Learning Transferable Deep Models for Land-Use Classification with High-Resolution Remote Sensing Images

In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-use mapping. However, due to the complex information brought by the increased spatial resolution and the data disturbances caused by different conditions of image acquisition, it is often difficult to find an efficient method for achieving accurate land-use classification with heterogeneous and high-resolution remote sensing images. In this paper, we propose a scheme to learn transferable deep models for land-use classification with HRRS images. The main idea is to rely on deep neural networks for presenting the semantic information contained in different types of land-uses and propose a pseudo-labeling and sample selection scheme for improving the transferability of deep models. More precisely, a deep Convolutional Neural Networks (CNNs) is first pre-trained with a well-annotated land-use dataset, referred to as the source data. Then, given a target image with no labels, the pre-trained CNN model is utilized to classify the image in a patch-wise manner. The patches with high classification probability are assigned with pseudo-labels and employed as the queries to retrieve related samples from the source data. The pseudo-labels confirmed with the retrieved results are regarded as supervised information for fine-tuning the pre-trained deep model. In order to obtain a pixel-wise land-use classification with the target image, we rely on the fine-tuned CNN and develop a hybrid classification by combining patch-wise classification and hierarchical segmentation. In addition, we create a large-scale land-use dataset containing $150$ Gaofen-2 satellite images for CNN pre-training. Experiments on multi-source HRRS images, including Gaofen-2, Gaofen-1, Jilin-1, Ziyuan-3, and Google Earth images, show encouraging results and demonstrate the efficiency of the proposed scheme.