Abstract:
In this paper, we develop a CNN-GCN joint network (CGJNet) to learn global scene features and context information of high resolution remote sensing (HRRS) images. The pro...Show MoreMetadata
Abstract:
In this paper, we develop a CNN-GCN joint network (CGJNet) to learn global scene features and context information of high resolution remote sensing (HRRS) images. The proposed CGJNet method is composed of two streams, including CNN-stream (C-stream) and GCN-stream (G-stream). In the C-stream, a variation of the DenseNet-121 is developed to describe global visual information of HRRS images. In the G-stream, a GCN model is designed to reveal spatial structure by constructing adjacency graphs. As a result, accuracy of scene classification is effectively improved via integrating the two parts of crucial information. Experimental results on the AID data set demonstrate that the proposed CGJNet framework achieves remarkable classification results compared with many state-of-the-art (SOTA) methods, and the highest OA reaches 97.14%.
Date of Conference: 11-16 July 2021
Date Added to IEEE Xplore: 12 October 2021
ISBN Information: