Spatial-Spectral Information Based Abundance-Constrained Endmember Extraction Methods

Xu, Mingming; Du, Bo; Zhang, Liangpei

doi:10.1109/jstars.2013.2268661

Cited by 32 publications

(35 citation statements)

References 54 publications

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“…In addition, a collection of endmember extraction algorithms that incorporate spatial information has been proposed to refine spectral-only algorithms (Li and Zhang 2011a;Rogge et al 2007;Zhang, Rivard, and Rogge 2008;Xu, Du, and Zhang 2014;Xu, Zhang, and Du 2015). These either average spectrally similar and spatially adjacent endmember candidates produced by spectral-only algorithms, or select endmember candidates that are present in homogenous neighbourhoods.…”

Section: Endmember Extractionmentioning

confidence: 99%

“…Li and Zhang (2011a) refined the iterative error analysis (IEA) approach (Neville et al 1999) by identifying endmember candidates that had sufficient spectrally similar pixels in a local window. Xu, Du, and Zhang (2014) developed the abundance-constrained endmember extraction (ACEE) algorithm and further incorporated spatial information in ACEE. A unique feature of the proposed spatial-spectral information-based ACEE (SSACEE) is that local homogeneity of endmember candidates is determined using estimated abundances instead of pixel spectra.…”

Section: Endmember Extractionmentioning

confidence: 99%

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A survey of methods incorporating spatial information in image classification and spectral unmixing

Wang

Shi

Diao

et al. 2016

International Journal of Remote Sensing

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Over the past decade, the incorporation of spatial information has drawn increasing attention in multispectral and hyperspectral data analysis. In particular, the property of spatial autocorrelation among pixels has shown great potential for improving understanding of remotely sensed imagery. In this paper, we provide a comprehensive review of the state-of-the-art techniques in incorporating spatial information in image classification and spectral unmixing. For image classification, spatial information is accounted for in the stages of pre-classification, sample selection, classifiers, post-classification, and accuracy assessment. With regards to spectral unmixing, spatial information is discussed in the context of endmember extraction, selection of endmember combinations, and abundance estimation. Finally, a perspective on future research directions for advancing spatial-spectral methods is offered.

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Section: Endmember Extractionmentioning

confidence: 99%

Section: Endmember Extractionmentioning

confidence: 99%

A survey of methods incorporating spatial information in image classification and spectral unmixing

Wang

Shi

Diao

et al. 2016

International Journal of Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Recently, methods based on estimated abundance to guide the endmember induction process have been proposed [30], [31]. These methods assume that the pixels with highest abundance absolute values for some set of candidate endmembers are better candidates to be the actual endmembers.…”

Section: Introductionmentioning

confidence: 99%

“…These methods are fast but also sensitive to outliers. In [30], a spatial information process is proposed based on the assumption that the purest signatures are usually distributed in spatially homogeneous areas to overcome the outlier-related issues.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Negative Abundance‐Oriented Hyperspectral Unmixing Algorithm

Marrero

López

Callicó

et al. 2015

IEEE Trans. Geosci. Remote Sensing

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International audienceSpectral unmixing is a popular technique for analyzing remotely sensed hyperspectral data sets with subpixel precision. Over the last few years, many algorithms have been developed for each of the main processing steps involved in spectral unmixing (SU) under the LMM assumption: 1) estimation of the number of endmembers; 2) identification of the spectral signatures of the endmembers; and 3) estimation of the abundance of endmembers in the scene. Although this general processing chain has proven to be effective for unmixing certain types of hyperspectral images, it also has some drawbacks. The first one comes from the fact that the output of each stage is the input of the following one, which favors the propagation of errors within the unmixing chain. A second problem is the huge variability of the results obtained when estimating the number of endmembers of a hyperspectral scene with different state-of-the-art algorithms, which influences the rest of the process. A third issue is the computational complexity of the whole process. To address the aforementioned issues, this paper develops a novel negative abundance-oriented SU algorithm that covers, for the first time in the literature, the main steps involved in traditional hyperspectral unmixing chains. The proposed algorithm can also be easily adapted to a scenario in which the number of endmembers is known in advance and two additional variations of the algorithm are provided to deal with high-noise scenarios and to significantly reduce its execution time, respectively. Our experimental results, conducted using both synthetic and real hyperspectral scenes, indicate that the presented method is highly competitive (in terms of both unmixing accuracy and computational performance) with regard to other SU techniques with similar requirements, while providing a fully self-contained unmixing chain without the need for any input parameters

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Creating Spectral Words for Large-Scale Hyperspectral Remote Sensing Image Retrieval

Zhang

Liu

2016

Lecture Notes in Computer Science

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Spatial-Spectral Information Based Abundance-Constrained Endmember Extraction Methods

Cited by 32 publications

References 54 publications

A survey of methods incorporating spatial information in image classification and spectral unmixing

A survey of methods incorporating spatial information in image classification and spectral unmixing

A Novel Negative Abundance‐Oriented Hyperspectral Unmixing Algorithm

Creating Spectral Words for Large-Scale Hyperspectral Remote Sensing Image Retrieval

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