Enhanced Super-Resolution-based Dual-Path Short-Term Dense Concatenate Metric Change Detection Network for Heterogeneous Remote Sensing Images

LI Xi; ZENG Huaien; WEI Pengcheng

doi:10.11999/JEIT250328

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LI Xi, ZENG Huaien, WEI Pengcheng. Enhanced Super-Resolution-based Dual-Path Short-Term Dense Concatenate Metric Change Detection Network for Heterogeneous Remote Sensing Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250328

Citation:

LI Xi, ZENG Huaien, WEI Pengcheng. Enhanced Super-Resolution-based Dual-Path Short-Term Dense Concatenate Metric Change Detection Network for Heterogeneous Remote Sensing Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250328

Citation:

LI Xi, ZENG Huaien, WEI Pengcheng. Enhanced Super-Resolution-based Dual-Path Short-Term Dense Concatenate Metric Change Detection Network for Heterogeneous Remote Sensing Images[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250328

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Enhanced Super-Resolution-based Dual-Path Short-Term Dense Concatenate Metric Change Detection Network for Heterogeneous Remote Sensing Images

doi: 10.11999/JEIT250328 cstr: 32379.14.JEIT250328

LI Xi^{1, 2},
ZENG Huaien^{1, 2, 3
,
,},
WEI Pengcheng^{1, 2}

1.
National-Level Field Observation and Research Station for Landslides in Hubei-Yangtze Gorges, China Three Gorges University, Yichang 443002, China
2.
College of Civil Engineering & Architecture, China Three Gorges University, Yichang 443002, China
3.
Hubei Key Laboratory of Construction and Management in Hydropower Engineering, China Three Gorges University, Yichang 443002, China

Funds: The National Natural Science Foundation of China (42074005), Open Foundation of Hubei Key Laboratory of Construction and Management in Hydropower Engineering, China Three Gorges University (2023KSD11), Hubei Provincial Natural Science Foundation of China (2025ABF104)

Received Date: 2025-04-28
Accepted Date: 2025-12-31
Rev Recd Date: 2025-12-31

Available Online: 2026-01-08

Abstract

Abstract

Objective In sudden-onset natural disasters such as landslides and floods, homologous pre-event and post-event remote sensing images are often unavailable in a timely manner, which restricts accurate assessment of disaster-induced changes and subsequent disaster relief planning. Optical heterogeneous remote sensing images differ in sensor type, imaging angle, imaging altitude, and acquisition time. These differences lead to challenges in cross time–space–spectrum change detection, particularly due to spatial resolution inconsistency, spectral discrepancies, and the complexity and diversity of change types for identical ground objects. To address these issues, an Enhanced Super-Resolution-Based Dual-Path Short-Term Dense Concatenate Metric Change Detection Network (ESR-DSMNet) is proposed to achieve accurate and efficient change detection in optical heterogeneous remote sensing images. Methods The ESR-DSMNet consists of an Enhanced Super-Resolution-Based Heterogeneous Remote Sensing Image Quality Optimization Network (ESRNet) and a Dual-Path Short-Term Dense Concatenate Metric Change Detection Network (DSMNet). ESRNet first establishes mapping relationships between remote sensing images with different spatial resolutions using an enhanced super-resolution network. Based on this mapping, low-resolution images are reconstructed to enhance high-frequency edge information and fine texture details, thereby unifying the spatial resolution of heterogeneous remote sensing images at the image level. DSMNet comprises a semantic branch, a spatial-detail branch, a dual-branch feature fusion module, and a metric module based on a batch-balanced contrast loss function. This architecture addresses spectral discrepancies at the feature level and enables accurate and efficient change detection in heterogeneous remote sensing images. Three loss functions are used to optimize the proposed network, which is evaluated and compared with twelve deep learning-based change detection benchmark methods on four datasets, including homologous and heterogeneous remote sensing image datasets. Results and Discussions Comparative analysis on the SYSU dataset (Table 2) shows that DSMNet outperforms the other twelve change detection methods, achieving the highest recall and F1 values of 82.98% and 79.69%, respectively. The method exhibits strong internal consistency for large-area objects and the best visual performance (Fig. 5). On the CLCD dataset (Table 2), DSMNet ranks first in accuracy among the twelve methods, with recall and F1 values of 73.98% and 71.01%, respectively, and demonstrates superior performance in detecting small-object changes (Fig. 5). On the heterogeneous remote sensing image dataset WXCD (Table 3), ESR-DSMNet achieves the highest F1 value of 95.87% compared with the other methods, with more consistent internal regions and finer building edges (Fig. 6). On the heterogeneous remote sensing image dataset SACD (Table 3), ESR-DSMNet attains the highest recall and F1 values of 92.63% and 90.55%, respectively, and produces refined edges in both dense and sparse building change detection scenarios (Fig. 6). Compared with low-resolution images, the reconstructed images present sharper edges without distortion, which improves change detection accuracy (Fig. 6). Comparisons of reconstructed image quality using different super-resolution methods (Table 4 and Fig. 7), ablation experiments on the DSMNet core modules (Table 5 and Fig. 8), and model efficiency evaluations (Table 6 and Fig. 9) further verify the effectiveness and generalization performance of the proposed method. Conclusions The ESR-DSMNet is proposed to address spatial resolution inconsistency, spectral discrepancies, and the complexity and diversity of change types in heterogeneous remote sensing image change detection. The ESRNet unifies spatial resolution at the image level, whereas the DSMNet mitigates spectral differences at the feature level and improves detection accuracy and efficiency. The proposed network is optimized using three loss functions and validated on two homologous and two heterogeneous remote sensing image datasets. Experimental results demonstrate that ESR-DSMNet achieves superior generalization performance and higher accuracy and efficiency than twelve advanced deep learning-based remote sensing image change detection methods. Additional experiments on reconstructed image quality, DSMNet module ablation, and model efficiency comparisons further confirm the effectiveness of the proposed approach.
- Heterogeneous remote sensing images,
- Change detection,
- Super-resolution,
- Deep learning

