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http://dx.doi.org/10.7780/kjrs.2013.29.5.12

Feature Extraction and Classification of Multi-temporal SAR Data Using 3D Wavelet Transform  

Yoo, Hee Young (Geoinformatic Engineering Research Institute, Inha University)
Park, No-Wook (Department of Geoinformatic Engineering, Inha University)
Hong, Sukyoung (Soil and Fertilizer Management Division, National Academy of Agricultural Science, Rural Development Administration)
Lee, Kyungdo (Soil and Fertilizer Management Division, National Academy of Agricultural Science, Rural Development Administration)
Kim, Yihyun (Soil and Fertilizer Management Division, National Academy of Agricultural Science, Rural Development Administration)
Publication Information
Korean Journal of Remote Sensing / v.29, no.5, 2013 , pp. 569-579 More about this Journal
Abstract
In this study, land-cover classification was implemented using features extracted from multi-temporal SAR data through 3D wavelet transform and the applicability of the 3D wavelet transform as a feature extraction approach was evaluated. The feature extraction stage based on 3D wavelet transform was first carried out before the classification and the extracted features were used as input for land-cover classification. For a comparison purpose, original image data without the feature extraction stage and Principal Component Analysis (PCA) based features were also classified. Multi-temporal Radarsat-1 data acquired at Dangjin, Korea was used for this experiment and five land-cover classes including paddy fields, dry fields, forest, water, and built up areas were considered for classification. According to the discrimination capability analysis, the characteristics of dry field and forest were similar, so it was very difficult to distinguish these two classes. When using wavelet-based features, classification accuracy was generally improved except built-up class. Especially the improvement of accuracy for dry field and forest classes was achieved. This improvement may be attributed to the wavelet transform procedure decomposing multi-temporal data not only temporally but also spatially. This experiment result shows that 3D wavelet transform would be an effective tool for feature extraction from multi-temporal data although this procedure should be tested to other sensors or other areas through extensive experiments.
Keywords
feature extraction; 3D wavelet transform; PCA; land-cover classification; multi-temporal SAR; Radarsat-1;
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