Identification of Tailing Ponds From Multi-Source and High-Resolution Remote Sensing Imageries Using Deep Learning-Based Method

Monitoring the spatial distribution of tailing ponds is of great significance for environmental governance in mining areas. Remote sensing technology is an effective tool to quickly obtain the location information of tailing ponds in large areas. However, most current studies about tailing pond detection focus on using images from single sensor and are with relatively low detection accuracy in complex backgrounds. In this paper, we present a framework to annotate the location of Tailing Ponds (TPs) from multi-source and high-resolution remote sensing images. In the proposed framework, an improved You Only Look Once (YOLO) deep learning based object detection model is employed, which embeds a high-density feature aggregation module, a low-parameter feature aggregation module and a refined loss function. The introductions of two new modules can make the network better capture global information about the object and accelerate the detection speed, while the introduction of a refined loss function can make the target box regression process more robust and avoiding divergence during training. To train reliable and robust YOLO based TP detection model, sufficient training samples in different backgrounds were collected, and some different sample augmentation methods were also employed. Experimental results validate that, the proposed YOLO based TP annotation method can accurately identify the targets with high annotation accuracies in complex backgrounds.