Multibranch Fusion-Based Feature Enhance for Remote-Sensing Scene Classification

Remote-sensing (RS) scene classification is a fundamental and significant task in RS image interpretation, involving the annotation of semantic content. RS scene images are characterized by complex backgrounds, rich content, and multiscale targets, exhibiting both intraclass separation and interclass convergence. Therefore, extracting features that effectively express the intrinsic attributes of images and possess high discriminative is crucial for RS scene classification. Existing global-based methods often lack the ability to capture significant detailed information in similar scenes. Conversely, methods based on local discriminative features tend to overlook the interrelationships of objects within the same scene. To address these issues, this article proposes a unified framework named MBFNet to align and fuse features of different scales and levels for accurate RS scene classification. We utilize a multibranch feature-extracting network structure with parallel convolution and Transformer modules. Simultaneously, a kernel-selected multiscale aggregation (KSMSA) module is designed to efficiently process the diverse scale features emanating from these parallel branches. By selecting different convolution kernels, a dynamic receptive field is established to adaptively process features of different scales, reducing semantic differences to achieve effective aggregation of multiscale features. Moreover, a learnable multilevel aggregation (LMLA) module is designed to integrate shallow features, such as shape information, into deep features for more comprehensive feature fusion. Benefiting from KSMSA and LMLA, the proposed MBFNet improves the discriminability of features, thereby enhancing classification performance. Comprehensive experiments on three benchmark datasets demonstrate that the proposed method outperforms state-of-the-art RS scene classification methods in terms of performance.