Remote Sensing Image Scene Classification Based on Object Relationship Reasoning CNN
Zhengzhou Li, Qingqing Wu, Bei Cheng, Lei Cao, Huihui Yang
Abstract
Remote sensing image has been widely used in many fields such as military reconnaissance and earthquake relief. However, the complexity and diversity of the scene make the target detection and recognition performance poor. Convolutional neural networks (CNNs) have made breakthrough in remote sensing image processing due to their ability to extract deep features. This letter proposes a remote sensing image scene recognition method based on object relationship reasoning CNN (ORRCNN), which makes use of the relationship between objects to infer the scene information. The method has prior scene-information-based channel and object-detection-based channel to classify the remote sensing image. The prior scene-information-based channel makes use of the feature space to identify the scene, and the object-detection-based channel adopts the relationship between the object and the scene to classify the scene. Afterward, the Bayesian criterion infers the scene more accurately by means of fusing the scene information from the above channels. The experimental results show that the proposed method is excellent especially in the scene where there are iconic objects in the remote image.