Object detection in remote sensing imagery based on convolutional neural networks with suitable scale features

doi:10.11947/j.AGCS.2019.20180393

Abstract

Abstract: Object detection and recognition in high spatial resolution remote sensing images (HSRI) is an important part of image information automatic extraction, analysis and understanding in high resolution earth observation system. The robustness and universality of traditional object detection and recognition algorithms using artificial design object feature are poor. To solve these problems, object detection and recognition in HSRI based on convolutional neural networks (CNN) with suitable scale features is proposed. Firstly, the suitable scale of the region of interest (ROI) of object is obtained by statistic the scale range of object in HSRI in the process of training and testing of CNN. Then, a CNN framework for object detection and recognition in HSRI is designed according to the suitable object ROI scale. The mean average precision (mAP) of the proposed CNN framework and Faster-RCNN is tested using the WHU-RSone data set. The experimental results show that the mAP of ZF model and VGG-16 model of the proposed CNN framework are 8.17% and 8.31% higher than that of Faster R-CNN ZF model and Faster R-CNN VGG-16 model, respectively. The proposed CNN framework can obtain good object detection and recognition results.

Key words: high resolution remote sensing image, object detection and recognition, deep learning, convolutional neural network, object scale

CLC Number:

P237

DONG Zhipeng, WANG Mi, LI Deren, WANG Yanli, ZHANG Zhiqi. Object detection in remote sensing imagery based on convolutional neural networks with suitable scale features[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(10): 1285-1295.

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