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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References 15

1	Badrinarayanan V, Kendall A, Cipolla R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence(S0162-8828), 2017, 39(12) 2481-2495.
2	Lin G, Milan A, Shen C, et al. RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation[C]// IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2017:5168-5177.
3	Liu Z, Li X, Ping L, et al. Deep Learning Markov Random Field for Semantic Segmentation[J]. IEEE Trans Pattern Anal Mach Intell(S0162-8828), 2018: 1814-1828.
4	陈聪. 基于深层网络的城市街道场景的语义分割的方法研究[D]. 西安: 西安电子科技大学, 2019.
	Chen Cong.Reserch on Semantic Segmentation of Cityscape Based on Deep Neural Network[D].Xi′an Xidian University, 2019.
5	Long J, Shelhamer E, Darrell T. Fully Convolutional Networks for Semantic Segmentation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence(S0162-8828), 2014, 39(4): 640-651.
6	林志洁, 罗壮, 赵磊, 等.特征金字塔多尺度全卷积目标检测算法[J].浙江大学学报(工学版), 2019, 53(3): 533-540.
	Lin Zhijie, Luo Zhuang, Zhao Lei, et al. Multi-scale Full Convolution Target Detection Algorithm for Feature Pyramid [J]. Journal of Zhejiang University (Engineering Edition), 2019, 53(3): 533-540.
7	Wu Dihua,Yin Xuqiang,Jiang Bo,et al. Detection of the Respiratory Rate of Standing Cows by Combining the Deeplab V3+ Semantic Segmentation Model with the Phase-based Video Magnification algorithm[J]. Biosystems Engineering(S1615-7591), 2020: 72-89.
8	Chen L C, Papandreou G, Kokkinos I, et al. Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected Crfs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence(S0162-8828), 2018, 40(4): 834-848.
9	Tang Wei,Zou Dongsheng,Yang Su,et al. A Two-stage Approach for Automatic Liver Segmentation with Faster R-CNN and DeepLab[J]. Neural Computing and Applications(S0941-0643), 2020, 32(11): 6769-6778.
10	Wu Dihua,Yin Xuqiang,Jiang Bo,et al. Detection of the Respiratory Rate of Standing Cows by Combining the Deeplab V3+ Semantic Segmentation Model with the Phase-based Video Magnification Algorithm[J]. Biosystems Engineering(S1537-5110), 2020: 72-89.
11	Zhao H, Shi J, Qi X, et al. Pyramid Scene Parsing Network[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2881-2890.
12	Lin G, Milan A, Shen C, et al. Refinenet: Multi-path Refinement Networks for High-resolution Semantic Segmentation[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1925-1934.
13	Xu Wang,Chen Renwen,Huang Bin,et al. Single Image Super-Resolution Based on Global Dense Feature Fusion Convolutional Network[J]. Sensors(S1424-8220), 2019, 19(2): 316-335.
14	Liu Z, Li X, Ping L, et al. Deep Learning Markov Random Field for Semantic Segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence(S0162-8828), 2018, 40(8): 1418-1428.
15	Zhang Xiao, Chen Fuen. Lane Line Edge Detection Based on Improved Adaptive Canny Algorithm[J]. Journal of Physics: Conference Series(S1742-6588), 2020, 1549(2): 1362-1375.

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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