Independent two-step thresholding of binary images in inter-annual land cover change/no-change identification |
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| Institution: | 1. Laboratory of Embryonic Stem Cell Research, Department of Regeneration Science and Engineering, Institute for Frontier Life and Medical Sciences, Kyoto University, 53 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan;2. Institute for Integrated Cell-Material Sciences (iCeMS), Kyoto University, Yoshida-ushinomiya-cho, Sakyo-ku, Kyoto 606-8501, Japan;3. Division of Regenerative Medicine and Therapeutics, Department of Genetic Medicine and Regenerative Therapeutics, Institute of Regenerative Medicine and Biofunction, Graduate School of Medical Science, Tottori University, 86 Nishi-machi, Yonago 683-8504, Japan;4. Laboratory of Developmental Epigenome, Department of Regeneration Science and Engineering, Institute for Frontier Life and Medical Sciences, Kyoto University, 53 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan;5. Murdoch Children’s Research Institute, The RoyalChildren’s Hospital, Parkville, Victoria 3052, Australia;1. Graduate Schools of Medical Science and Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea;2. Department of Biological Sciences, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-701, Republic of Korea;3. Department of Pediatrics, Asan Medical Center Children’s Hospital, University of Ulsan College of Medicine, Seoul, Republic of Korea;1. Epigenetics Theme, Wisconsin Institute for Discovery, University of Wisconsin, 330 North Orchard Street, Room 2118, Madison, WI 53715, USA;2. Department of Cell and Regenerative Biology, University of Wisconsin, 1111 Highland Avenue, Madison, WI 53715, USA;1. Département d’urologie, CHU Tivoli, université libre de Bruxelles, Bruxelles, Belgique;2. Département de radiologie, CHU Tivoli, université libre de Bruxelles, Bruxelles, Belgique;3. Département d’urologie, cliniques universitaires de Bruxelles, hôpital Erasme, université libre de Bruxelles, route de Lennik 808, 1070 Bruxelles, Belgique;4. Département d’anatomopathologie, CHU Tivoli, université libre de Bruxelles, Bruxelles, Belgique;1. Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, TX 77555, USA;2. Sealy Center for Vaccine Development, University of Texas Medical Branch, Galveston, TX 77555, USA;3. Center for Biodefense and Emerging Infectious Diseases, University of Texas Medical Branch, Galveston, TX 77555, USA;4. Institute for Human Infections and Immunity, University of Texas Medical Branch, Galveston, TX 77555, USA;5. Department of Pathology, University of Texas Medical Branch, Galveston, TX 77555, USA |
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| Abstract: | Binary images from one or more spectral bands have been used in many studies for land-cover change/no-change identification in diverse climatic conditions. Determination of appropriate threshold levels for change/no-change identification is a critical factor that influences change detection result accuracy. The most used method to determine the threshold values is based on the standard deviation (SD) from the mean, assuming the amount of change (due to increase or decrease in brightness values) to be symmetrically distributed on a standard normal curve, which is not always true. Considering the asymmetrical nature of distribution histogram for the two sides, this study proposes a relatively simple and easy ‘Independent Two-Step’ thresholding approach for optimal threshold value determination for spectrally increased and decreased part using Normalized Difference Vegetation Index (NDVI) difference image. Six NDVI differencing images from 2007 to 2009 of different seasons were tested for inter-annual or seasonal land cover change/no-change identification. The relative performances of the proposed and two other methods towards the sensitivity of distributions were tested and an improvement of ～3% in overall accuracy and of ～0.04 in Kappa was attained with the Proposed Method. This study demonstrated the importance of consideration of normality of data distributions in land-cover change/no-change analysis. |
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| Keywords: | Binary images NDVI differencing Distribution normality Thresholding Change detection |
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