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References(32)

References

[1]	LIU Quanyong, PENG Jiangtao, ZHANG Genwei, et al. Deep contrastive learning network for small-sample hyperspectral image classification[J]. Journal of Remote Sensing, 2023, 3: 0025. doi: 10.34133/remotesensing.0025.
[2]	LIU Shuaijun, LIU Jia, TAN Xiaoyue, et al. A hybrid spatiotemporal fusion method for high spatial resolution imagery: Fusion of gaofen-1 and sentinel-2 over agricultural landscapes[J]. Journal of Remote Sensing, 2024, 4: 0159. doi: 10.34133/remotesensing.0159.
[3]	WANG Haoyu and LI Xiaofeng. Expanding horizons: U-net enhancements for semantic segmentation, forecasting, and super-resolution in ocean remote sensing[J]. Journal of Remote Sensing, 2024, 4: 0196. doi: 10.34133/remotesensing.0196.
[4]	MEI Shaohui, LIAN Jiawei, WANG Xiaofei, et al. A comprehensive study on the robustness of deep learning-based image classification and object detection in remote sensing: Surveying and benchmarking[J]. Journal of Remote Sensing, 2024, 4: 0219. doi: 10.34133/remotesensing.0219.
[5]	BAI Ting, WANG Le, YIN Dameng, et al. Deep learning for change detection in remote sensing: A review[J]. Geo-Spatial Information Science, 2023, 26(3): 262–288. doi: 10.1080/10095020.2022.2085633.
[6]	DAUDT R C, SAUX B L, and BOULCH A. Fully convolutional Siamese networks for change detection[C]. 2018 25th IEEE international conference on image processing (ICIP), Athens, Greece, 2018: 4063–4067. doi: 10.1109/ICIP.2018.8451652.
[7]	ZHANG Chenxiao, YUE Peng, TAPETE D, et al. A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 166: 183–200. doi: 10.1016/j.isprsjprs.2020.06.003.
[8]	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: 8007805. doi: 10.1109/LGRS.2021.3056416.
[9]	CHEN Hao and 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. doi: 10.3390/rs12101662.
[10]	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. doi: 10.1109/JSTARS.2020.3037893.
[11]	LEI Tao, GENG Xinzhe, NING Hailong, et al. Ultralightweight spatial-spectral feature cooperation network for change detection in remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 4402114. doi: 10.1109/TGRS.2023.3261273.
[12]	CHEN Hao, QI Zipeng, and SHI Zhenwei. Remote sensing image change detection with transformers[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5607514. doi: 10.1109/TGRS.2021.3095166.
[13]	FENG Yuchao, XU Honghui, JIANG Jiawei, et al. ICIF-Net: Intra-scale cross-interaction and inter-scale feature fusion network for bitemporal remote sensing images change detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 4410213. doi: 10.1109/TGRS.2022.3168331.
[14]	LIU Mengxi, SHI Qian, LIU Penghua, et al. Siamese generative adversarial network for change detection under different scales[C]. IGARSS 2020–2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, United States, 2020: 2543–2546. doi: 10.1109/IGARSS39084.2020.9323499.
[15]	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.
[16]	LI Shaochun, WANG Yanjun, CAI Hengfan, et al. MF-SRCDNet: Multi-feature fusion super-resolution building change detection framework for multi-sensor high-resolution remote sensing imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2023, 119: 103303. doi: 10.1016/j.jag.2023.103303.
[17]	LEDIG C, THEIS L, HUSZÁR F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, United States, 2017: 105–114. doi: 10.1109/CVPR.2017.19.
