测绘学报 ›› 2024, Vol. 53 ›› Issue (6): 1224-1235.doi: 10.11947/j.AGCS.2024.20230436
丁少鹏1,2(), 卢秀山3, 刘如飞1(
), 杨懿2, 顾海燕2, 李海涛2
收稿日期:
2023-09-28
发布日期:
2024-07-22
通讯作者:
刘如飞
E-mail:dingsp18@163.com;liurufei@sdust.edu.cn
作者简介:
丁少鹏(1994—),男,博士生,研究方向为遥感影像变化检测。 E-mail:dingsp18@163.com
基金资助:
Shaopeng DING1,2(), Xiushan LU3, Rufei LIU1(
), Yi YANG2, Haiyan GU2, Haitao LI2
Received:
2023-09-28
Published:
2024-07-22
Contact:
Rufei LIU
E-mail:dingsp18@163.com;liurufei@sdust.edu.cn
About author:
DING Shaopeng (1994—), male, PhD candidate, majors in remote sensing image change detection. E-mail: dingsp18@163.com
Supported by:
摘要:
高分遥感影像具有丰富的细节特征,建筑物变化类型多,尺度差异大。针对建筑物变化在复杂环境中易出现空洞和遗漏的问题,本文提出联合目标特征引导与多重注意力的建筑物变化检测方法,通过建筑物目标级引导强化类别信息,实现高分辨影像精细变化信息提取。该方法由建筑物显著增强模块和目标引导的多重注意力模块组成,通过全局深层特征感知与融合,提取建筑物重点区域,结合目标级特征引导和多重自注意力强化特征表达,增强上下文特征相关关系,有效减少细节特征损失,解决目标空洞和边缘不清晰造成的细节损失问题。通过两组试验表明该方法能够提升准确率,在变化种类越多的场景中有效减少变化损失,提高算法稳定性。
中图分类号:
丁少鹏, 卢秀山, 刘如飞, 杨懿, 顾海燕, 李海涛. 联合目标特征引导与多重注意力的建筑物变化检测[J]. 测绘学报, 2024, 53(6): 1224-1235.
Shaopeng DING, Xiushan LU, Rufei LIU, Yi YANG, Haiyan GU, Haitao LI. Building change detection method combining object feature guidance and multiple attention mechanism[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1224-1235.
表1
LEVIR-CD精度评价"
方法 | P | R | F1值 | IoU | OA |
---|---|---|---|---|---|
FC-EF | 78.23 | 82.28 | 80.20 | 66.95 | 97.93 |
FC-Siam-diff | 88.73 | 85.90 | 87.29 | 77.45 | 98.73 |
FC-Siam-conc | 86.43 | 85.93 | 86.18 | 75.72 | 98.60 |
STANet | 91.38 | 85.01 | 88.08 | 78.69 | 98.74 |
BiT | 89.56 | 90.33 | 89.94 | 81.72 | 98.98 |
ChangeFormer | 88.62 | 91.78 | 90.17 | 82.10 | 99.02 |
SNAFF | 91.94 | 89.65 | 90.78 | 83.11 | 99.07 |
GASNet | 90.51 | 91.61 | 91.05 | 83.58 | 99.08 |
本文方法 | 91.82 | 91.34 | 91.58 | 84.47 | 99.14 |
表2
WHU-CD数据精度对比"
方法 | P | R | F1值 | IoU | OA |
---|---|---|---|---|---|
FC-EF | 65.59 | 79.44 | 71.85 | 56.07 | 97.81 |
FC-Siam-diff | 87.18 | 58.53 | 70.04 | 53.89 | 96.82 |
FC-Siam-conc | 50.93 | 88.17 | 64.56 | 47.67 | 95.88 |
STANet | 93.03 | 83.49 | 88.00 | 78.58 | 99.03 |
BiT | 88.47 | 83.68 | 86.01 | 75.45 | 98.77 |
ChangeFormer | 85.32 | 89.73 | 87.47 | 77.73 | 98.96 |
SNAFF | 92.76 | 89.65 | 91.18 | 83.79 | 99.26 |
GASNet | 92.83 | 90.33 | 91.56 | 84.44 | 99.29 |
本文方法 | 92.04 | 91.38 | 91.71 | 84.69 | 99.30 |
表3
不同数据的消融试验"
数据集 | 方法 | P | R | F1值 | IoU | OA |
---|---|---|---|---|---|---|
LEVIR-CD | Baseline | 88.04 | 89.90 | 88.96 | 80.11 | 98.86 |
Baseline+BISEM | 91.42 | 89.22 | 90.31 | 82.33 | 99.02 | |
Baseline+FGMAM | 90.61 | 90.33 | 90.47 | 82.60 | 99.03 | |
本文方法 | 91.82 | 91.34 | 91.58 | 84.47 | 99.14 | |
WHU-CD | Baseline | 87.63 | 87.99 | 87.81 | 78.27 | 98.96 |
Baseline+BISEM | 91.61 | 88.13 | 89.83 | 81.55 | 99.15 | |
Baseline+FGMAM | 87.47 | 91.89 | 89.63 | 81.21 | 99.09 | |
本文方法 | 92.04 | 91.38 | 91.71 | 84.69 | 99.30 |
[1] | 张继贤, 李海涛, 顾海燕, 等. 人机协同的自然资源要素智能提取方法[J]. 测绘学报, 2021, 50(8):1023-1032. DOI:10.11947/j.AGCS.2021.20210102. |
ZHANG Jixian, LI Haitao, GU Haiyan, et al. Study on man-machine collaborative intelligent extraction for natural resource features[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1023-1032. DOI:10.11947/j.AGCS.2021.20210102. | |
[2] | 陈军, 刘万增, 武昊, 等. 智能化测绘的基本问题与发展方向[J]. 测绘学报, 2021, 50(8):995-1005.DOI:10.11947/j.AGCS.2021.20210235. |
CHEN Jun, LIU Wanzeng, WU Hao, et al. Smart surveying and mapping: fundamental issues and research agenda[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):995-1005.DOI:10.11947/j.AGCS.2021.20210235. | |
