應用NMF方法分析多頻譜遙測影像

Unsupervised Classification of Remote Sensing Imagery With Non-negative Matrix Factorization

歐陽廣德(Kuang-De, Ou Yang)

指導教授 ： 劉長遠

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2005年)

https://doi.org/10.6342/NTU.2005.00111

摘要

無資料

關鍵字

非監督式分類 ； 遙測影像 ； 非負矩陣分解

並列摘要

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.

並列關鍵字

Unsupervised Classification ； Remote Sensing ； Non-negative Matrix Factorization ； Fuzzy C Means ； Partitioned Noise Adjusted Principle Component Analysis

參考文獻

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