DIAMONDNET: SHIP DETECTION IN REMOTE SENSING IMAGES BY EXTRACTING AND CLUSTERING KEYPOINTS IN A DIAMOND

Abstract

Ship detection plays an important role in military and civil fields. Despite it has been studied for decays, ship detection in remote sensing images is still a challenging topic. In this work, we come up with a novel ship detection framework based on the keypoint extraction technique. We use a convolutional neural network to detect ship keypoints and then cluster the keypoints into groups, where each group is composed of keypoints belonging to the same ship. The choice of the keypoints is specifically considered to derive an effective ship representation. One keypoint is located at the center of the ship and the rest four keypoints are located at the head, the tail, the midpoint of the left side and the midpoint of the right side, respectively. Since these keypoints are distributed in a diamond, we name our network DiamondNet. In addition, a corresponding clustering algorithm based on the geometric characteristics of the ships is proposed to cluster keypoints into groups. We demonstrate that our method provides a more flexible and effective way to represent ships than the popular anchor-based methods, since either the rectangular bounding box or the rotated bounding box of each ship instance can be easily derived from the ship keypoints. Experiments on two datasets reveal that our DiamondNet reaches the state-of-the-art results.