改进的DeeplabV3Plus高分辨率遥感影像土地覆盖分类

doi:10.3778/j.issn.1002-8331.2309-0228

摘要/Abstract

摘要： 高分辨率遥感图像中提取的土地覆盖信息在城市规划建设和土地利用等领域具有巨大的价值，但由于土地覆盖类型复杂、光谱差异性较小等因素，目前对土地覆盖类别进行高质量的语义分割仍然受到一定限制。因此，针对该问题提出了一种新颖的全连接网络MFC-Net，该模型采用全新的基于点积注意力的空洞空间金字塔池化模块（DPA-ASPP），提高了聚合上下文信息方面的能力及效率。更进一步的，针对不同尺度的特征提出了注意力增强融合模块（AEFM）来增强特征表示，改善不同形状和大小地物的分割效果。该模型充分利用了高分辨率遥感影像中丰富的上下文信息及多尺度信息，在LoveDA大型遥感图像数据集上取得了优于当前主流模型的分割结果（84.26%MPA和69.67%MIOU）。

关键词: 遥感图像, 深度学习, 语义分割, 土地覆盖分类, LoveDA数据集

Abstract: Land cover information extracted from high-resolution remote sensing images is of great value in the fields of urban planning and construction and land use, etc. However, high-quality semantic segmentation of land cover categories is still restricted at present due to the complicated land cover types and small spectral variability. Therefore, a novel fully-connected network MFC-Net is proposed in this paper to address this challenge, and the model adopts a novel dot-product attention-atrous spatial pyramid pooling (DPA-ASPP), which improves the capability and efficiency in terms of aggregating contextual information. Furthermore, the attention enhanced fusion module (AEFM) is proposed for different scales of features to enhance the feature representation and improve the segmentation of features of different shapes and sizes. The model makes full use of the rich contextual and multi-scale information in high-resolution remote sensing images, and achieves segmentation results (84.26% MPA and 69.67% MIOU) better than the current mainstream models on LoveDA large remote sensing image dataset.

Key words: remote sensing imagery, deep learning, semantic segmentation, land cover classification, LoveDA dataset

朱凡, 罗小波. 改进的DeeplabV3Plus高分辨率遥感影像土地覆盖分类[J]. 计算机工程与应用, 2024, 60(13): 266-275.

ZHU Fan, LUO Xiaobo. Improved Land Cover Classification of DeeplabV3Plus High-Resolution Remote Sensing Imagery[J]. Computer Engineering and Applications, 2024, 60(13): 266-275.

参考文献

[1] DASTOUR H, HASSAN Q K J S. A comparison of deep transfer learning methods for land use and land cover classification[J]. Sustainability, 2023, 15(10): 7854.
[2] VAN LEEUWEN B, TOBAK Z, KOVáCS F J J O E G. Machine learning techniques for land use/land cover classification of medium resolution optical satellite imagery focusing on temporary inundated areas[J]. Journal of Environmental Geography, 2020, 13(1/2): 43-52.
[3] WANG J, BRETZ M, DEWAN M A A, et al. Machine learning in modelling land-use and land cover-change (LULCC): current status, challenges and prospects[J]. Science of The Total Environment, 2022, 822: 153559.
[4] NAUSHAD R, KAUR T, GHADERPOUR E. Deep transfer learning for land use and land cover classification: a comparative study[J]. Sensors, 2021, 21(23): 13.
[5] HE T D, WANG S X. Multi-spectral remote sensing land-cover classification based on deep learning methods[J]. J Supercomput, 2021, 77(3): 2829-2843.
[6] JAMALI A J S A S. Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: a case study of the northern region of Iran[J]. SN Applied Sciences, 2019, 1(11): 1448.
[7] SARGENT I, ZHANG C, ATKINSON P M. Joint deep learning for land cover and land use classification[J]. Remote Sensing of Environment, 2019, 221: 173-187.
[8] KROUPI E, KESA M, NAVARRO-SANCHEZ V D, et al. Deep convolutional neural networks for land-cover classification with Sentinel-2 images[J]. J Appl Remote Sens, 2019, 13(2): 1-22.
[9] PAPOUTSIS I, BOUNTOS N I, ZAVRAS A, et al. Benchmarking and scaling of deep learning models for land cover image classification[J]. ISPRS-J Photogramm Remote Sens, 2023, 195: 250-268.
[10] MANZANAREZ S, MANIAN V, SANTOS M. Land use land cover labeling of GLOBE images using a deep learning fusion model[J]. Sensors, 2022, 22(18): 6895.
[11] CHEN B, XIA M, HUANG J. MFANet: a multi-level feature aggregation network for semantic segmentation of land cover[J]. Remote Sens, 2021, 13(4): 731.
[12] TONG X Y, XIA G S, LU Q K, et al. Land-cover classification with high-resolution remote sensing images using transferable deep models[J]. Remote Sensing of Environment, 2020, 237: 111322.
[13] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[C]//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015: 234-241.
[14] ZHOU Z, RAHMAN SIDDIQUEE M M, TAJBAKHSH N, et al. Unet++: a nested U-net architecture for medical image segmentation[C]//Proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis and 8th International Workshop on Multimodal Learning for Clinical Decision Support, Granada, Spain, 2018: 3-11.
[15] HUANG H, LIN L, TONG R, et al. Unet 3+: a full-scale connected unet for medical image segmentation[C]//Proceedings of the 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020: 1055-1059.
[16] LI R, ZHENG S, DUAN C, et al. Multistage attention ResU-Net for semantic segmentation of fine-resolution remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5.
[17] 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.
[18] CHEN L C, PAPANDREOU G, SCHROFF F, et al. Rethinking atrous convolution for semantic image segmentation[J]. arXiv:1706.05587, 2017.
[19] LIU Z, MAO H, WU C Y, et al. A convnet for the 2020s[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 11976-11986.
[20] POLIYAPRAM V, IMAMOGLU N, NAKAMURA R. Deep learning model for water/ice/land classification using large-scale medium resolution satellite images[C]//Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019: 3884-3887.
[21] HE C, LIU Y, WANG D, et al. Automatic extraction of bare soil land from high-resolution remote sensing images based on semantic segmentation with deep learning[J]. Remote Sensing, 2023, 15(6): 1646.
[22] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
[23] SANDLER M, HOWARD A, ZHU M, et al. MobileNetv2: inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 4510-4520.
[24] CHOLLET F. Xception: deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1251-1258.
[25] LIU Z, LIN Y, CAO Y, et al. Swin transformer: hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 10012-10022.
[26] XIE S, GIRSHICK R, DOLLáR P, et al. Aggregated residual transformations for deep neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1492-1500.
[27] 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, 2017, 40(4): 834-848.
[28] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 3-19.
[29] LI R, ZHENG S, ZHANG C, et al. Multiattention network for semantic segmentation of fine-resolution remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-13.
[30] LONG J, LI M M, WANG X Q. Integrating spatial details with long-range contexts for semantic segmentation of very high-resolution remote-sensing images[J]. IEEE Geosci Remote Sens Lett, 2023, 20: 1-5.
[31] QIN X, ZHANG Z, HUANG C, et al. U2-Net: going deeper with nested U-structure for salient object detection[J]. Pattern Recognition, 2020, 106: 107404.
[32] ZHANG Y, CHEN H J, LI Y F, et al. ImDeeplabV3plus with instance selective whitening loss in domain generalization semantic segmentation[J]. IET Intell Transp Syst, 2023, 17(1): 180-192.
[33] CHEN Y, FANG P, YU J, et al. Hi-ResNet: a high-resolution remote sensing network for semantic segmentation[J]. arXiv:2305.12691, 2023.