Remote sensing image semantic segmentation supported by deep convolutional neural networks

doi:10.13474/j.cnki.11-2246.2020.0141

Abstract

Abstract: Aiming at the problem of category imbalance and insufficient utilization of context information in high-score remote sensing image semantic segmentation, this paper proposes an optimized DeeplabV3 + algorithm. Firstly, the data imbalance problem is solved by modifying the cross-entropy loss function. Secondly, it replace the ASPP module with Vortex Pooling to improve the context information. Then it use multi-scale input to make full use of the multi-scale information of the image. And then it use the voting strategy for feature fusion to improve the accuracy of image segmentation. Finally, morphology is used for post-processing to eliminate stitching marks and noise. Train on the CCF contest dataset and compare it with other classic semantic segmentation algorithms. The experimental results show that the algorithm in this paper makes full use of contextual information, effectively reduces misclassification, makes segmentation boundaries more accurate, and captures linear targets more effectively. The MIoU on the entire test image can reach 85.21%, which is significantly better than the SegNet and U-Net algorithms.

Key words: semantic segmentation, DeeplabV3+, high-resolution remote sensing image, Vortex Pooling, multi-scale information

CLC Number:

P237

XIE Meng, LIU Wei, LI Erzhu, YANG Mengyuan, WANG Xiaotan. Remote sensing image semantic segmentation supported by deep convolutional neural networks[J]. Bulletin of Surveying and Mapping, 2020, 0(5): 36-42.

References

[1] PANBOONYUEN T, JITKAJORNWANICH K, LAWAWI-ROJWONG S. Semantic segmentation on remotely sensed images using an enhanced global convolutional network with channel attention and domain specific transfer learning[J]. Remote Sensing, 2019, 11(1):83.
[2] ALSHEHHI R, MARPU P R, WOON W L, et al. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 130(8):139-149.
[3] MARMANIS D, SCHINDLER K, WEGNER J D, et al. Classification with an edge:improving semantic image segmentation with boundary detection[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 135(1):158-172.
[4] AUDEBERT N, LE SAUX B, LEFÈVRE S, et al. Beyond RGB:very high resolution urban remote sensing with multimodal deep networks[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 140(1):20-32.
[5] MARCOS D, VOLPI M, KELLENBERGER B, et al. Land cover mapping at very high resolution with rotation equivariant CNNs:towards small yet accurate models[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 145(2):96-107.
[6] MAGGIORI E, TARABALKA Y, CHARPIAT G, et al. High-resolution aerial image labeling with convolutional neural networks[J]. IEEE Transactions on Geoscience & Remote Sensing, 2017, 55(12):7092-7103.
[7] 张新明, 祝晓斌, 蔡强, 等. 图像语义分割深度学习模型综述[J]. 高技术通讯, 2017, 27(S1):808-815.
[8] HU F, XIA G S, HU J,et al. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery[J]. Remote Sensing, 2015, 7(11):14680-14707.
[9] CASTELLUCCIO M, POGGI G, SANSONE C, et al. Land use classification in remote sensing images by convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston:IEEE, 2015.
[10] BECK R A. Remote sensing and GIS as counterterrorism tools in the Afghanistan war:a case study of the Zhawar Kili region[J]. The Professional Geographer, 2003, 55(2):170-179.
[11] LONG J, SHELHAMER E, DARRELL T. Fully convolu-tional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 39(4):640-651.
[12] BADRINARAYANAN V, KENDALL A, Cipolla R. SegNet:a deep convolutional encoder-decoder architecture for scene segmentation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(12):2481-2495.
[13] RONNEBERGER O, FISCHER P, BROX T. U-Net:convolutional networks for biomedical image segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston:IEEE, 2015.
[14] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[J]. Computer Science, 2014(4):357-361.
[15] 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 & Machine Intelligence, 2018, 40(4):834-848.
[16] ZHAO H, SHI J, QI X, et al. Pyramid scene parsing network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii:IEEE, 2017.
[17] PENG C, ZHANG X, YU G, et al. Large kernel matters-improve semantic segmentation by global convolutional network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii:IEEE, 2017.
[18] CHEN L C, PAPANDREOU G, SCHROFF F, et al. Rethinking atrous convolution for semantic image segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii:IEEE, 2017.
[19] 吴止锾, 高永明, 李磊, 等. 类别非均衡遥感图像语义分割的全卷积网络方法[J]. 光学学报, 2019, 39(4):401-412.
[20] 陈天华, 郑司群, 于峻川. 采用改进DeepLab网络的遥感图像分割[J]. 测控技术, 2018, 37(11):34-39.
[21] 苏健民, 杨岚心, 景维鹏. 基于U-Net的高分辨率遥感图像语义分割方法[J]. 计算机工程与应用, 2019, 55(7):207-213.
[22] LIU Y, REN Q, GENG J, et al. Efficient patch-wise semantic segmentation for large-scale remote sensing images[J]. Sensors, 2018, 18(10):3232.
[23] CHEN L C, ZHU Y, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City:IEEE, 2018.
[24] CHOLLET F. Xception:deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii:IEEE, 2017.
[25] XIE C W, ZHOU H Y, WU J, et al. Vortex Pooling:improving context representation in semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City:IEEE, 2018.
[26] GARCIA-GARCIA A, ORTS-ESCOLANO S, OPREA S, et al. A review on deep learning techniques applied to semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii:IEEE, 2017.
[27] ZHANG H, DANA K, SHI J, et al. Context encoding for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City:IEEE, 2018.
[28] 刘懿兰, 黄晓霞, 李红旮, 等.基于卷积神经网络与条件随机场方法提取乡镇非正规固体废弃物[J]. 地球信息科学学报, 2019, 21(2):259-268.