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Algorithm of regional land cover classification in PALSAR’s FBD data based on support vector machine
Hongfu Wang, Xiaorong Xue
This paper presents the assessment of ALOS PALSAR 50-morthorectified FBD data for regional land cover classification. A new tool using the recursive feature elimination SVM-based process and the textural Haralick’s parameters is introduced. The real contribution of textures within the land Cover classification can be understood. This paper presents the assessment of ALOS PALSAR orthorectified FBD data for regional land cover classification. It is shown that the SVM-RFE algorithm is effective for providing an optimized set of textural parameters to be computed at large scale. In addition to this, an original methodology has been implemented with the intention to give a real insight about the usefulness of textural parameters within the SVM based classification. The classification accuracy will likely increase if multitemporal PALSAR acquisitions are integrated
