{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:11:26Z","timestamp":1760148686585,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T00:00:00Z","timestamp":1684713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2022JQ-694"],"award-info":[{"award-number":["2022JQ-694"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Edge detection for PolSAR images has demonstrated its importance in various applications such as segmentation and classification. Although there are many edge detectors which have demonstrated an impressive ability to achieve accurate edge detection results, these methods only focus on edge detection in a single-date PolSAR image. However, a single-date PolSAR image cannot fully characterize the changes in scattering mechanisms of land cover in different growth cycles, resulting in some omissions of the true edges. In this paper, we propose a novel edge detection method for multi-temporal PolSAR images based on the SIRV model and an SDAN-based 3D Gaussian-like kernel. The spherically invariant random vector (SIRV) and span-driven adaptive neighborhood (SDAN) improve the estimation accuracy of the average covariance matrix (ACM) in terms of data representation and spatial support, respectively. We propose an SDAN-based 2D Gaussian kernel to accurately extract the edge strength of single-date PolSAR images. Then, we design a 1D convolution kernel in the temporal dimension to smooth fluctuations in the edge strength of multi-temporal PolSAR images. The SDAN-based 2D Gaussian kernels in the X- and Y-directions are combined with the 1D convolution kernel in the Z-direction to form an SDAN-based 3D Gaussian-like kernel. In addition, we design an adaptive hysteresis threshold method to optimize the edge map. The performance of our proposed method is presented and analyzed on two real multi-temporal PolSAR datasets, and the results demonstrate that the proposed edge detector achieves a better performance than other edge detectors, particularly for crop regions with time-varying scattering mechanisms.<\/jats:p>","DOI":"10.3390\/rs15102685","type":"journal-article","created":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T05:49:32Z","timestamp":1684734572000},"page":"2685","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Edge Detection Method for Multi-Temporal PolSAR Images Based on the SIRV Model and a SDAN-Based 3D Gaussian-like Kernel"],"prefix":"10.3390","volume":"15","author":[{"given":"Xiaolong","family":"Zheng","sequence":"first","affiliation":[{"name":"High-Tech Institute of Xi\u2019an, Xi\u2019an 710025, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5025-8200","authenticated-orcid":false,"given":"Dongdong","family":"Guan","sequence":"additional","affiliation":[{"name":"High-Tech Institute of Xi\u2019an, Xi\u2019an 710025, China"}]},{"given":"Bangjie","family":"Li","sequence":"additional","affiliation":[{"name":"High-Tech Institute of Xi\u2019an, Xi\u2019an 710025, China"}]},{"given":"Zhengsheng","family":"Chen","sequence":"additional","affiliation":[{"name":"High-Tech Institute of Xi\u2019an, Xi\u2019an 710025, China"}]},{"given":"Lefei","family":"Pan","sequence":"additional","affiliation":[{"name":"High-Tech Institute of Xi\u2019an, Xi\u2019an 710025, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,22]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"PolSAR image classification with multiscale superpixel-based graph convolutional network","volume":"60","author":"Cheng","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2412","DOI":"10.1109\/TGRS.2019.2949066","article-title":"Adaptive statistical superpixel merging with edge penalty for PolSAR image segmentation","volume":"58","author":"Xiang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5435","DOI":"10.1109\/TGRS.2015.2422737","article-title":"Adaptive-window polarimetric SAR image speckle filtering based on a homogeneity measurement","volume":"53","author":"Lang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","first-page":"1","article-title":"A Novel Crop Classification Method Based on the Tensor-GCN for Time-Series PolSAR Data","volume":"60","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sun, Y., Lei, L., Guan, D., Wu, J., Kuang, G., and Liu, L. (2022). Image regression with structure cycle consistency for heterogeneous change detection. IEEE Trans. Neural Netw. Learn. Syst., 1\u201315.","DOI":"10.1109\/TNNLS.2022.3184414"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/TGRS.2002.808063","article-title":"CFAR edge detector for polarimetric SAR images","volume":"41","author":"Schou","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1109\/LGRS.2012.2184521","article-title":"Edge detector of SAR images using Gaussian-Gamma-shaped bi-windows","volume":"9","author":"Shui","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1661","DOI":"10.1109\/LGRS.2016.2600704","article-title":"Edge detector for polarimetric SAR images using SIRV model and gauss-shaped filter","volume":"13","author":"Xiang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6340","DOI":"10.1080\/01431161.2018.1460501","article-title":"Enhanced edge detection for polarimetric SAR images using a directional span-driven adaptive window","volume":"39","author":"Wang","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1109\/LGRS.2016.2569534","article-title":"Improved multiscale edge detection method for polarimetric SAR images","volume":"13","author":"Jin","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4895","DOI":"10.1109\/JSTARS.2018.2877670","article-title":"CFAR-based adaptive PolSAR speckle filter","volume":"11","author":"Sharma","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Deng, S., Zhang, J., Li, P., and Huang, G. (2011, January 24\u201329). Edge detection from polarimetric SAR images using polarimetric whitening filter. Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6049161"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1109\/JSTARS.2013.2266319","article-title":"Comparing edge detection methods based on stochastic entropies and distances for PolSAR imagery","volume":"7","author":"Nascimento","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","first-page":"1","article-title":"Fusion of evidences in intensity channels for edge detection in PolSAR images","volume":"19","author":"Marengoni","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"8974","DOI":"10.1109\/ACCESS.2020.2963989","article-title":"A novel hybrid edge detection method for polarimetric SAR images","volume":"8","author":"Shi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2384","DOI":"10.1109\/78.539023","article-title":"Non\u2013Gaussian clutter modeling with generalized spherically invariant random vectors","volume":"44","author":"Barnard","year":"1996","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1016\/j.rse.2017.09.035","article-title":"Effect of classifier selection, reference sample size, reference class distribution and scene heterogeneity in per-pixel classification accuracy using 26 Landsat sites","volume":"204","author":"Heydari","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1109\/TGRS.2009.2035496","article-title":"Coherency matrix estimation of heterogeneous clutter in high-resolution polarimetric SAR images","volume":"48","author":"Vasile","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIT.1973.1055076","article-title":"A representation theorem and its applications to spherically-invariant random processes","volume":"19","author":"Yao","year":"1973","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_20","first-page":"1","article-title":"TSPol-ASLIC: Adaptive superpixel generation with local iterative clustering for time-series quad-and dual-polarization SAR data","volume":"60","author":"Gao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lee, J.S., and Pottier, E. (2017). Polarimetric Radar Imaging: From Basics to Applications, CRC Press.","DOI":"10.1201\/9781420054989"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1782","DOI":"10.1109\/LGRS.2014.2309139","article-title":"Urban area human-made target detection for PolSAR data based on a nonzero-mean statistical model","volume":"11","author":"Wu","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1109\/LGRS.2012.2198612","article-title":"Supervised graph embedding for polarimetric SAR image classification","volume":"10","author":"Shi","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5923","DOI":"10.1109\/TGRS.2019.2903096","article-title":"SIRV-based high-resolution PolSAR image speckle suppression via dual-domain filtering","volume":"57","author":"Ren","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3115","DOI":"10.1109\/TGRS.2017.2662010","article-title":"Adaptive superpixel generation for polarimetric SAR images with local iterative clustering and SIRV model","volume":"55","author":"Xiang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Magnier, B., Abdulrahman, H., and Montesinos, P. (2018). A review of supervised edge detection evaluation methods and an objective comparison of filtering gradient computations using hysteresis thresholds. J. Imaging, 4.","DOI":"10.3390\/jimaging4060074"},{"key":"ref_27","first-page":"165","article-title":"Determining hysteresis thresholds for edge detection by combining the advantages and disadvantages of thresholding methods","volume":"19","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/LGRS.2015.2493640","article-title":"Edge detector of SAR images using crater-shaped window with edge compensation strategy","volume":"13","author":"Wei","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/LGRS.2019.2919422","article-title":"Edge detection for PolSAR images integrating scattering characteristics and optimal contrast","volume":"17","author":"Quan","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2685\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:39:49Z","timestamp":1760125189000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2685"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,22]]},"references-count":29,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15102685"],"URL":"https:\/\/doi.org\/10.3390\/rs15102685","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,5,22]]}}}