Daifeng Peng

2.0k total citations · 3 hit papers
24 papers, 1.5k citations indexed

About

Daifeng Peng is a scholar working on Media Technology, Ecology and Atmospheric Science. According to data from OpenAlex, Daifeng Peng has authored 24 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Media Technology, 9 papers in Ecology and 9 papers in Atmospheric Science. Recurrent topics in Daifeng Peng's work include Remote-Sensing Image Classification (17 papers), Remote Sensing in Agriculture (9 papers) and Remote Sensing and Land Use (9 papers). Daifeng Peng is often cited by papers focused on Remote-Sensing Image Classification (17 papers), Remote Sensing in Agriculture (9 papers) and Remote Sensing and Land Use (9 papers). Daifeng Peng collaborates with scholars based in China, Italy and Canada. Daifeng Peng's co-authors include Haiyan Guan, Yongjun Zhang, Lorenzo Bruzzone, Haiyong Ding, Xu Huang, Yi Wan, Zhi Zheng, Bin Zhang, Hao Tang and Lei Ding and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, International Journal of Remote Sensing and Remote Sensing.

In The Last Decade

Daifeng Peng

24 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

2019 600 citations Daifeng Peng, Yongjun Zhang et al. Remote Sensing profile →
SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images

2020 308 citations Daifeng Peng, Lorenzo Bruzzone et al. IEEE Transactions on Geoscience and Remote Sensing profile →
Adapting Segment Anything Model for Change Detection in VHR Remote Sensing Images

2024 87 citations Lei Ding, Kun Zhu et al. IEEE Transactions on Geoscience and Remote Sensing profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Daifeng Peng China	13	1.1k	788	518	259	218	24	1.5k
Andrea Marinoni Italy	19	958 0.9×	645 0.8×	280 0.5×	244 0.9×	167 0.8×	79	1.5k
Erzhu Li China	19	752 0.7×	471 0.6×	310 0.6×	398 1.5×	162 0.7×	62	1.3k
Sudipan Saha Germany	17	877 0.8×	574 0.7×	388 0.7×	252 1.0×	245 1.1×	60	1.3k
Tongfei Liu China	17	786 0.7×	547 0.7×	329 0.6×	170 0.7×	145 0.7×	42	1.0k
Isabel Sargent United Kingdom	9	695 0.6×	335 0.4×	428 0.8×	253 1.0×	136 0.6×	19	1.3k
Keiller Nogueira Brazil	13	1000 0.9×	402 0.5×	371 0.7×	794 3.1×	262 1.2×	30	1.7k
Haokui Zhang China	15	1.8k 1.7×	1.1k 1.5×	332 0.6×	750 2.9×	323 1.5×	30	2.4k
Emmanuel Maggiori France	5	710 0.6×	352 0.4×	201 0.4×	354 1.4×	165 0.8×	8	1.2k
Fan Hu China	12	977 0.9×	389 0.5×	239 0.5×	771 3.0×	189 0.9×	20	1.4k

Countries citing papers authored by Daifeng Peng

Since Specialization

Citations

This map shows the geographic impact of Daifeng Peng's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daifeng Peng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daifeng Peng more than expected).

Fields of papers citing papers by Daifeng Peng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daifeng Peng. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daifeng Peng. The network helps show where Daifeng Peng may publish in the future.

Co-authorship network of co-authors of Daifeng Peng

This figure shows the co-authorship network connecting the top 25 collaborators of Daifeng Peng. A scholar is included among the top collaborators of Daifeng Peng based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Daifeng Peng. Daifeng Peng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Peng, Daifeng, Xuelian Liu, Yongjun Zhang, et al.. (2024). Deep learning change detection techniques for optical remote sensing imagery: Status, perspectives and challenges. International Journal of Applied Earth Observation and Geoinformation. 136. 104282–104282. 12 indexed citations

Liu, Bin, Jian Kang, Haiyan Guan, et al.. (2024). RTCNet: A novel real-time triple branch network for pavement crack semantic segmentation. International Journal of Applied Earth Observation and Geoinformation. 136. 104347–104347. 1 indexed citations

