Research on Semantic Segmentation of Natural Landform Based on Edge Detection Module

doi:10.16182/j.issn1004731x.joss.20-0756

Abstract

Abstract:

To classify pixels of natural landform edges in remote sensing images, this paper proposes a multi-channel fusion model and a decoder-side module model both integrating an edge detection module. The edge detection module takes the Canny operator as the base to perform closed operations and mean filtering, as a result of which accurate image edges can be achieved. Based on DeepLabV3+, the semantic segmentation network is connected with an edge planning module in parallel at encoder and decoder sides respectively. The experimental results show that the two improved networks can achieve a better segmentation effect on a high-resolution natural landform image data set compared with the original DeepLabV3+ network. Particularly, the network with fusion at the decoder side achieves the highest intersection over union (IoU) of 72.60% and F1score of 86.64%, which can be used for the recognition and segmentation of natural landforms.

Key words: semantic segmentation, deeplabv3+, edge detection, natural landform

CLC Number:

TP391

Qizong Shen, Chunyan Gao. Research on Semantic Segmentation of Natural Landform Based on Edge Detection Module[J]. Journal of System Simulation, 2022, 34(2): 293-302.

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方法	植被茂盛		沙漠戈壁
方法	mIoU	F1score	mIoU	F1score
FC-DenseNet56	62.88	81.35	62.95	79.49
GCN	67.75	81.23	67.37	80.67
SegNet	67.25	84.44	68.19	84.28
DeepLabV3	70.47	85.15	70.26	85.06
多通道输入	72.51	86.59	72.36	86.13
解码器输入	72.60	86.64	72.44	86.25

地貌类型	标签	RGB	识别精度
山地岩石	01	（128,128,0）	0.638
沙漠沙砾	02	（128,0,128）	0.657
平原草地	03	（0,128,0）	0.712
河流水面	04	（0,0,128）	0.614

地貌类型	标签	RGB	识别精度
山地岩石	01	（128,128,0）	0.640
沙漠沙砾	02	（128,0,128）	0.659
平原草地	03	（0,128,0）	0.715
河流水面	04	（0,0,128）	0.617

地貌类型	标签	RGB	识别精度
山地岩石	01	（128,128,0）	0.643
沙漠沙砾	02	（128,0,128）	0.661
平原草地	03	（0,128,0）	0.720
河流水面	04	（0,0,128）	0.619

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