The Recognition Framework of Deep Kernel Learning for Enclosed Remote Sensing Objects

Abstract

Remote sensing image target recognition is used in various fields, such as ships, tanks, airplanes, and vehicles, which are closed targets. The features of these targets include target outlines that are obvious and target discriminant features that are significantly different from the surrounding environment, and the targets are characterized as small and dense. Therefore, the recognition of these types of targets is a popular topic. We proposed a recognition framework consisting of a remote sensing image target recognition method based on deep saliency kernel learning analysis, which uses a target region extraction method based on the visual saliency mechanism and implements a nonlinear deep kernel learning saliency feature analysis method to realize target extraction and recognition. Experimental results show that a 95.9% recognition rate is achieved for SAR remote sensing target recognition on the public MSTAR data set, a 96% recognition rate on the UC Merced Land Use data set, and an 85% recognition rate on a self-built visible light remote sensing image data set. The recognition framework can be used for video recognition.