Knowledge Distillation Based on Adaptive Learning and Channel Amplification Features for PolSAR Image Classification

The models currently used for polarimetric synthetic aperture radar (PolSAR) image classification tasks have problems, such as complex network structures, poor distinction of detailed features, and fixed loss weights during the training process. In response to these problems, this article proposes a PolSAR image classification method based on knowledge distillation using adaptive learning and channel amplification features. First, this article builds a knowledge distillation framework for PolSAR. Using a teacher network trained in advance that can acquire global knowledge to guide the student. This framework reduces the computational complexity and improves the classification accuracy of the student. Then, an adaptive loss weight learning mechanism is designed, which sets the weight of the Kullback-Leibler (KL) divergence loss during training into a learnable mode. The weight can be automatically adjusted according to the actual training situation of the student. Finally, a scheme for channel amplification to enhance features is proposed. This scheme obtains channel weights based on the student’s feature map information. These weights are amplified, strengthening the network’s ability to obtain feature information. Compared with the five PolSAR image classification algorithms, the method proposed in this article uses lower computational complexity to obtain higher classification accuracy on the Flevoland, San Francisco, and Xi’an datasets.