Cost-efficient information extraction from massive remote sensing data: When weakly supervised deep learning meets remote sensing big data

Abstract

With many platforms and sensors continuously observing the earth surface, the large amount of remote sensing data presents a big data challenge. While remote sensing data acquisition capability can fully meet the requirements of many application domains, there is still a need to further explore how to efficiently mine the useful information from remote sensing big data (RSBD). Many researchers in the remote sensing community have introduced deep learning in the process of RSBD, and deep learning-based methods have achieved better performance compared with traditional methods. However, there are still substantial obstacles to the application of deep learning in remote sensing. One of the major challenges is the generation of pixel-level labels with high quality for training samples, which is essential to deep learning models. Weakly supervised deep learning (WSDL) is a promising solution to address this problem as WSDL can utilize greedily labeled datasets that are easy to collect but not ideal to train the deep networks. In this review, we summarize the achievements of WSDL-driven cost-efficient information extraction from RSBD. We first analyze the opportunities and challenges of information extraction from RSBD. Based on the analysis of the theoretical foundations of WSDL in the computer vision (CV) domain, we conduct a survey on the WSDL-based information extraction methods under the data characteristic and task demand of RSBD in four different tasks: (i) scene classification, (ii) object detection, (iii) semantic segmentation and (iv) change detection. Finally, potential research directions are outlined to guide researchers to further exploit WSDL-based information extraction from RSBD.