On the Value of Terrain Classification Using SAR Altimeter Delay Doppler Image

Synthetic Aperture Radar (SAR) altimeter can provide highly accurate terrain data. In complex environments such as mountainous regions, terrain classification can improve data accuracy and reliability. However, classical methods rely on altimetry sequences, which may result in inaccuracies in environments with interference or when there are deviations in the prior Digital Elevation Model (DEM). This paper proposes a terrain classification method using SAR altimeter Delay Doppler Image (DDI). First, we introduce the SAR altimeter echo model, which can be used to generate DDIs. Then, typical terrain factors are selected for initial classification, and a Principal Component Analysis (PCA)-based model is constructed to select the main features, which are then used to choose samples. Finally, a Convolutional Neural Network (CNN) model is trained to achieve classification of DDIs. This process enables precise terrain classification. Experiments using simulation data verify that the proposed method can effectively classify DDIs. This approach has the potential for improving terrain assessment in various applications.