Fully Convolutional Networks for Multi-Temporal SAR Image Classification

Classification of crop types from multi-temporal SAR data is a complex task because of the need to extract spatial and temporal features from images affected by speckle. Previous methods applied speckle filtering and then classification in two separate processing steps. This paper introduces fully convolutional networks (FCN) for pixel-wise classification of crops from multi-temporal SAR data. It applies speckle filtering and classification in a single framework. Furthermore, it also uses dilated kernels to increase the capability to learn long distance spatial dependencies. The proposed FCN was compared with patch-based convolutional neural network (CNN) and support vector machine (SVM) classifiers. The proposed method performed better when compared with the patch-based CNN and SVM.