[18]	WANG Xintao, YU Ke, WU Shixiang, et al. ESRGAN: Enhanced super-resolution generative adversarial networks[C]. Proceedings of the European Conference on Computer Vision, Munich, Germany, 2019: 63–79. doi: 10.1007/978-3-030-11021-5_5.
[19]	LIU Mengxi, SHI Qian, MARINONI A, et al. Super-resolution-based change detection network with stacked attention module for images with different resolutions[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 4403718. doi: 10.1109/TGRS.2021.3091758.
[20]	LI Xi, YAN Li, ZHANG Yi, et al. ESR-DMNet: Enhanced super-resolution-based dual-path metric change detection network for remote sensing images with different resolutions[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5402415. doi: 10.1109/TGRS.2024.3362895.
[21]	SHAO Ruizhe, DU Chun, CHEN Hao, et al. SUNet: Change detection for heterogeneous remote sensing images from satellite and UAV using a dual-channel fully convolution network[J]. Remote Sensing, 2021, 13(18): 3750. doi: 10.3390/rs13183750.
[22]	LIU Mengxi, SHI Qian, LI Jianlong, et al. Learning token-aligned representations with multimodel transformers for different-resolution change detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 4413013. doi: 10.1109/TGRS.2022.3200684.
[23]	XIANG Yunfan, TIAN Xiangyu, XU Yue, et al. EGMT-CD: Edge-guided multimodal transformers change detection from satellite and aerial images[J]. Remote Sensing, 2024, 16(1): 86. doi: 10.3390/rs16010086.
[24]	FAN Mingyuan, LAI Shenqi, HUANG Junshi, et al. Rethinking BiSeNet for real-time semantic segmentation[C]. Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 9711–9720. doi: 10.1109/CVPR46437.2021.00959.
[25]	YU Changqian, WANG Jingbo, PENG Chao, et al. BiSeNet: Bilateral segmentation network for real-time semantic segmentation[C]. Proceedings of the 15th European Conference on Computer Vision, Munich, Germany, 2018: 334–349. doi: 10.1007/978-3-030-01261-8_20.
[26]	YU Changqian, GAO Changxin, WANG Jingbo, et al. BiSeNet V2: Bilateral network with guided aggregation for real-time semantic segmentation[J]. International Journal of Computer Vision, 2021, 129(11): 3051–3068. doi: 10.1007/s11263-021-01515-2.
[27]	SHI Qian, LIU Mengxi, LI Shengchen, et al. A deeply supervised attention metric-based network and an open aerial image dataset for remote sensing change detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5604816. doi: 10.1109/TGRS.2021.3085870.
[28]	LIU Mengxi, CHAI Zhuoqun, DENG Haojun, et al. A CNN-transformer network with multiscale context aggregation for fine-grained cropland change detection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 4297–4306. doi: 10.1109/JSTARS.2022.3177235.
[29]	KEYS R. Cubic convolution interpolation for digital image processing[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1981, 29(6): 1153–1160. doi: 10.1109/TASSP.1981.1163711.
[30]	ZHANG Puzhao, GONG Maoguo, SU Linzhi, et al. Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 116: 24–41. doi: 10.1016/j.isprsjprs.2016.02.013.
[31]	MA Chao, YANG C Y, YANG Xiaokang, et al. Learning a no-reference quality metric for single-image super-resolution[J]. Computer Vision and Image Understanding, 2017, 158: 1–16. doi: 10.1016/j.cviu.2016.12.009.
[32]	MITTAL A, SOUNDARARAJAN R, and BOVIK A C. Making a "completely blind" image quality analyzer[J]. IEEE Signal Processing Letters, 2013, 20(3): 209–212. doi: 10.1109/LSP.2012.2227726.