An Adaptive Multilayer Feature Fusion Strategy For Remote Sensing Scene Classification

Remote sensing scene classification (RSSC) is one of the fundamental and challenging tasks for remote-sensing understanding and interpretation. How to construct discriminative features is crucial for scene classification. It is generally acknowledged that integrating convolutional features from different layers can significantly improve scene classification performance. However, some existing methods directly concatenate the high-layer and low-layer features without considering feature redundancy, semantic ambiguity and background noise, resulting in sub-optimal performance. In this paper, inspired by the attention mechanism, we propose a simple but effective strategy to fuse different convolutional features after feature selection operation, instead of directing concatenation. The basic idea of the feature selection operation is that we need to fuse those low-layer features, which are consistent with high-layer semantics. In particular, the proposed strategy is flexible and can be embedded into many neural architectures. Experimental results on two benchmark datasets demonstrate that the proposed method can obtain more valuable features and achieve competitive performance than other scene classification approaches.