Complex-Valued Self-Supervised PolSAR Image Classification Integrating Attention Mechanism
Zuzheng Kuang, Haixia Bi, Fan Li
Abstract
Polarimetric synthetic aperture radar (PolSAR) image classification is acknowledged as a critical task in remote sensing image processing, and the performance of this task has witnessed a substantial improvement owing to developing deep neural networks. However, existing approaches still suffer from at least one of the following limitations. First, their performance mainly depends on huge amounts of annotations. Secondly, the physical mechanism and characteristics of PolSAR data are not fully exploited in these methods. To tackle the label scarcity issue, we establish an attention mechanism-incorporated self-supervision framework for PolSAR image classification via designing a predictive auxiliary learning task. According to the properties of PolSAR data, we adapt the framework to complex-valued. Additionally, noise injection augmentation scheme considering the speckle noise distribution is designed to enhance the robustness of our model. Involving the complex-valued characteristics of the PolSAR data and noise into the architecture and loss function design makes it essentially distinctive from existing self-supervised methods.