Scalable multi-class geospatial object detection in high-spatial-resolution remote sensing images

In this paper we present a conceptually simple but surprisingly effective multi-class geospatial object detection method based on Collection of Part Detectors (COPD), which can be easily scaled to a larger number of object classes. The presented COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier trained using a weakly supervised learning method that only requires image labels indicating the presence of objects for the training data. Here, each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a feasible solution for rotation-invariant and simultaneous detection of multi-class geospatial objects. Comprehensive evaluations on high-spatial-resolution remote sensing images and comparisons with a number of state-of-the-art approaches demonstrate the effectiveness and superiority of the presented method.