Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery
نویسندگان
چکیده
منابع مشابه
EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing
Data acquired from multi-channel sensors is a highly valuable asset to interpret the environment for a variety of remote sensing applications. However, low spatial resolution is a critical limitation for the sensors and the constituent materials of a scene can be mixed in different fractions due to their spatial interactions. Spectral unmixing is a technique that allows us to obtain the materia...
متن کاملGeometrical Endmember Extraction and Linear Spectral Unmixing of Multispectral Image
Accurate mapping is prepared using Linear unmixing of satellite images. Endmember extraction contributes the unmixing accuracy. In this paper, Endmembers are extracted using different Geometrical algorithms like Pixel Purity Index (PPI), Nearest Finder (N-FINDR) and Sequential Maximum Angle Convex Cone (SMACC) algorithms. Extracted Endmembers are given as input for unmixing and it is attempted ...
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Spectral unmixing given a library of endmember spectra can be achieved by multiple endmember spectral mixture analysis (MESMA), which tries to find the optimal combination of endmember spectra for each pixel by iteratively examining each endmember combination. However, as library size grows, computational complexity increases which often necessitates a laborious and heuristic library reduction ...
متن کاملMultiobjective Optimized Endmember Extraction for Hyperspectral Image
Endmember extraction (EE) is one of the most important issues in hyperspectral mixture analysis. It is also a challenging task due to the intrinsic complexity of remote sensing images and the lack of priori knowledge. In recent years, a number of EE methods have been developed, where several different optimization objectives have been proposed from different perspectives. In all of these method...
متن کاملEndmember Extraction from Hyperspectral Image
For a single pixel in a hyperspectral image, its spectrum is a mixture of several spectra from different materials. Therefore, a hyperspectral image can be seen as highly mixed data. Thus the common problem is how to decompose these mixed pixels into endmembers and their corresponding proportions. Each endmember presents a material, and its proportion shows its percentage among other materials....
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2009
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2009.2025797