Aircraft Segmentation Based On Deep Learning framework : from extreme points to remote sensing image segmentation

Remote sensing image segmentation is a very important technology. Although the segmentation method based on convolutional neural networks (CNNs) has achieved promising results in natural image test set, e.g. VOC or COCO, they provide inferior performance when being transferred to remote sensing images. Due to the limits of labeled remote sensing images, fine-tuning pre-trained CNNs using remote sensing images do not benefit the image segmentation performance. Inspired by the recent works of interactive segmentation methods which exploit several extreme clicks that are fed into CNNs to improve the accuracy of the segmentation, we propose an effective method to improve the segmentation accuracy, which uses four extreme points (the top, bottom, left, and right) as the guide information. In terms of mIoU, our method achieves 84.4% on remote sensing image dataset, which outperforms the previous work by 23.1%. Compared with the previous interactive segmentation methods, the proposed method achieves superior performance. In addition, an improved method with an extra point is proposed based on the inaccurate part of results obtained by four extreme points. It is very feasible to be applied in an interactive segmentation toolbox.