{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T02:54:38Z","timestamp":1775530478081,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,9,21]],"date-time":"2017-09-21T00:00:00Z","timestamp":1505952000000},"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>Optical satellite imagery is often contaminated by the persistent presence of clouds and atmospheric haze. Without an effective method for removing this contamination, most optical remote sensing applications are less reliable. In this research, a methodology has been developed to fully automate and improve the Haze Optimized Transformation (HOT)-based haze removal. The method is referred to as AutoHOT and characterized with three notable features: a fully automated HOT process, a novel HOT image post-processing tool and a class-based HOT radiometric adjustment method. The performances of AutoHOT in haze detection and compensation were evaluated through three experiments with one Landsat-5 TM, one Landsat-7 ETM+ and eight Landsat-8 OLI scenes that encompass diverse landscapes and atmospheric haze conditions. The first experiment confirms that AutoHOT is robust and effective for haze detection. The average overall, user\u2019s and producer\u2019s accuracies of AutoHOT in haze detection can reach 96.4%, 97.6% and 97.5%, respectively. The second and third experiments demonstrate that AutoHOT can not only accurately characterize the haze intensities but also improve dehazed results, especially for brighter targets, compared to traditional HOT radiometric adjustment.<\/jats:p>","DOI":"10.3390\/rs9100972","type":"journal-article","created":{"date-parts":[[2017,9,21]],"date-time":"2017-09-21T12:17:40Z","timestamp":1505996260000},"page":"972","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Haze Removal Based on a Fully Automated and Improved Haze Optimized Transformation for Landsat Imagery over Land"],"prefix":"10.3390","volume":"9","author":[{"given":"Lixin","family":"Sun","sequence":"first","affiliation":[{"name":"Canada Centre for Remote Sensing, 560 Rochester Street, Ottawa, ON K1A 0E4, Canada"}]},{"given":"Rasim","family":"Latifovic","sequence":"additional","affiliation":[{"name":"Canada Centre for Remote Sensing, 560 Rochester Street, Ottawa, ON K1A 0E4, Canada"}]},{"given":"Darren","family":"Pouliot","sequence":"additional","affiliation":[{"name":"Canada Centre for Remote Sensing, 560 Rochester Street, Ottawa, ON K1A 0E4, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/S0034-4257(00)00169-3","article-title":"Classification and change detection using Landsat TM data: When and how to correct atmospheric effects?","volume":"75","author":"Song","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1109\/JSTARS.2011.2179638","article-title":"Multimodal change detection, application to the detection of flooded areas: Outcome of the 2009\u20132010 data fusion contest","volume":"5","author":"Longbotham","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/S0034-4257(01)00223-1","article-title":"An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target-based atmospheric correction","volume":"78","author":"Miura","year":"2001","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4027","DOI":"10.1080\/01431160701227703","article-title":"Detection and substitution of clouds\/hazes and their cast shadows on IKONOS images","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","unstructured":"Kaufman, Y.J. (1989). The atmospheric effect on remote sensing and its correction. Theory and Applications of Optical Remote Sensing, Wiley-Interscience."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/S0034-4257(02)00034-2","article-title":"An image transform to characterize and compensate for spatial variations of thin cloud contamination of Landsat images","volume":"82","author":"Zhang","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5895","DOI":"10.1109\/TGRS.2013.2293662","article-title":"Haze detection and removal in remotely sensed multispectral imagery","volume":"52","author":"Makarau","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","first-page":"379","article-title":"Combined haze and cirrus removal for multispectral imagery","volume":"13","author":"Makarau","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gao, B.-C., and Li, R.R. (2017). Removal of thin cirrus scattering effects in Landsat 8 OLI images using cirrus detecting channel. Romote Sens., 9.","DOI":"10.3390\/rs9080834"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1109\/LGRS.2013.2283792","article-title":"A principal component based haze masking method for visible images","volume":"11","author":"Li","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1080\/01431168808954942","article-title":"Algorithm for automatic atmospheric correction to visible and near-IR imagery","volume":"9","author":"Kaufman","year":"1988","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/0034-4257(88)90019-3","article-title":"An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data","volume":"24","author":"Chavez","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1080\/01431160600928559","article-title":"Haze removal for high resolution satellite data: A case study","volume":"28","author":"Moro","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1286","DOI":"10.1109\/36.628795","article-title":"Remote sensing of aerosol over the continents with the aid of a 2.2 \u00b5m channel","volume":"35","author":"Kaufman","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2490","DOI":"10.1109\/36.964986","article-title":"Atmospheric correction of Landsat ETM+ land surface imagery\u2014Part I: Methods","volume":"39","author":"Liang","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1080\/01431160500486690","article-title":"An automated atmospheric correction algorithm for visible\/NIR imagery","volume":"27","author":"Richter","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1109\/36.981363","article-title":"Haze detection and removal in high resolution satellite image with wavelet analysis","volume":"40","author":"Du","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.isprsjprs.2014.06.011","article-title":"An effective thin cloud removal procedure for visible remote sensing images","volume":"96","author":"Shen","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/0098-3004(96)00010-6","article-title":"Atmospheric correction of satellite data with haze removal including a haze\/clear transition region","volume":"22","author":"Richter","year":"1996","journal-title":"Comput. Geosci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5331","DOI":"10.1080\/01431160903369600","article-title":"Haze removal based on advanced haze-optimized transformation (AHOT) for multispectral imagery","volume":"31","author":"He","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8685","DOI":"10.1080\/01431161.2010.547884","article-title":"Haze detection, perfection and removal for high resolution satellite imagery","volume":"32","author":"Liu","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jiang, H., Lu, N., and Yao, L. (2016). A high-fidelity haze removal method based on HOT for visible remote sensing images. Remote Sens., 8.","DOI":"10.3390\/rs8100844"},{"key":"ref_24","unstructured":"Kauth, R.J., and Thomas, G.S. (July, January 29). The tasseled cap\u2014A graphic description of the spectral-temporal development of agricultural crops as seen in Landsat. Proceedings of the Landsat Symposium on Machine Processing of Remotely Sensed Data, West Lafayette, IN, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5540","DOI":"10.1109\/TGRS.2013.2290237","article-title":"A robust approach for object-based detection and radiometric characterization of cloud shadow using haze optimized transformation","volume":"52","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","unstructured":"United States Geological Survey (USGS) (2017, September 21). Landsat Data Archive. Global Visualization Viewer (GLOVIS), Available online: https:\/\/landsat.gsfc.nasa.gov\/data\/where-to-get-data\/."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.rse.2009.01.007","article-title":"Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+ and EO-1 ALI sensors","volume":"113","author":"Chander","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_28","unstructured":"Papadimitriou, C.H., and Steiglitz, K. (1998). Combinatorial Optimization: Algorithms and Complexity, Dover Publications, Inc."},{"key":"ref_29","unstructured":"Serra, J. (1984). Image Analysis and Mathematical Morphology, Academic Press, Inc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1109\/34.3918","article-title":"Efficient component labeling of images of arbitrary dimension represented by linear bintrees","volume":"10","author":"Samet","year":"1988","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1117\/12.487091","article-title":"Investigation of spectral screening techniques for independent component analysis based hyperspectral image processing","volume":"Volume 5093","author":"Robila","year":"2003","journal-title":"Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/10\/972\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:45:29Z","timestamp":1760208329000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/10\/972"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,21]]},"references-count":31,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2017,10]]}},"alternative-id":["rs9100972"],"URL":"https:\/\/doi.org\/10.3390\/rs9100972","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,9,21]]}}}