Ding, Lei, et al.. (2024). Adapting Segment Anything Model for Change Detection in VHR Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–11. 87 indexed citations breakdown →

Wang, Guojie, et al.. (2024). Edge-guided multi-scale foreground attention network for change detection in high resolution remote sensing images. International Journal of Applied Earth Observation and Geoinformation. 133. 104070–104070. 2 indexed citations

Zang, Yufu, Haiyan Guan, Daifeng Peng, et al.. (2024). VAM-Net: Vegetation-Attentive deep network for Multi-modal fusion of visible-light and vegetation-sensitive images. International Journal of Applied Earth Observation and Geoinformation. 127. 103642–103642. 2 indexed citations

Li, Yansheng, Xinwei Li, Yongjun Zhang, Daifeng Peng, & Lorenzo Bruzzone. (2023). Cost-efficient information extraction from massive remote sensing data: When weakly supervised deep learning meets remote sensing big data. International Journal of Applied Earth Observation and Geoinformation. 120. 103345–103345. 19 indexed citations

Peng, Daifeng, et al.. (2023). High‐resolution optical remote sensing image change detection based on dense connection and attention feature fusion network. The Photogrammetric Record. 38(184). 498–519. 5 indexed citations

Zheng, Zhi, et al.. (2022). EMS-CDNet: an efficient multi-scale-fusion change detection network for very high-resolution remote sensing images. International Journal of Remote Sensing. 43(14). 5252–5279. 5 indexed citations

Ding, Haiyong, et al.. (2022). Object-Oriented Change Detection Method Based on Spectral–Spatial–Saliency Change Information and Fuzzy Integral Decision Fusion for HR Remote Sensing Images. Remote Sensing. 14(14). 3297–3297. 5 indexed citations

10.

Peng, Daifeng, Haiyan Guan, Yufu Zang, & Lorenzo Bruzzone. (2021). Full-Level Domain Adaptation for Building Extraction in Very-High-Resolution Optical Remote-Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. 60. 1–17. 45 indexed citations

11.

Kang, Jian, Haiyan Guan, Daifeng Peng, & Ziyi Chen. (2021). Multi-scale context extractor network for water-body extraction from high-resolution optical remotely sensed images. International Journal of Applied Earth Observation and Geoinformation. 103. 102499–102499. 35 indexed citations

12.

Peng, Daifeng, et al.. (2021). SCDNET: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation. 103. 102465–102465. 93 indexed citations

13.

Zheng, Zhi, et al.. (2021). CLNet: Cross-layer convolutional neural network for change detection in optical remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 175. 247–267. 146 indexed citations

14.

Peng, Daifeng, Lorenzo Bruzzone, Yongjun Zhang, et al.. (2020). SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. 59(7). 5891–5906. 308 indexed citations breakdown →

15.

Huang, Xu, Xue Wan, & Daifeng Peng. (2020). Robust Feature Matching with Spatial Smoothness Constraints. Remote Sensing. 12(19). 3158–3158. 10 indexed citations

16.

Cao, Shuang, et al.. (2019). Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery. Remote Sensing. 11(14). 1708–1708. 12 indexed citations

17.

Peng, Daifeng & Haiyan Guan. (2019). Unsupervised change detection method based on saliency analysis and convolutional neural network. Journal of Applied Remote Sensing. 13(2). 1–1. 20 indexed citations

18.

Guan, Haiyan, et al.. (2019). A Comparative Land-Cover Classification Feature Study of Learning Algorithms: DBM, PCA, and RF Using Multispectral LiDAR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12(4). 1314–1326. 37 indexed citations

19.

Peng, Daifeng, Yongjun Zhang, & Haiyan Guan. (2019). End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++. Remote Sensing. 11(11). 1382–1382. 600 indexed citations breakdown →

20.

Peng, Daifeng & Yongjun Zhang. (2016). BUILDING CHANGE DETECTION BY COMBINING LiDAR DATA AND ORTHO IMAGE. The international archives of the photogrammetry, remote sensing and spatial information sciences. XLI-B3. 669–676. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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