Spatial Information Considered Network for Scene Classification

Remote sensing image (RSI) scene classification is a fundamental problem for understanding high-resolution RSIs. More recently, deep learning methods, especially convolutional neural networks (CNNs), and large datasets have greatly promoted the RSI scene classification. 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 paper, 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 RSI scene classification dataset named as CSU-RSISC10 dataset 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 dataset.