Classification of landsat TM image based on non negative matrix factorization

Non-negative matrix factorization (NMF) is one of the recently emerged dimensionality reduction methods. Unlike other methods, NMF is based on non-negative constraints, which allows learn parts from objects. In this paper a performance comparison of PCA and NMF, which are data preprocessing algorithms in remote sensing imagery classification, is presented. PCA and NMF are applied to a remote sensing imagery (128 times 128), obtained from Shunyi, Beijing. For classification, a maximum likelihood classification method is used for the preprocessed data. The results show that classification with NMF has more confident results than that with PCA. NMF keeps more abundant texture information.