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Currently, high-resolution remote sensing image (HRI) interpretation is often done using deep learning methods. However, most deep learning networks currently used to classify HRIs rely on manually created skeletons, which are labour- and time-intensive and unable to completely identify the intrinsic features of the target image. To solve these problems, this study proposes a deep network skeleton search method based on a three-layer search space. A decoupled search approach was designed to optimize this three-layer search space. Our results demonstrated that the network architecture based on the decoupled search of the depth network skeleton outperforms the manually created depth network currently used for HRI classification. In particular, the mean Intersection over Union (mIoU), Accuracy (Acc) and Frequency Weighted Intersection over Union (FWIoU) values of the proposed method on the first test dataset were 0.902, 0.882 and 0.913, respectively, and those of the second test dataset were 0.887, 0.893 and 0.911, respectively. This method enables the development of autonomous designs of network skeletons, namely for HRI feature extraction and classification.

A decoupled search deep network framework for high-resolution remote sensing image classification