Abstract
Hyperspectral remote sensing technology has a strong capability for ground object detection due to the low spatial resolution of hyperspectral imaging spectrometers. A single pixel that leads to a hyperspectral remote sensing image usually contains more than one feature coverage type, resulting in a mixed pixel. The existence of a mixed pixel affects the accuracy of the ground object identification and classification and hinders the application and development of hyperspectral technology. For the problem of unmixing of mixed pixels in hyperspectral images (HSIs), the linear mixing model can model the mixed pixels well. Through the collation of nearly five years of the literature, this paper introduces the development status and problems of linear unmixing models from four aspects: geometric method, nonnegative matrix factorization (NMF), Bayesian method, and sparse unmixing.
Highlights
Hyperspectral imaging has a pivotal role in the field of remote sensing, which collects and processes information across the entire electromagnetic spectrum
It can be seen that the linear spectral mixing model is still a hot topic at home and abroad, and it is the most widely used model. e linear spectral mixing model has the advantages of simplicity, high efficiency, and clear physical meaning, which is scientific in theory, and the linear spectral mixing model can better describe the actual spectral mixing phenomenon for hyperspectral images with spatial resolution below the meter level
Fang et al [42] proposed a hyperspectral unmixing algorithm based on the sparsity of abundance and the local invariance of images, which combined constrained nonnegative matrix factorization (NMF) and improved spatial spectral preprocessing to perform the mixed pixel unmixing of Hyperspectral images (HSIs)
Summary
Hyperspectral imaging has a pivotal role in the field of remote sensing, which collects and processes information across the entire electromagnetic spectrum. Spectral unmixing technology is the most effective method to deal with mixed pixels. It can break for the limitation of spatial resolution of hyperspectral imaging spectrometers and express the real attributes of mixed pixels, improving the classification accuracy of hyperspectral images and applying them to the field of remote sensing. Take the urban dataset as an example to introduce the hyperspectral dataset, and there are 307 × 307 pixels, each of which corresponds to a 2 × 2 square meter area In this image, there are 210 wavelengths ranging from 400 nm to 2500 nm, resulting in a spectral resolution of 10 nm. It can be seen that the linear spectral mixing model is still a hot topic at home and abroad, and it is the most widely used model. e linear spectral mixing model has the advantages of simplicity, high efficiency, and clear physical meaning, which is scientific in theory, and the linear spectral mixing model can better describe the actual spectral mixing phenomenon for hyperspectral images with spatial resolution below the meter level
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