Unsupervised Classification of Remote Sensing Imagery With Non-negative Matrix Factorization
Date Issued
2005
Date
2005
Author(s)
Yang, Kuang-De, Ou
DOI
en-US
Abstract
An unsupervised classification method provides the interpretation, feature extraction and
endmember estimation for the remote sensing image data without any prior knowledge
about the ground quality. We explore such method and construct an algorithm based on the
non-negative matrix factorization (NMF). The use of the NMF is to match the non-negative
property in sensing spectrum data.. The data dimensionality is estimated by using the partitioned
noise-adjusted principlal component analysis (PNAPCA). The initial matrix used
to start the NMF is obtained by using the fuzzy c-mean (FCM). This algorithm is capable
to produce a region- or part-based representation of objects in images. Both simulated and
real sensing data are used to test the algorithm.
Subjects
非監督式分類
遙測影像
非負矩陣分解
Unsupervised Classification
Remote Sensing
Non-negative Matrix Factorization
Fuzzy C Means
Partitioned Noise Adjusted Principle Component Analysis
Type
thesis
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