Multilevel Capsule Weighted Aggregation Network Based on a Decoupled Dynamic Filter for Remote Sensing Scene Classification

Abstract

The improvement of remote sensing scene classification(RSSC) by effectively extracting discriminant representations for complex and diverse scenes remains a challenging task. The capsule network(CapsNet) can encode the spatial relationship of features in an image, which exhibits encouraging performance. Nevertheless, the original CapsNet is unsuitable for RSSC with complex image background. In addition, conventional neural network methods only use the last features from their last convolutional layer and discount the intermediate features with complementary information. To search the additional information in intermediate convolutional layers and increase the performance of feature aggregation, this paper proposes a multilevel capsule weighted aggregation network (MCWANet) based on a decoupled dynamic filter(DDF), in which a new multilevel capsule encoding module and a new capsule sorting pooling (CSPool) method are implemented by combining the advantageous attributes of a residual DDF block, weighted capsule aggregation, and the new CSPool method. Extensive experiments on two challenging datasets, AID and NWPU-RESISC45, demonstrate that multilevel and multiscale features can be extracted and fused to extract semantically strong feature representation and that the proposed MCWANet performs competitively in RSSC.

Keywords

• Scene classification
• decoupled dynamic filter
• weighted aggregation
• capsule network