Spatial Information Considered Network for Scene Classification

Remote sensing image (RSI) scene classification (RSISC) is a fundamental problem for understanding high-resolution RSIs. More recently, deep learning methods, especially convolutional neural networks (CNNs), and large data sets have greatly promoted the RSISC. However, deep learning methods rely heavily on the visual features extracted from the patches cropped from original RSIs, so the intraclass diversity and interclass similarity are two big challenges. To address these problems, in this letter, we propose a spatial information considered model to learn more discriminative features. By combining CNN and recurrent neural network, the proposed method can exploit both local and long-range spatial relation information to enhance the representational ability of the learned features. As the initial visual features of a single patch are transformed into higher-level features with spatial information, the proposed method achieves more accurate scene classification. Besides, we present an RSISC data set named as CSU-RSISC10 data set to preserve the spatial information between scenes in a new way of organization. Experiments demonstrate that the proposed method outperforms other three state-of-the-art methods in scene classification using CSU-RSISC10 data set.

Automated summary

This study introduces a model considering spatial information, which combines CNN and recurrent neural networks to learn more discriminative features, thus improving the accuracy of remote sensing image scene classification. Experimental results demonstrate that the proposed method outperforms other three state-of-the-art methods on the existing dataset.