A Gaussian elimination based fast endmember extraction algorithm for hyperspectral imagery

Geng, Xiurui; Xiao, Zhengqing; Ji, Liu; Zhao, Yongchao; Wang, Fuxiang

doi:10.1016/j.isprsjprs.2013.02.020

Cited by 41 publications

(18 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The spectrum of yellow water is very similar to that of ice/snow, but with a much lower value. A pure water signature can be extracted by endmember extraction algorithms [40,41], or by using water signatures in a spectral library. In this study, for similarity, we manually pick some pure water points in the image for each kind of water based on the ground reference maps.…”

Section: Pure Water Signature Extractionmentioning

confidence: 99%

Target Detection Method for Water Mapping Using Landsat 8 OLI/TIRS Imagery

Geng

Sun

et al. 2015

Water

Self Cite

View full text Add to dashboard Cite

Extracting surface water distribution with satellite imagery has been an important subject in remote sensing. Spectral indices of water only use information from a limited number of bands, thus they may have poor performance from pixels contaminated by ice/snow, clouds, etc. The detection algorithms using information from all spectral bands, such as constrained energy minimization (CEM), could avoid this problem to some extent. However, these are mostly designed for hyperspectral imagery, and may fail when applied to multispectral data. It has been proved that adding linearly irrelevant data to original data could improve the performance of CEM. In this study, two kinds of linearly irrelevant data are added for water extraction: the spectral indices and the spectral similarity metric data. CEM is designed for targets with low-probability distribution in an image, but water bodies do not always satisfy this condition. We thereby impose a sensible coefficient for each pixel to form the weighted autocorrelation matrix. In this study, the weight is based on the orthogonal subspace projection, so this new method is named Orthogonal subspace projection Weighted CEM (OWCEM). The newly launched Landsat 8 images over two lakes, the Hala Lake in China with ice/snow distributed in the north, and the Huron Lake in OPEN ACCESSWater 2015, 7 795North America, a lake with a very large surface area, are selected to test the accuracy and robustness of our algorithm. The Kappa coefficient and the receiver operating characteristic (ROC) curve are calculated as an accuracy evaluation standard. For both lakes, our method can greatly suppress the background (including ice/snow and clouds) and extract the complete water surface with a high accuracy (Kappa coefficient > 0.96).

show abstract

Section: Pure Water Signature Extractionmentioning

confidence: 99%

Target Detection Method for Water Mapping Using Landsat 8 OLI/TIRS Imagery

Geng

Sun

et al. 2015

Water

Self Cite

View full text Add to dashboard Cite

show abstract

“…As illustrated in Figure 3a, a water-land mixing model assumes that a mixed pixel is a linear combination of water and land endmembers weighted by their corresponding fractions, which satisfy the abundance sum-to-one constraint (ASC) and abundance nonnegative constraint (ANC) [28], where r is the spectrum of the mixed pixel, eW and eL correspond to the water and land endmembers, and cW and cL represent the fractions of water and land endmembers. The endmembers can be extracted from the Landsat images using the common endmember extraction algorithms [28][29][30][31][32][33]. However, these algorithms often assume that all pixels in the image are constructed by the same mixing model.…”

Section: Solving the Spectral Mixing Problem Using Local Spectral Unmmentioning

confidence: 99%

Improving the Accuracy of the Water Surface Cover Type in the 30 m FROM-GLC Product

Gong

Geng

et al. 2015

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Abstract:The finer resolution observation and monitoring of the global land cover (FROM-GLC) product makes it the first 30 m resolution global land cover product from which one can extract a global water mask. However, two major types of misclassification exist with this product due to spectral similarity and spectral mixing. Mountain and cloud shadows are often incorrectly classified as water since they both have very low reflectance, while more water pixels at the boundaries of water bodies tend to be misclassified as land. In this paper, we aim to improve the accuracy of the 30 m FROM-GLC water mask by addressing those two types of errors. For the first, we adopt an object-based method by computing the topographical feature, spectral feature, and geometrical relation with cloud for every water object in the FROM-GLC water mask, and set specific rules to determine whether a water object is misclassified. For the second, we perform a local spectral unmixing using a two-endmember linear mixing model for each pixel falling in the water-land boundary zone that is 8-neighborhood connected to water-land boundary pixels. Those pixels with big enough water fractions are determined as water. The procedure is automatic. Experimental results show that the total area of inland water has been decreased by 15.83% in the new global water mask compared with the FROM-GLC water mask. Specifically, OPEN ACCESSRemote Sens. 2015, 7 13508 more than 30% of the FROM-GLC water objects have been relabeled as shadows, and nearly 8% of land pixels in the water-land boundary zone have been relabeled as water, whereas, on the contrary, fewer than 2% of water pixels in the same zone have been relabeled as land. As a result, both the user's accuracy and Kappa coefficient of the new water mask (UA = 88.39%, Kappa = 0.87) have been substantially increased compared with those of the FROM-GLC product (UA = 81.97%, Kappa = 0.81).

show abstract

“…LMM is the most popular model for hyperspectral image analysis [28], [3]. Suppose we are given a hyperspectral image with L bands and N pixels {y n } N n=1 ∈ R L + .…”

Section: A Linear Mixture Model (Lmm)mentioning

confidence: 99%

Structured Sparse Method for Hyperspectral Unmixing

Zhu

Wang

Xiang

et al. 2014

ISPRS Journal of Photogrammetry and Remote Sensing

223

136

View full text Add to dashboard Cite

Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method from the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly.

show abstract

A Gaussian elimination based fast endmember extraction algorithm for hyperspectral imagery

Cited by 41 publications

References 27 publications

Target Detection Method for Water Mapping Using Landsat 8 OLI/TIRS Imagery

Target Detection Method for Water Mapping Using Landsat 8 OLI/TIRS Imagery

Improving the Accuracy of the Water Surface Cover Type in the 30 m FROM-GLC Product

Structured Sparse Method for Hyperspectral Unmixing

Contact Info

Product

Resources

About