[3] | 王志盼, 沈彦, 王亮, 等. 单类分类框架下的高分辨率遥感影像建筑物变化检测算法[J]. 武汉大学学报(信息科学版), 2020, 45(10):1610-1618. |
WANG Zhipan, SHEN Yan, WANG Liang, et al. High-resolution remote sensing image building change detection based on one-class classifier framework[J]. Geomatics and Information Science of Wuhan University, 2020, 45(10):1610-1618. | |
[4] | 叶沅鑫, 孙苗苗, 周亮, 等. 面向建筑物变化检测的主体边缘分解与重组神经网络[J]. 测绘学报, 2023, 52(1):71-81.DOI:10.11947/j.AGCS.2023.20210350. |
YE Yuanxin, SUN Miaomiao, ZHOU Liang, et al. Main body, edge decomposition and reorganization network for building change detection[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(1):71-81.DOI:10.11947/j.AGCS.2023.20210350. | |
[5] | SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4):640-651. |
[6] | RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C]//Proceedings of 2015 International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2015: 234-241. |
[7] | HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778. |
[8] | ZHOU Lichen, ZHANG Chuang, WU Ming. D-LinkNet: LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City: IEEE, 2018: 182-186. |
[9] | SUN Ke, XIAO Bin, LIU Dong, et al. Deep high-resolution representation learning for human pose estimation[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 5686-5696. |
[10] | ZUO Zongcheng, ZHANG Wen, ZHANG Dongying. A remote sensing image semantic segmentation method by combining deformable convolution with conditional random fields[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(3):39-49. |
[11] | CAYE DAUDT R, LE SAUX B, BOULCH A. Fully convolutional Siamese networks for change detection[C]//Proceedings of 2018 IEEE International Conference on Image Processing. Athens: IEEE, 2018: 4063-4067. |
[12] | WANG Moyang, TAN Kun, JIA Xiuping, et al. A deep Siamese network with hybrid convolutional feature extraction module for change detection based on multi-sensor remote sensing images[J]. Remote Sensing, 2020, 12(2):205. |
[13] | HE Hao, WANG Shuyang, WANG Shicheng, et al. A road extraction method for remote sensing image based on encoder-decoder network[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(2):16-25. |
[14] | 王超, 王帅, 陈晓, 等. 联合UNet++和多级差分模块的多源光学遥感影像对象级变化检测[J]. 测绘学报, 2023, 52(2):283-296.DOI:10.11947/j.AGCS.2023.20220202. |
WANG Chao, WANG Shuai, CHEN Xiao, et al. Object-level change detection of multi-sensor optical remote sensing images combined with UNet++ and multi-level difference module[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(2):283-296. DOI:10.11947/j.AGCS.2023.20220202. | |
[15] | ZHENG Zhi, WAN Yi, ZHANG Yongjun, et al. CLNet: cross-layer convolutional neural network for change detection in optical remote sensing imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 175:247-267. |
[16] | 王昶, 张永生, 纪松, 等. 建筑物变化的多特征融和及随机多图综合检测法[J]. 测绘学报, 2021, 50(2):235-247.DOI:10.11947/j.AGCS.2021.20200097. |
WANG Chang, ZHANG Yongsheng, JI Song, et al. Multi-feature fusion and random multi-graph synthetic building change method[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(2):235-247.DOI:10.11947/j.AGCS.2021.20200097. | |
[17] | PENG Xueli, ZHONG Ruofei, LI Zhen, et al. Optical remote sensing image change detection based on attention mechanism and image difference[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(9):7296-7307. |
