基于密集连接的高分辨率遥感图像分类

doi:10.16088/j.issn.1001-6600.2021071503

Abstract

Abstract: High-resolution remote sensing image classification is a current research hotspot. The high-resolution remote sensing image classification model (Deeplab) based on deep convolutional networks and fully connected conditional random fields is widely used in this field because of its efficient and accurate classification performance. The Deeplab model has the problem of insufficient information utilization of high-resolution remote sensing images by hole convolution, which limits the further improvement of classification accuracy. In view of this, this paper proposes a new high-resolution remote sensing image classification model (Dspp). The Dspp model adopts a dense convolution network connection structure, and replaces Deeplab′s hollow convolution pyramid structure with a dense connection structure to improve information utilization and enhance the generalization ability of the model. Compared with the FCN model, the FCN-8S model and the Deeplab model, the overall accuracy of the Dspp model has improved by 16.8%, 11.7%, and 7.7%, which verifies the effectiveness of the model.

Key words: high resolution remote sensing image, classification model, hole convolution;dense connection structure, full connection conditional random field

CLC Number:

TP751

CHEN Zhiming, ZHANG Jiang, QIU Hanqing, DAI Yingcheng, WU Yuxin, LI Jianjun. High Resolution Remote Sensing Image Classification Based on Dense Connection[J].Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(3): 88-94.

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High Resolution Remote Sensing Image Classification Based on Dense Connection

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