposed approach can yield comparable experimental results with three simulated hyperspectral data cubes and two real hyperspectral remote-sensing images.

scholarly journals Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery

2018 ◽  
Vol 10 (10) ◽  
pp. 1546 ◽  
Author(s):  
Ruyi Feng ◽  
Lizhe Wang ◽  
Yanfei Zhong
Keyword(s):  
Remote Sensing ◽  
Least Squares ◽  
Ordinary Least Squares ◽  
Hyperspectral Remote Sensing ◽  
Spectral Unmixing ◽  
Spectral Library ◽  
Sparse Regression ◽  
Regression Problem ◽  
Remote Sensing Imagery ◽  
Least Angle Regression

Sparse unmixing has been successfully applied in hyperspectral remote sensing imagery analysis based on a standard spectral library known in advance. This approach involves reformulating the traditional linear spectral unmixing problem by finding the optimal subset of signatures in this spectral library using the sparse regression technique, and has greatly improved the estimation of fractional abundances in ubiquitous mixed pixels. Since the potentially large standard spectral library can be given a priori, the most challenging task is to compute the regression coefficients, i.e., the fractional abundances, for the linear regression problem. There are many mathematical techniques that can be used to deal with the spectral unmixing problem; e.g., ordinary least squares (OLS), constrained least squares (CLS), orthogonal matching pursuit (OMP), and basis pursuit (BP). However, due to poor prediction accuracy and non-interpretability, the traditional methods often cannot obtain satisfactory estimations or achieve a reasonable interpretation. In this paper, to improve the regression accuracy of sparse unmixing, least angle regression-based constrained sparse unmixing (LARCSU) is introduced to further enhance the precision of sparse unmixing. Differing from the classical greedy algorithms and some of the cautious sparse regression-based approaches, the LARCSU algorithm has two main advantages. Firstly, it introduces an equiangular vector to seek the optimal regression steps based on the simple underlying geometry. Secondly, unlike the alternating direction method of multipliers (ADMM)-based algorithms that introduce one or more multipliers or augmented terms during their optimization procedures, no parameters are required in the computational process of the LARCSU approach. The experimental results obtained with both simulated datasets and real hyperspectral images confirm the effectiveness of LARCSU compared with the current state-of-the-art spectral unmixing algorithms. LARCSU can obtain a better fractional abundance map, as well as a higher unmixing accuracy, with the same order of magnitude of computational effort as the CLS-based methods.

Hyperspectral Remote Sensing Technology (HRST) program

1997 ◽  
Author(s):  
Tom Wilson ◽  
Rebecca Baugh ◽  
Ron Contillo ◽  
Tom Wilson ◽  
Rebecca Baugh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Remote Sensing Technology ◽  
Sensing Technology
Sign in / Sign up

Export Citation Format

Share Document