{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:32:35Z","timestamp":1760236355940,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T00:00:00Z","timestamp":1636934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61427808","61333009"],"award-info":[{"award-number":["61427808","61333009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application.<\/jats:p>","DOI":"10.3390\/rs13224588","type":"journal-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T20:46:47Z","timestamp":1637009207000},"page":"4588","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Sea-Land Clutter Classification Based on Graph Spectrum Features"],"prefix":"10.3390","volume":"13","author":[{"given":"Le","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory for IOT and Information Fusion Technology of Zhejiang, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Anke","family":"Xue","sequence":"additional","affiliation":[{"name":"Key Laboratory for IOT and Information Fusion Technology of Zhejiang, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Xiaodong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory for IOT and Information Fusion Technology of Zhejiang, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3557-5897","authenticated-orcid":false,"given":"Shuwen","family":"Xu","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Kecheng","family":"Mao","sequence":"additional","affiliation":[{"name":"Key Laboratory for IOT and Information Fusion Technology of Zhejiang, Hangzhou Dianzi University, Hangzhou 310018, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.comcom.2019.08.016","article-title":"High-dimensional feature extraction of sea clutter and target signal for intelligent maritime monitoring network","volume":"147","author":"Liu","year":"2019","journal-title":"Comput. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Luo, Y., Qi, G., Meng, J., Li, Y., and Mazur, N. (2021). Remote Sensing Image Defogging Networks Based on Dual Self-Attention Boost Residual Octave Convolution. Remote Sens., 13.","DOI":"10.3390\/rs13163104"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Luo, Y., Wei, H., Li, Y., Qi, G., Mazur, N., Li, Y., and Li, P. (2021). Atmospheric Light Estimation Based Remote Sensing Image Dehazing. Remote Sens., 13.","DOI":"10.3390\/rs13132432"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jakubiak, A., Arabas, J., Grabczak, K., Radomski, D., and Swiderski, J. (1997, January 14\u201316). Radar clutter classification using Kohonen neural network. Proceedings of the Radar 97 (Conf. Publ. No. 449), Edinburgh, UK.","DOI":"10.1049\/cp:19971658"},{"key":"ref_5","first-page":"758","article-title":"Polish radar technology. IV-Signal detection in non-Gaussian clutter","volume":"27","author":"Jakubiak","year":"1991","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sarikaya, T., Soysal, G., Efe, M., Sobaci, E., and Kirubarajan, T. (2017, January 23\u201326). Sea-land classification using radar clutter statistics for shore-based surveillance radars. Proceedings of the International Conference on Radar Systems (Radar 2017), Belfast, UK.","DOI":"10.1049\/cp.2017.0488"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1109\/5.90155","article-title":"Classification of radar clutter in an air traffic control environment","volume":"79","author":"Haykin","year":"1991","journal-title":"Proc. IEEE"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9379","DOI":"10.3390\/rs6109379","article-title":"Statistical analysis of SAR sea clutter for classification purposes","volume":"6","year":"2014","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, L., You, W., Wu, Q., Qi, S., and Ji, Y. (2018). Deep learning-based automatic clutter\/interference detection for HFSWR. Remote Sens., 10.","DOI":"10.3390\/rs10101517"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1109\/JSTSP.2017.2780798","article-title":"Machine Learning Techniques for Coherent CFAR Detection Based on Statistical Modeling of UHF Passive Ground Clutter","volume":"12","author":"Delreymaestre","year":"2018","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ward, K.D., Watts, S., and Tough, R.J. (2006). Sea Clutter: Scattering, the K Distribution and Radar Performance, IET.","DOI":"10.1049\/PBRA020E"},{"key":"ref_12","unstructured":"Blau, W., and Farber, J. (1968). Radar Clutter Modeling, Spectronics Inc.. Technical Report."},{"key":"ref_13","unstructured":"Parthiban, A., Madhavan, J., Radhakrishna, P., Savitha, D., and Kumar, L.S. (2004, January 11\u201314). Modeling and simulation of radar sea clutter using K-distribution. Proceedings of the 2004 International Conference on Signal Processing and Communications, SPCOM\u201904, Bangalore, India."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kemkemian, S., Degurse, J.F., Corretja, V., and Cottron, R. (2016, January 10\u201312). Sea clutter modelling for space-time processing. Proceedings of the 2016 17th International Radar Symposium (IRS), Krakow, Poland.","DOI":"10.1109\/IRS.2016.7497344"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Melebari, A., Mishra, A.K., and Gaffar, M.A. (2015, January 9\u201311). Statistical analysis of measured high resolution land clutter at X-band and clutter simulation. Proceedings of the 2015 European Radar Conference (EuRAD), Paris, France.","DOI":"10.1109\/EuRAD.2015.7346248"},{"key":"ref_16","unstructured":"Xue, J., and Xu, S. (2016, January 5\u20138). Parameters estimation based on moments and Nelder-Mead algorithm for compound-Gaussian clutter with inverse Gaussian texture. Proceedings of the 2016 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Hong Kong, China."