Abstract
Hyperspectral unmixing (HU) is one of the most active hyperspectral image (HSI) processing research fields, which aims to identify the materials and their corresponding proportions in each HSI pixel. The extensions of the nonnegative matrix factorization (NMF) have been proved effective for HU, which usually uses the sparsity of abundances and the correlation between the pixels to alleviate the non-convex problem. However, the commonly used L 1 / 2 sparse constraint will introduce an additional local minima because of the non-convexity, and the correlation between the pixels is not fully utilized because of the separation of the spatial and structural information. To overcome these limitations, a novel bilateral filter regularized L 2 sparse NMF is proposed for HU. Firstly, the L 2 -norm is utilized in order to improve the sparsity of the abundance matrix. Secondly, a bilateral filter regularizer is adopted so as to explore both the spatial information and the manifold structure of the abundance maps. In addition, NeNMF is used to solve the object function in order to improve the convergence rate. The results of the simulated and real data experiments have demonstrated the advantage of the proposed method.
Highlights
Hyperspectral imaging has become an emerging technique in remote sensing and has been successfully used in many applications, such as military reconnaissance, mineral exploration, and environmental monitoring [1]
The linear mixing model (LMM) assumes that the endmembers do not interfere with each other, and each pixel in an hyperspectral image (HSI) can be linearly represented by the endmembers
We considered the abundance sum-to-one constraint (ASC) and the sparse constraint together, and proposed to use the L2-norm in order to enhance the sparsity of the abundances in the nonnegative matrix factorization (NMF) model
Summary
Hyperspectral imaging has become an emerging technique in remote sensing and has been successfully used in many applications, such as military reconnaissance, mineral exploration, and environmental monitoring [1]. HSIs often suffer from low spatial resolution, which means that one pixel may contain more than one material; in other words, there are mixed pixels. To address this problem, the spectral information can be utilized to identify the spectrum of each material (called endmember) in the mixed pixels and their corresponding percentages (called abundance); the whole procedure is called unmixing. We introduce the LMM and the NMF algorithm for subsequent discussion. The former is the basic model of the linear spectral unmixing, and the latter is one of the representative statistical methods for unmixing. The LMM assumes that the endmembers do not interfere with each other, and each pixel in an HSI can be linearly represented by the endmembers
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