A Multi-Model Fusion of Convolution Neural Network and Random Forest for Detecting Settlements Without Electricity

In this paper, a multi-model fusion framework is proposed for automatic detection of settlements without electricity (DSE) based on the multimodal and multitemporal remote sensing data. To settle the problems of data noise and data redundancy, the data preprocessing step, which consists of band selection, cloud removal, grayscale stretch and data augmentation, is firstly applied. Two models of single-task and dual-task are further constructed for DSE. The single-task model builds a global context convolutional neural network (GC-CNN) for the detection of settlements without electricity and the dual-task model employs the GC-CNN for settlement detection and the random forest classifier for electricity detection. Moreover, a model fusion principle and a post-processing method is designed to integrate and improve the results above, thus producing the final segmentation result. Verified through the competition website, the proposed method achieved a F1-score of 0.8806, ranking second in the first track of 2021 IEEE GRSS Data Fusion Contest.