Image segmentation and density clustering for moving object patches extraction in remote sensing image

Recently, moving object detection in large-scale remote sensing images achieves outstanding performance by fully convolutional neural network. However, handling such data is very time-consuming because the search space is extremely large. This paper proposes a specific improved method from the point of candidate region proposals. First, irregular candidate areas are roughly extracted by neighborhood differencing and local errors handling. Then a spatial-constraint based density cluster algorithm (SC-DBSCAN) is proposed to merge adjacent areas into patches as CNN input, which aims to reduce final outputs' amount and area. Through the priori of space constraints, this algorithm can adaptively divide data into multi types of clusters, and choose different merging strategies according to the complexity of clusters. For complicated clusters, the outputs are related to traverse sequence of each object, and thus a random search strategy based on simulated annealing is applied to avoid local optima and improve the patches' quality. Finally, by reducing the times of model inferences and avoiding redundant object detections, the detection efficiency of proposed method is significantly improved.