{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:28:49Z","timestamp":1791916129560,"version":"4.3.4"},"reference-count":24,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,1,4]],"date-time":"2017-01-04T00:00:00Z","timestamp":1483488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Statistically homogeneous pixels (SHP) play a crucial role in synthetic aperture radar (SAR) analysis. In past studies, various two-sample tests were applied on multitemporal SAR data stacks under the assumption of having stationary backscattering properties over time. In this letter, we propose the Robust T-test (TR) to improve the effectiveness of test operation. The TR test reduces the impact of temporal variabilities and outliers, thus helping to identify SHP with assurances of similar temporal behaviors. This method includes three steps: (1) signal suppression; (2) outlier removal; and (3) one-sample test. In the experiments, we apply the TR test on both simulated and real data. Different stack sizes, types of distributions, and hypothesis tests are compared. Results of both experiments signify that the TR test outperforms conventional approaches and provides reliable SHP for SAR image analysis.<\/jats:p>","DOI":"10.3390\/rs9010037","type":"journal-article","created":{"date-parts":[[2017,1,4]],"date-time":"2017-01-04T09:39:32Z","timestamp":1483522772000},"page":"37","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Identification of Statistically Homogeneous Pixels Based on One-Sample Test"],"prefix":"10.3390","volume":"9","author":[{"given":"Keng-Fan","family":"Lin","sequence":"first","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4086-8608","authenticated-orcid":false,"given":"Daniele","family":"Perissin","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3460","DOI":"10.1109\/TGRS.2011.2124465","article-title":"A new algorithm for processing interferometric data-stacks: SqueeSAR","volume":"49","author":"Ferretti","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1109\/LGRS.2010.2083631","article-title":"Adaptive InSAR stack multilooking exploiting amplitude statistics: A comparison between different techniques and practical results","volume":"8","author":"Parizzi","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","unstructured":"Goel, K. (2014). Advanced Stacking Techniques and Applications in High Resolution SAR Interferometry. [Ph.D. Thesis, Technical University of Munich]."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5454","DOI":"10.1109\/TGRS.2013.2289370","article-title":"A distributed scatterer interferometry approach for precision monitoring of known surface deformation phenomena","volume":"52","author":"Goel","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1109\/TGRS.2013.2238947","article-title":"Adaptive multilooking of airborne single-pass multi-baseline InSAR stacks","volume":"52","author":"Schmitt","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.1109\/JSTARS.2015.2421554","article-title":"Nonlocal adaptive multilooking in SAR multipass differential interferometry","volume":"8","author":"Sica","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1109\/TGRS.2010.2076376","article-title":"NL-InSAR: Nonlocal interferogram estimation","volume":"49","author":"Deledalle","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1109\/TGRS.2014.2352555","article-title":"NL-SAR: A unified nonlocal framework for resolution-preserving (Pol)(In) SAR Denoising","volume":"53","author":"Deledalle","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2459","DOI":"10.1109\/TGRS.2013.2261996","article-title":"Hybrid approach for unbiased coherence estimation for multitemporal InSAR","volume":"52","author":"Jiang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6584","DOI":"10.1109\/TGRS.2014.2298408","article-title":"InSAR coherence estimation for small data sets and its impact on temporal decorrelation extraction","volume":"52","author":"Jiang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6807","DOI":"10.1109\/TGRS.2014.2303516","article-title":"Adaptive covariance matrix estimation for multi-baseline InSAR data stacks","volume":"52","author":"Schmitt","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1109\/TGRS.2014.2336237","article-title":"Fast statistically homogeneous pixel selection for covariance matrix estimation for multitemporal InSAR","volume":"53","author":"Jiang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1016\/j.dsp.2009.10.014","article-title":"SAR imagery segmentation by statistical region growing and hierarchical merging","volume":"20","author":"Carvalho","year":"2009","journal-title":"Digit. Signal Process."},{"key":"ref_14","unstructured":"Walpole, R.E., and Myers, R.H. (1993). Probability and Statistics for Engineers and Scientists, Macmillan. [5th ed.]."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1109\/TGRS.2004.834630","article-title":"A new statistical model for Markovian classification of urban areas in high-resolution SAR images","volume":"42","author":"Tison","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","unstructured":"Goodman, J.W. (1975). Laser Speckle and Related Phenomena, Springer."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1109\/TAP.1984.1143200","article-title":"Amplitude distribution of composite terrain radar clutter and the \u03ba-distribution","volume":"32","author":"Jao","year":"1984","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1109\/36.581981","article-title":"A model for extremely heterogeneous clutter","volume":"35","author":"Frery","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1109\/TIP.2006.871124","article-title":"SAR amplitude probability density function estimation based on a generalized Gaussian model","volume":"15","author":"Moser","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2392","DOI":"10.1109\/TGRS.2002.803727","article-title":"A review of speckle filtering in the context of estimation theory","volume":"40","author":"Touzi","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","unstructured":"Mascarenhas, N.D.A. (1996, January 2\u20134). An overview of speckle noise filtering in SAR images. Proceedings of the First Latino-American Seminar on Radar Remote Sensing\u2014Image Processing Techniques, Buenos Aires, Argentina."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wilcox, R.R. (2011). Introduction to Robust Estimation and Hypothesis Testing, Elsevier Science. [3rd ed.].","DOI":"10.1016\/B978-0-12-386983-8.00001-9"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5186","DOI":"10.1016\/j.csda.2007.11.008","article-title":"An adjusted boxplot for skewed distributions","volume":"52","author":"Hubert","year":"2008","journal-title":"Computat. Stat. Data Anal."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1198\/106186004X12632","article-title":"A robust measure of skewness","volume":"13","author":"Brys","year":"2004","journal-title":"J. Comput. Graph. Stat."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/1\/37\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:25:27Z","timestamp":1760207127000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/1\/37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,1,4]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2017,1]]}},"alternative-id":["rs9010037"],"URL":"https:\/\/doi.org\/10.3390\/rs9010037","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,1,4]]}}}