SPIFFNet: A Statistical Prediction Interval-Guided Feature Fusion Network for SAR and Optical Image Classification

The problem of the feature extraction and fusion classification of optical and SAR data remains challenging due to the differences in optical and synthetic aperture radar (SAR) imaging mechanisms. To this end, a statistical prediction interval-guided feature fusion network, SPIFFNet, is proposed for optical and SAR image classification. It consists of two modules, the feature propagation module (FPM) and the feature fusion module (FFM). Specifically, FPM imposes restrictions on the scale factor of the batch normalization (BN) layer by means of statistical prediction interval, and features exceeding the scale factor of the interval are considered redundant and are replaced by features from other modalities to improve the classification accuracy and enhance the information interaction. In the feature fusion stage, we combine channel attention (CA), spatial attention (SA), and multiscale squeeze enhanced axial attention (MSEA) to propose FFM to improve and fuse cross-modal features in a multiscale cross-learning manner. To counteract category imbalance, we also implement a weighted cross-entropy loss function. Extensive experiments on three optical–SAR datasets show that SPIFFNet exhibits excellent performance.