A Spatial Compositional Model for Linear Unmixing and Endmember Uncertainty Estimation
نویسندگان
چکیده
منابع مشابه
Geometrical 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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Remote sensing images contain a lot of mixed image pixels, but it is difficult to classify these pixels. If the number of pixel’s end-member is regarded as unchangeable, the traditional pixel unmixing algorithm cannot get a good result. In this paper we develop a new method of selective end-members for pixel unmixing based on the fuzzy ARTMAP neural network, which firstly compares the pixel’s s...
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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...
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The analysis of hyperspectral images on the basis of the spectral decomposition of their pixels through the so called spectral unmixing process, has applications in tematic map generation, target detection and unsupervised image segmentation. The critical step is the determination of the endmembers used as the references for the unmixing process. We give a comprehensive enumeration of the metho...
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Inverse theory concerns the problem of making inferences about physical systems from indirect noisy measurements. Information about the errors in the observations is essential to solve any inverse problem, otherwise it is impossible to say when a feature “fits the data.” In practice, however, one seldom has a direct estimate of the data errors. We exploit the trade-off between data prediction a...
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ژورنال
عنوان ژورنال: IEEE Transactions on Image Processing
سال: 2016
ISSN: 1057-7149,1941-0042
DOI: 10.1109/tip.2016.2618002