Multisource Data Fusion for the Detection of Settlements Without Electricity

The international charity SolarAid aims to provide access to lights in areas without electricity, and it is a challenge to accurately and efficiently transmit the lights to the areas in need. Multisource, multitemporal, and multimodal remote sensing images can provide rich information about the target area, so using multisource remote sensing images for accurate detection of human settlements without electricity is a feasible solution. In this paper two separate detection tasks are formulated: building two attention SENet for settlements detection and light detection using the Sentinel-2 dataset and the Suomi Visible Infrared Imaging Radiometer Suite (VIIRS) night time dataset, respectively. In addition, we study a new outlier removal method based on the pixel distribution characteristics of the VIIRS dataset for data pre-processing, and propose a post-processing method based on region continuity for further correction of the results. Experiments show that our method can maximize the use of multisource data information and rank first in the detection of settlements without electricity challenge track (Track DSE) of the 2021 IEEE GRSS Data Fusion Contest.