Vehicle Detection From High-Resolution Remote Sensing Imagery Using Convolutional Capsule Networks

Vehicle detection plays an important role in a variety of traffic-related applications. However, due to the scale and orientation variations and partial occlusions of vehicles, it is still challengeable to accurately detect vehicles from remote sensing images. This letter proposes a convolutional capsule network for detecting vehicles from high-resolution remote sensing images. First, a test image is segmented into superpixels to generate meaningful and nonredundant patches. Then, these patches are input to a convolutional capsule network to label them into vehicles or the background. Finally, nonmaximum suppression is adopted to eliminate repetitive detections. Quantitative evaluations on four test data sets show that average completeness, correctness, quality, and F-1-measure of 0.93, 0.97, 0.90, and 0.95, respectively, are obtained. Comparative studies with three existing methods confirm that the proposed method effectively performs in detecting vehicles of various conditions.