MULTICLASS ORIENTED SHIP LOCALIZATION AND RECOGNITION IN HIGH RESOLUTION REMOTE SENSING IMAGES

Automatic inshore ship recognition, including target localization and type classification, is an important and challenging problem. However, arbitrarily rotated ships are always moored inshore densely. This makes it very difficult to locate ship targets. To resolve this problem, we proposes a multiclass oriented ship localization and recognition framework based on a cascade region convolutional neural network (R-CNN). First, Cascade R-CNN is adopted to localize and classify the positive regions a set of bounding boxes (BBox). Second, a novel procedure which transforms a bounding box to a rotated bounding box (B2RB) is designed and applied to each BBox to regress a rotated BBox (RBox) and non-maximum suppression (NMS) is adopted to remove redundant RBoxes. Extensive experimental results conducted on the dataset collected from Google Earth demonstrate the effectiveness of our proposed approach, compared to two other state-of-the-art approaches.