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Multiple endmember Hyperspectral Sparse Unmixing Based on Improved OMP Algorithm

Journal of Harbin Institute of Technology(New Series) 2015年第5期22卷 97-104页

作者： Chunhui Zhao Haifeng Zhu Shiling Cui Bin Qi College of Information and Communication Engineering Harbin Engineering University

In conventional linear spectral mixture analysis model,a class is represented by a single ***,the intra-class spectral variability is usually very large,which makes it difficult to represent a class,and in this case,it leads to incorrect unmixing results. Some proposed algorithms play a positive role in overcoming the endmember variability,but there are shortcomings on computation intensive,unsatisfactory unmixing results and so on. Recently,sparse regression has been applied to unmixing,assuming each mixed pixel can be expressed as a linear combination of only a few spectra in a spectral library. It is essentially the same as multiple endmember spectral unmixing. OMP( orthogonal matching pursuit),a sparse reconstruction algorithm,has advantages of simple structure and high efficiency. However,it does not take into account the constraints of abundance non-negativity and abundance sum-to-one( ANC and ASC),leading to undesirable unmixing results. In order to solve these issues,this paper presents an improved OMP algorithm( fully constraint OMP,FOMP) for multiple endmember hyperspectral sparse unmixing. The proposed algorithm overcomes the shortcomings of OMP,and on the other hand,it solves the problem of endmember *** ANC and ASC constraints are firstly added into the OMP algorithm,and then the endmember set is refined by the relative increase in root-mean-square-error( RMSE) to avoid over-fitting,finally pixels are unmixed by their optimal endmember set. The simulated and real hyperspectral data experiments show that FOPM unmixing results are ideally comparable and abundance RMSE reduces much lower than OMP and simple spectral mixture analysis( s SMA),and has a strong anti-noise performance. It proves that multiple endmember spectral mixture analysis is more reasonable.

关键词： hyperspectral image sparse representation multiple endmember spectral unmixing OMP ANC and ASC

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Matrix calculation of high-dimensional cross product and its application in automatic recognition of the endmembers of hyperspectral imagary

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Science China(Information Sciences) 2011年第1期54卷 197-203页

作者： GENG XiuRui1,ZHAO YongChao1,LIU SuHong2 & WANG FuXiang3 1Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics,Chinese Academy of Sciences,Beijing 100080,China 2State Key Laboratory of Remote Sensing Science,Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing Applications of Chinese Academy of Sciences, School of Geography,Beijing Normal University,Beijing 100875,China 3School of Electronics and Information Engineering,Beihang University,Beijing 100191,China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing China State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing Applications of Chinese Academy of Sciences School of Geography Beijing Normal University Beijing China School of Electronics and Information Engineering Beihang University Beijing China

This paper gives the definition of the high-dimensional cross product and its calculation by extending the 3-D cross product definition into the high-dimensional vector *** on the properties of the cross product,the volume variance index(VVI)is proposed to be used in extracting automatically the endmembers of the hypherspectral imagery which eliminates the shortcoming of the traditional method of using simplex only where the extraction results were easily impacted by the abnormal pixels.A case study of endmembers extraction experiment using the VVI method with the AVIRIS data for Cuprite has shown a very good result.

关键词： endmember simplex hyperspectral imagery cross product

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Non-negative matrix factorization based unmixing for principal component transformed hyperspectral data

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Frontiers of Information Technology & Electronic Engineering 2016年第5期17卷 403-412页

作者： Xiu-rui GENG Lu-yan JI Kang SUN Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China MOE Key Laboratory for Earth System Modeling Center for Earth System Science Tsinghua University Beijing 100084 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang 050000 China

Non-negative matrix factorization(NMF) has been widely used in mixture analysis for hyperspectral remote sensing. When used for spectral unmixing analysis, however, it has two main shortcomings:(1) since the dimensionality of hyperspectral data is usually very large, NMF tends to suffer from large computational complexity for the popular multiplicative iteration rule;(2) NMF is sensitive to noise(outliers), and thus the corrupted data will make the results of NMF meaningless. Although principal component analysis(PCA) can be used to mitigate these two problems, the transformed data will contain negative numbers, hindering the direct use of the multiplicative iteration rule of NMF. In this paper, we analyze the impact of PCA on NMF, and find that multiplicative NMF can also be applicable to data after principal component transformation. Based on this conclusion, we present a method to perform NMF in the principal component space, named ‘principal component NMF'(PCNMF). Experimental results show that PCNMF is both accurate and time-saving.

关键词： Non-negative matrix factorization(NMF) Principal component analysis(PCA) endmember Hyperspectral

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Detection of short-term urban land use changes by combining SAR time series images and spectral angle mapping

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Frontiers of Earth Science 2019年第3期13卷 495-509页

作者： Zhuokun PAN Yueming HU Guangxing WANG College of Natural Resources and Environment South China Agricultural UniversityGuangzhou 510642China Key Laboratory of Construction Land Transformation Ministry of Land and ResourcesGuangzhou 510642China Guangdong Provincial Key Laboratory of Land Use and Consolidation Guangzhou 510642China Guangdong Provincial Land Information Engineering Research Center Guangzhou 510642China College of Agriculture and Animal Husbandry Qinghai UniversityXining 810016China Department of Geography Southern Illinois University at CarbondaleIL 62901USA

Rapid urban sprawl and re-construction of old towns have been leading to great changes of land use in cities of China. To witness short-term urban land use changes, rapid or real time remote sensing images and effective detection methods are required. With the availability of short repeat cycle, relatively high spatial resolution, and weather-independent Synthetic Aperture Radar (SAR) remotely sensed data, detection of short-term urban land use changes becomes possible. This paper adopts newly released Sentinel-1 SAR data for urban change detection in Tianhe District of Guangzhou City in Southern China, where dramatic urban redevelopment practices have been taking place in past years. An integrative method that combines the SAR time series data and a spectral angle mapping (SAM) was developed and applied to detect the short-term land use changes. Linear trend transformations of the SAR time series data were first conducted to reveal patterns of substantial changes. Spectral mixture analysis was then conducted to extract temporal endmembers to reflect the land development patterns based on the SAR backscattering intensities over time. Moreover, SAM was applied to extract the information of significant increase and decrease patterns. The results of validation and method comparison showed a significant capability of both the proposed method and the SAR time series images for detecting the short-term urban land use changes. The method received an overall accuracy of 78%, being more accurate than that using a bi-temporal image change detection method. The results revealed land use conversions due to the removal of old buildings and their replacement by new construction. This implies that SAR time series data reflects the spatiotemporal evolution of urban constructed areas within a short time period and this study provided the potential for detecting changes that requires continuously short-term capability, and could be potential in other landscapes.

关键词： Sentinel-1 SAR time series images urban land use change detection temporal endmember spectral angle mapping

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