Feature Dimension Reduction Using Stacked Sparse Auto Encoders for Crop Classification with Multi Temporal, Quad Pol SAR Data
Crop classification in agriculture is one of important applications for polarimetric synthetic aperture radar (PolSAR) data. For agricultural crop discrimination, compared with single temporal data, multi temporal data can dramatically increase crop classification accuracies since the same crop shows different external phenomena as it grows up. In practice, the utilization of multi temporal data encounters a serious problem known as a "dimension disaster" Aiming to solve this problem and raise the classification accuracy, this study developed a feature dimension reduction method using stacked sparse auto encoders (S SAEs) for crop classification. First, various incoherent scattering decomposition algorithms were employed to extract a variety of detailed and quantitative parameters from multi temporal PolSAR data. Second, based on analyzing the configuration and main parameters for constructing an S SAE, a three hidden layer S SAE network was built to reduce the dimensionality and extract effective features to manage the "dimension disaster" caused by excessive scattering parameters, especially for multi temporal, quad pol SAR images. Third, a convolutional neural network (CNN) was constructed and employed to further enhance the crop classification performance. Finally, the performances of the proposed strategy were assessed with the simulated multi temporal Sentinel 1 data for two experimental sites established by the European Space Agency (ESA) The experimental results showed that the overall accuracy with the proposed method was raised by at least 17% compared with the long short term memory (LSTM) method in the case of a 1% training ratio. Meanwhile, for a CNN classifier, the overall accuracy was almost 4% higher than those of the principle component analysis (PCA) and locally linear embedded (LLE) methods. The comparison studies clearly demonstrated the advantage of the proposed multi temporal crop classification methodology in terms of classification accuracy, even with small training ratios.
Remote. Sens.