Pixel-based airplanes segmentation in remote sensing image
In this paper, we present a novel pixel-based airplane segmentation method from remote sensing images by combining Single Shot MultiBox Detector (SSD) and Single-layer Cellular Automata (SCA). SSD is a kind of deep ConvNet for object detection while SCA is a saliency detection method via Cellular Automata. First, we obtain detection result where every airplane is boxed by a rectangle through the SSD model. The last two conventional layers in original SSD are removed in order to fit the small objects of remote sensing (RS) image. Then the result is processed via single-layer Cellular Automata to achieve pixel-based segmentation. The experiments demonstrate that our approach is efficient and works well for automatic airplane segmentation in RS image.