Interests
Regional/global land surface feature detection and dynamic monitoring based on multi-source geospatial data and intelligent algorithms
Project Chaired
• Research on high-resolution remotely sensed image change detection based on visual mechanisms, funded by the Natural Science Foundation of Jiangsu Province (BK20190785), July 2019 – June 2023.
• Super-pixel-based unsupervised change detection, funded by the Fundamental Research Funds for the Central Universities of China (2017BSCXB39), Jan 2017 – Jan 2018.
Awards
• Second Prize in the National Teaching Innovation Competition for Teachers Majoring in Surveying and Mapping, Aug. 2024.
• First Prize in the National Lecture Competition for Young Teachers Majoring in Surveying and Mapping, Jul. 2022.
• First Prize in the National Teaching Innovation Competition for Teachers Majoring in Surveying and Mapping, Jul. 2022.
• Second Prize in the Teaching Innovation Competition at NUIST, Sep. 2021.
Publications
• Yang, F., He, P., Wang, H., Hou, D., Li, D., and Shi, Y. Long-term, high-resolution GPP mapping in Qinghai using multi-source data and google earth engine. International Journal of Digital Earth, 2023; 16(2), 4885- 4905. https://doi.org/10.1080/17538947.2023.2288131
• Yang, F., He, P., Ding, H., and Shi, Y. A monthly high-resolution net primary productivity dataset (30 m) of Qinghai Plateau from 1987 to 2021. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023; 16, 8262-8273. https://doi.org/10.1109/JSTARS.2023.3312518
• He, P., Shi, Y., Ding, H., and Yang, F. Classification and transition of grassland in Qinghai, China, from 1986 to 2020 with Landsat archives on Google Earth Engine. Land, 2023; 12(9), 1686. https://doi.org/10.3390/land12091686
• Shi M., He P., Shi Y. Detecting extratropical cyclones of the northern hemisphere with Single Shot Detector. Remote Sensing, 2022; 14(2):254. https://doi.org/10.3390/rs14020254
• Peng, D., Bruzzone, L., Zhang, Y., Guan, H. and He, P. SCDNET: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery. International Journal of Applied Earth Observation and Geoinformation, 2021;103:102465. https://doi.org/10.1016/j.jag.2021.102465
• He, P., Zhao, X., Shi, Y., Cai, L. Unsupervised change detection from remotely sensed images based on multi-scale visual saliency coarse-to-fine fusion. Remote Sensing. 2021;13, 630. https://doi.org/10.3390/rs13040630
• He, P., Shi, W., & Zhang, H. Adaptive superpixel based Markov random field model for unsupervised change detection using remotely sensed images. Remote Sensing Letters, 2018; 9(8), 724- 732. https://doi.org/10.1080/2150704X.2018.1470698
• Hao, M., Shi, W., Deng, K., Zhang, H. and He, P., 2016. An object-based change detection approach using uncertainty analysis for VHR images. Journal of Sensors, 2016; https://doi.org/10.1155/2016/9078364
• Shao, P.; Shi, W.; He, P.; Hao, M.; Zhang, X. Novel approach to unsupervised change detection based on a robust semi-supervised FCM clustering algorithm. Remote Sensing. 2016, 8, 264. https://doi.org/10.3390/rs8030264
• Shi, W., Shao, P., Hao, M., He, P. and Wang, J. Fuzzy topology–based method for unsupervised change detection. Remote Sensing Letters, 2016; 7(1), 81-90. https://doi.org/10.1080/2150704X.2015.1109155
• He, P., Shi, W., Miao Z. Zhang, H., Can L. Advanced Markov Random field model based on local uncertainty for unsupervised change detection. Remote Sensing Letters, 2015; 6(9), 667- 676. https://doi.org/10.1080/2150704X.2015.1054045
• Cai, L., Shi, W., He, P., Miao Z., Hao, M., Zhang, H., Can L. Fusion of multiple features to produce a segmentation algorithm for remote sensing images. Remote Sensing Letters, 2015; 6(5), 390- 398.https://doi.org/10.1080/2150704X.2015.1037467
• He, P., Shi, W., Zhang, H., Hao M. A novel dynamic threshold method for unsupervised change detection from remotely sensed images. Remote Sensing Letters, 2014; 5(4), 396- 403. https://doi.org/10.1080/2150704X.2014.912766