PLANET MINERAL DISTRIBUTION DETECTION VIA CLUSTERING-AWARE NONNEGATIVE MATRIX FACTORIZATION

Spectral unmixing is an important technique to exploit mineral distribution through remote sensing image. In this paper, we propose an unmixing algorithm combining clustering-aware method with the sparsity-constrained nonnegative matrix factorization (SNMF) algorithm. Pixels with similar spectra have high possibility to share similar typical endmembers, therefore we preprocess the image using K-means cluster algorithm and then optimizes the initial endmember spectra by selecting the typical ones of each cluster as the initial endmember value. Due to the local convergence feature of NMF, the optimal initial value can accelerate the convergence of the algorithm and obtain more accurate results. Meanwhile, we use the sparsity-constrained NMF in global unmixing to control the sparse property of abundance distribution. The experiments on synthetic data and Chang'e-1 hyperspectral data show that K-means nonnegative matrix factorization (KNMF) is superior to the other unmixing methods.