Multi-temporal full polarimetry L-band SAR data classification for agriculture land cover mapping

This paper presents a multi-step framework for classification and crop mapping using several polarimetric features, extracted from multitemopral Synthetic Aperture Radar (SAR) imagery. The multi-temporal data classification, not only improves the overall retrieval accuracy, but also provides more reliable crop discrimination in comparison to single-date data [1]. This is mainly because various phenogical stages of crops can contribute discrimination and classification of agricultural lands. The proposed framework in this paper consists of three main steps: a) data preprocessing, b) processing, and c) classification and evaluation. Several polarimetic features are extracted from preprocessed data, including the coherency and/or the covariance matrixes. Polarimetry decompositions then can allpy to ectract the statistical or physical based polarimetric components. Support vector machines´ (SVM) classifier is employed for classification of these features. In addition, different kinds of kernel functions are used to evaluate the performance of SVM for classification. The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Wennipeg, Manitoba, Canada. in summer of 2012. The experimental tests show that using two data data increases the overall accuracy of the classification up to 14%, and using an aditional date, i.e. three multitemporal datasets, increases the overall accuracy about 9% in comparing to two date imagery. The effect of multi-temporal data in crop classification is much more than even using more training data, which sometimes is expensive and time consuming.