ABSTRACT

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.

摘要

遥感场景分类（RSSC）是遥感理解和解释的基本任务之一。如何构造判别特征对于场景分类至关重要。通常认为，集成来自不同层的卷积特征可以显着提高场景分类性能。然而，一些现有方法直接将高层和低层特征连接在一起，而没有考虑特征冗余，语义歧义和背景噪声，从而导致次优性能。在本文中，基于注意力机制的启发，我们提出了一种简单但有效的策略，即在特征选择操作后融合不同的卷积特征，而不是直接进行级联。特征选择操作的基本思想是我们需要融合那些与高层语义一致的低层特征。特别地，所提出的策略是灵活的，并且可以被嵌入许多神经体系结构中。在两个基准数据集上的实验结果表明，与其他场景分类方法相比，该方法可以获取更多有价值的功能并获得竞争优势。