Remote Aircraft Target Recognition Method Based on Superpixel Segmentation and Image Reconstruction

Satellite images are always with complex background and shadow areas. These factors can lead to target segmentation break up and recognition with a low accuracy. Aiming at solving these problems, we proposed an aircraft recognition method based on superpixel segmentation and reconstruction. First, we need to estimate the orientation of an aircraft by using histograms of oriented gradients. And then, an improved Simple Linear Iterative Cluster (SLIC) superpixel segmentation algorithm is provided. By comparing texture feature instead of color feature space, we cluster the pixels that are with the same features. Last, through target template images and orientation, we reconstruct the superpixels. Also, the lowest error matching ratio is the recognized target. The test results show that the algorithm is robust to noise and recognize more aircrafts. Especially, when the satellite images with complex background and shadow areas, our method recognizes accuracy better than other methods. It can satisfy the demand of satellite image aircraft recognition.