A Multi Moving Target Recognition Algorithm Based on Remote Sensing Video

The Earth observation remote sensing images can display ground activities and status intuitively, which plays an important role in civil and military fields. However, the information obtained from the research only from the perspective of images is limited, so in this paper we conduct research from the perspective of video. At present, the main problems faced when using a computer to identify remote sensing images are: They are difficult to build a fixed regular model of the target due to their weak moving regularity. Additionally, the number of pixels occupied by the target is not enough for accurate detection. However, the number of moving targets is large at the same time. In this case, the main targets cannot be recognized completely. This paper studies from the perspective of Gestalt vision, transforms the problem of moving target detection into the problem of salient region probability, and forms a Saliency map algorithm to extract moving targets. On this basis, a convolutional neural network with global information is constructed to identify and label the target. And the experimental results show that the algorithm can extract moving targets and realize moving target recognition under many complex conditions such as target's long-term stay and small-amplitude movement.