Spatial Information Considered Network for Scene Classification

doi:10.1109/LGRS.2020.2992929

Spatial Information Considered Network for Scene Classification

2021-06-01

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DOI：10.1109/LGRS.2020.2992929

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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.

Keywords:

Feature extraction

Visualization

Convolution

Remote sensing

Machine learning

Image resolution

Recurrent neural networks

Convolutional neural networks (CNNs)

large-size data set

recurrent neural network (RNN)

scene classification

scene spatial relationship

spatial information considered network (SIC-Net)

Spatial Information Considered Network for Scene Classification

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