Image retrieval from remote sensing big data: A survey

The blooming proliferation of aeronautics and astronautics platforms, together with the ever-increasing remote sensing imaging sensors on these platforms, has led to the formation of rapidly-growing earth observation data with the characteristics of large volume, large variety, large velocity, large veracity and large value, which raises awareness about the importance of large-scale image processing, fusion and mining. Unconsciously, we have entered an era of big earth data, also called remote sensing (RS) big data. Although RS big data provides great opportunities for a broad range of applications such as disaster rescue, global security, and so forth, it inevitably poses many additional processing challenges. As one of the most fundamental and important tasks in RS big data mining, image retrieval (i.e., image information mining) from RS big data has attracted continuous research interests in the last several decades. This paper mainly works for systematically reviewing the emerging achievements for image retrieval from RS big data. And then this paper further discusses the RS image retrieval based applications including fusion-oriented RS image processing, geo-localization and disaster rescue. To facilitate the quantitative evaluation of the RS image retrieval technique, this paper gives a list of publicly open datasets and evaluation metrics, and briefly recalls the mainstream methods on two representative benchmarks of RS image retrieval. Considering the latest advances from multiple domains including computer vision, machine learning and knowledge engineering, this paper points out some promising research directions towards RS big data mining. From this survey, engineers from industry may find skills to improve their RS image retrieval systems and researchers from academia may find ideas to conduct some innovative work.

Automated summary

The paper discusses the importance of image retrieval in RS big data, as well as related applications and challenges, and provides publicly open datasets, evaluation metrics, and mainstream methods. The authors also point out future research directions for RS big data mining.