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/7.745679","article-title":"High resolution radar clutter statistics","volume":"35","author":"Lampropoulos","year":"1999","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_18","unstructured":"Anastassopoulos, V., and Lampropoulos, G.A. (1995, January 8\u201311). High resolution radar clutter classification. Proceedings of the IEEE 1995 International Radar Conference, Alexandria, VA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Darzikolaei, M.A., Ebrahimzade, A., and Gholami, E. (2015, January 5\u20136). Classification of radar clutters with artificial neural network. Proceedings of the 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), Tehran, Iran.","DOI":"10.1109\/KBEI.2015.7436109"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4304","DOI":"10.1109\/JSTARS.2017.2742939","article-title":"Modified Entropy-Based Fully Polarimetric Target Classification Method for Ground Penetrating Radars (GPR)","volume":"10","author":"Yu","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","first-page":"1505","article-title":"Sea-surface floating small target detection based on polarization features","volume":"15","author":"Xu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jing, W., Ji, G., Liu, S., Wang, X., and Tian, Y. (2017). Target Detection in Sea Clutter Based on ELM. China Conference on Wireless Sensor Networks, Springer.","DOI":"10.1007\/978-981-10-8123-1_3"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.atmosres.2012.02.007","article-title":"Artificial Intelligence Techniques for Clutter Identification with Polarimetric Radar Signatures","volume":"109","author":"Islam","year":"2012","journal-title":"Atmos. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1109\/LGRS.2018.2886782","article-title":"Model for Non-Gaussian Sea Clutter Amplitudes Using Generalized Inverse Gaussian Texture","volume":"16","author":"Xue","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1049\/iet-rsn.2019.0193","article-title":"Constant false alarm rate detection based on estimating statistical distribution of non-homogeneous sea clutter in sky-wave over-the-horizon radar","volume":"14","author":"Li","year":"2020","journal-title":"IET Radar Sonar Navig."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Angelliaume, S., Rosenberg, L., and Ritchie, M. (2019). Modeling the amplitude distribution of radar sea clutter. Remote Sens., 11.","DOI":"10.3390\/rs11030319"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ma, L., Wu, J., Zhang, J., Wu, Z., Jeon, G., Tan, M., and Zhang, Y. (2019). Sea Clutter Amplitude Prediction Using a Long Short-Term Memory Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11232826"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3042","DOI":"10.1109\/TSP.2014.2321121","article-title":"Discrete Signal Processing on Graphs: Frequency Analysis","volume":"62","author":"Sandryhaila","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1109\/TSP.2013.2238935","article-title":"Discrete Signal Processing on Graphs","volume":"61","author":"Sandryhaila","year":"2013","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/LCOMM.2016.2618871","article-title":"Novel Robust Band-Limited Signal Detection Approach Using Graphs","volume":"21","author":"Yan","year":"2017","journal-title":"IEEE Commun. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1016\/j.neucom.2005.12.015","article-title":"Translation-invariant classification of non-stationary signals","volume":"69","author":"Guigue","year":"2006","journal-title":"Neurocomputing"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7093","DOI":"10.1109\/TGRS.2019.2911451","article-title":"Robust Target Detection Within Sea Clutter Based on Graphs","volume":"57","author":"Yan","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.laa.2006.08.017","article-title":"Old and new results on algebraic connectivity of graphs","volume":"423","year":"2007","journal-title":"Linear Algebra Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"298","DOI":"10.21136\/CMJ.1973.101168","article-title":"Algebraic connectivity of graphs","volume":"23","author":"Fiedler","year":"1973","journal-title":"Czechoslov. Math. J."},{"key":"ref_35","unstructured":"Lu, G., and Zhou, L. (2019). Algorithm of the outlier nodes detection of WSN based on graph signal processing. J. Comput. Appl., 1\u20136."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.neucom.2020.05.073","article-title":"An unsupervised image segmentation method combining graph clustering and high-level feature representation","volume":"409","author":"Jiao","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1109\/LGRS.2019.2894385","article-title":"SVM based sea-surface small target detection: A false-alarmrate-controllable approach","volume":"16","author":"Li","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1109\/JSEN.2019.2943152","article-title":"Recognition and Mitigation of Micro-Doppler Clutter in Radar Systems via Support Vector Machine","volume":"20","author":"Zhan","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, L., Thiyagalingam, J., Xue, A., and Xu, S. (2020). A Novel Method for Sea-Land Clutter Separation Using Regularized Randomized and Kernel Ridge Neural Networks. Sensors, 20.","DOI":"10.3390\/s20226491"},{"key":"ref_40","unstructured":"Bakker, R., and Currie, B. (1998, February 04). The McMaster IPIX Radar Sea Clutter Database. Available online: http:\/\/soma.crl.mcmaster.ca\/ipix\/."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4588\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:30:29Z","timestamp":1760167829000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/22\/4588"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,15]]},"references-count":40,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13224588"],"URL":"https:\/\/doi.org\/10.3390\/rs13224588","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,11,15]]}}}