Incorporating spatial information in spectral unmixing: A review
نویسندگان
چکیده
منابع مشابه
An Overview of Nonlinear Spectral Unmixing Methods in the Processing of Hyperspectral Data
The hyperspectral imagery provides images in hundreds of spectral bands within different wavelength regions. This technology has increasingly applied in different fields of earth sciences, such as minerals exploration, environmental monitoring, agriculture, urban science, and planetary remote sensing. However, despite the ability of these data to detect surface features, the measured spectrum i...
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The earth is continually being influenced by some actions such as flood, tornado and human artificial activities. This process causes the changes in land cover type. Thus, for optimal management of the use of resources, it is necessary to be aware of these changes. Today’s remote sensing plays key role in geology and environmental monitoring by its high resolution, wide covering and low cost...
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Spectral unmixing of hyperspectral images is one of the most important research fields in remote sensing. Recently, the direct use of spectral libraries in spectral unmixing is on increase. In this way which is called sparse unmixing, we do not need an endmember extraction algorithm and the number determination of endmembers priori. Since spectral libraries usually contain highly correlated s...
متن کاملHyperspectral Image Resolution Enhancement Based on Spectral Unmixing and Information Fusion
Hyperspectral imaging sensors exibit high spectral resolution, but normally low spatial resolution. This leads to spectral signatures of pixels originating from different object types. Such pixels are called mixed pixels. Spectral unmixing methods can be employed to estimate the fractions of reflected light from the different objects within the pixel area. However, spectral unmixing does not pr...
متن کاملHyperspectral Unmixing with Endmember Variability using Semi-supervised Partial Membership Latent Dirichlet Allocation
A semi-supervised Partial Membership Latent Dirichlet Allocation approach is developed for hyperspectral unmixing and endmember estimation while accounting for spectral variability and spatial information. Partial Membership Latent Dirichlet Allocation is an effective approach for spectral unmixing while representing spectral variability and leveraging spatial information. In this work, we exte...
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تاریخ انتشار 2014