Abstract： Change detection is a standard tool to extract and analyze the earth＇s surface features from remotely sensed data. Among the different change detection techniques, change vector analysis （CVA） have an ex...Abstract： Change detection is a standard tool to extract and analyze the earth＇s surface features from remotely sensed data. Among the different change detection techniques, change vector analysis （CVA） have an exceptional advantage of discriminating change in terms of change magnitude and vector direction from multispectral bands. The estimation of precise threshold is one of the most crucial task in CVA to separate the change pixels from unchanged pixels because overall assessment of change detection method is highly dependent on selected threshold value. In recent years, integration of fuzzy clustering and remotely sensed data have become appropriate and realistic choice for change detection applications. The novelty of the proposed model lies within use of fuzzy maximum likelihood classification （FMLC） as fuzzy clustering in CVA. The FMLC based CVA is implemented using diverse threshold determination algorithms such as double-window flexible pace search （DFPS）, interactive trial and error （T＆E）, and 3x3-pixel kernel window （PKW）. Unlike existing CVA techniques, addition of fuzzy clustering in CVA permits each pixel to have multiple class categories and offers ease in threshold determination process. In present work, the comparative analysis has highlighted the performance of FMLC based CVA overimproved SCVA both in terms of accuracy assessment and operational complexity. Among all the examined threshold searching algorithms, FMLC based CVA using DFPS algorithm is found to be the most efficient method.展开更多
文摘Abstract： Change detection is a standard tool to extract and analyze the earth＇s surface features from remotely sensed data. Among the different change detection techniques, change vector analysis （CVA） have an exceptional advantage of discriminating change in terms of change magnitude and vector direction from multispectral bands. The estimation of precise threshold is one of the most crucial task in CVA to separate the change pixels from unchanged pixels because overall assessment of change detection method is highly dependent on selected threshold value. In recent years, integration of fuzzy clustering and remotely sensed data have become appropriate and realistic choice for change detection applications. The novelty of the proposed model lies within use of fuzzy maximum likelihood classification （FMLC） as fuzzy clustering in CVA. The FMLC based CVA is implemented using diverse threshold determination algorithms such as double-window flexible pace search （DFPS）, interactive trial and error （T＆E）, and 3x3-pixel kernel window （PKW）. Unlike existing CVA techniques, addition of fuzzy clustering in CVA permits each pixel to have multiple class categories and offers ease in threshold determination process. In present work, the comparative analysis has highlighted the performance of FMLC based CVA overimproved SCVA both in terms of accuracy assessment and operational complexity. Among all the examined threshold searching algorithms, FMLC based CVA using DFPS algorithm is found to be the most efficient method.
