PolSAR Image Classification Based on Low-Frequency and Contour Subbands-Driven Polarimetric SENet

• Main Authors: Rui Qin, Xiongjun Fu, Ping Lang
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Subjects: Convolutional neural network (CNN); nonsub- sampled laplacian pyramid (NSLP); polarimetric synthetic aperture radar (PolSAR) image classification; polarimetric feature; spatial domain; squeeze-and-excitation (SE) network
• Online Access: https://ieeexplore.ieee.org/document/9165101/

In order to more efficiently mine the features of PolSAR images and build a more suitable classification model that combines the features of the polarimetric domain and the spatial domain, this article proposes a PolSAR image classification method, called low-frequency and contour subbands-driven polarimetric squeeze-and-excitation network (LC-PSENet). First, the proposed LC-PSENet introduces the nonsubsampled Laplacian pyramid to decompose polarimetric feature maps, so as to construct a multichannel PolSAR image based on the low-frequency subband and contour subband of these maps. It guides the network to perform feature mining and selection in the subbands of each polarimetric map in a supervised way, automatically balancing the contributions of polarimetric features and their subbands and the influence of interference information such as noise, making the network learning more efficient. Second, the method introduces squeeze-and-excitation operation in the convolutional neural network (CNN) to perform channel modeling on the polarimetric feature subbands. It strengthens the learning of the contributions of local maps of the polarimetric features and subbands, thereby, effectively combining the features of the polarimetric domain and the spatial domain. Experiments on the datasets of Flevoland, The Netherlands, and Oberpfaffenhofen show that the proposed LC-PSENet achieves overall accuracies of 99.66%, 99.72%, and 95.89%, which are 0.87%, 0.27%, and 1.42% higher than the baseline CNN, respectively. The isolated points in the classification results are obviously reduced, and the distinction between boundary and nonboundary is more clear and delicate. Also, the method performs better than many current state-of-the-art methods in terms of classification accuracy.