[18] | JIANG Huiwei, HU Xiangyun, LI Kun, et al. PGA-SiamNet: pyramid feature-based attention-guided Siamese network for remote sensing orthoimagery building change detection[J]. Remote Sensing, 2020, 12(3):484. |
[19] | LIU Yi, PANG Chao, ZHAN Zongqian, et al. Building change detection for remote sensing images using a dual-task constrained deep Siamese convolutional network model[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(5):811-815. |
[20] | WANG Kexian, ZHENG Shunyi, LI Rui, et al. A deep double-channel dense network for hyperspectral image classification[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(4):46-62. |
[21] | 贾立新, 胡奕标, 金燕, 等. 融合多种注意力机制的结直肠息肉分割神经网络[J]. 计算机辅助设计与图形学学报, 2023, 35(3):138-148. |
JIA Lixin, HU Yibiao, JIN Yan, et al. Polyp segmentation network combined with multi-attention mechanism[J]. Journal of Computer-Aided Design & Computer Graphics, 2023, 35(3):138-148. | |
[22] | CHEN Jie, YUAN Ziyang, PENG Jian, et al. DASNet: dual attentive fully convolutional Siamese networks for change detection in high-resolution satellite images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14:1194-1206. |
[23] | CHEN Hao, QI Zipeng, SHI Zhenwei. Remote sensing image change detection with transformers[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-14. |
[24] | CHEN Hao, SHI Zhenwei. A spatial-temporal attention-based method and a new dataset for remote sensing image change detection[J]. Remote Sensing, 2020, 12(10):1662. |
[25] | FANG Sheng, LI Kaiyu, SHAO Jinyuan, et al. SNUNet-CD: a densely connected Siamese network for change detection of VHR images[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19:1-5. |
[26] | PENG Daifeng, ZHAI Chenchen, ZHANG Yongjun, et al. High-resolution optical remote sensing image change detection based on dense connection and attention feature fusion network[J]. The Photogrammetric Record, 2023, 38(184):498-519. |
[27] | ZHANG Ruiqian, ZHANG Hanchao, NING Xiaogang, et al. Global-aware Siamese network for change detection on remote sensing images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 199:61-72. |
[28] | BANDARA W G C, PATEL V M. A transformer-based Siamese network for change detection[C]//Proceedings of 2022 IEEE International Geoscience and Remote Sensing Symposium. Kuala Lumpur: IEEE, 2022: 207-210. |
[29] | SONG Zixuan, LI Xiongfei, ZHU Rui, et al. ERMF: edge refinement multi-feature for change detection in bitemporal remote sensing images[J]. Signal Processing: Image Communication, 2023, 116:116964. |
[30] | ZHANG Hongyan, LIN Manhui, YANG Guangyi, et al. ESCNet: an end-to-end superpixel-enhanced change detection network for very-high-resolution remote sensing images[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(1):28-42. |
[31] | CHEN Chen, LI Zhilin, LI Songnian, et al. From digitalized to intelligentized surveying and mapping: fundamental is-sues and research agenda[J]. Journal of Geodesy and Geoinformation Science, 2022, 5(2):148-160. |
[32] | JI Shunping, WEI Shiqing, LU Meng. Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(1):574-586. |
[33] | 陈军, 艾廷华, 闫利, 等. 智能化测绘的混合计算范式与方法研究[J/OL]. 测绘学报: 1-19 [2024-05-18]. http://kns.cnki.net/kcms/detail/11.2089.P.20240415.1049.002.html. |
CHEN Jun, AI Tinghua, YAN Li, et al. Hybrid computational paradigm and methods for intelligentized surveying and mapping[J/OL]. Acta Geodaetica et Cartographica Sinica: 1-19 [2024-05-18]. http://kns.cnki.net/kcms/detail/11.2089.P.20240415.1049.002.html. |
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