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Fusion of spectral, spatial, and temporal information is an effective method used in many satellite remote sensing applications. On the other hand, one drawback of this fusion is an increase in complexity. In this paper, we focus on developing a fast and well-performed classification method for agricultural crops using time-series SAR data. In order to achieve this, a novel two-stage approach is proposed. In the first stage, a high-dimensional feature space is obtained using time-series dual-pol SAR data and morphological operators. Spectral, spatial, and temporal information is combined into a single high-dimensional feature space. In the second stage, a dimension reduction technique is applied to the feature vector in order to decrease time complexity and increase classification accuracy by considering the global and local pattern information in the high-dimensional feature space. The contribution of the morphological profiles to the classification performance is significant; however the time complexity is increased drastically. The proposed method overcomes the time complexity stemming from high-dimensional feature space; it also improves the classification performance. The superiority of the proposed method to the comparative methods in agricultural crop classification is experimentally shown with the improvements in both classification and time performance using time-series TerraSAR-X images.

SAR image time-series analysis framework using morphological operators and global and local information-based linear discriminant analysis