An Aircraft Object Detection Algorithm Based on Small Samples in Optical Remote Sensing Image

Abstract

In recent years, remote sensing technology has developed rapidly, and the ground resolution of spaceborne optical remote sensing images has reached the sub-meter range, providing a new technical means for aircraft object detection. Research on aircraft object detection based on optical remote sensing images is of great significance for military object detection and recognition. However, spaceborne optical remote sensing images are difficult to obtain and costly. Therefore, this paper proposes the aircraft detection algorithm, itcan detect aircraft objects with small samples. Firstly, this paper establishes an aircraft object dataset containing weak and small aircraft objects. Secondly, the detection algorithm has been proposed to detect weak and small aircraft objects. Thirdly, the aircraft detection algorithm has been proposed to detect multiple aircraft objects of varying sizes. There are 13,324 aircraft in the test set. According to the method proposed in this paper, the f1 score can achieve 90.44%. Therefore, the aircraft objects can be detected simply and efficiently by using the method proposed. It can effectively detect aircraft objects and improve early warning capabilities.

Keywords

• aircraft object detection
• support vector machines
• clustering
• small samples learning