Classification of ground objects from remote sensing image with close spectral curves based on modified density mixture model

Xinyu Zhou; Ye Zhang

doi:10.1117/12.2528079

6 September 2019 Classification of ground objects from remote sensing image with close spectral curves based on modified density mixture model

Xinyu Zhou, Ye Zhang

Author Affiliations +

Proceedings Volume 11137, Applications of Digital Image Processing XLII; 111370B (2019) https://doi.org/10.1117/12.2528079
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

Abstract

As the sharply development of remote sensing technology, spatial resolution and spectral resolution become much higher in hyperspectral images than before. Commonly, spectral differences often be used in distinguish objects that are difficult to be classified, especially, which share the same color or texture. However, the spectral features are not as unique as we think. In many cases, spectral curves of same materials may be different and, on the contrary, that of different materials may be same. In this condition, false alarm and missing alarm probability are high. To solve this problem, a modified density mixture model is provided. Firstly, each band of data is whitened to remove the correlation between the pixels in order to reduce redundancy. Secondly, the whitened result will be handled by the weighted multivariate normal distribution model. Then, several pixels of each kind of objects are taken to build an spectral library. Finally, Spectral Angle Mapping (SAP) is applied to classification by matching with spectral library. The result demonstrates that objects are classified precisely with low false alarm and missing alarm probability, for the spectral difference of the same kind of objects decreases, and that of different kinds of objects increases compared with the data before processing.

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Xinyu Zhou and Ye Zhang "Classification of ground objects from remote sensing image with close spectral curves based on modified density mixture model", Proc. SPIE 11137, Applications of Digital Image Processing XLII, 111370B (6 September 2019); https://doi.org/10.1117/12.2528079

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