Review on different change vector analysis algorithms based change detection techniques

Detection of Earth surface changes is essential to monitor regional climatic changes, avalanche hazard analysis and energy balance studies that occurs due to air temperature anomalies. Geographic Information System (GIS) enables such research activities or procedures to carry out through change detection analysis. From this perspective, different change detection techniques have been developed for Land-Use Land-Covered (LULC) region. Among the various change detection techniques, Change Vector Analysis (CVA) has level headed capability of extracting maximum information in terms of overall magnitude of change and the direction of change between multi-temporal multi-spectral bands satellite datasets. Recently developed CVA techniques such as CVA in Posterior Probability Space (CVAPS), Cross Correlogram Spectral Matching (CCSM) based CVA, CVA using enhanced Principal Component Analysis (PCA) and Inverse Triangular (IT) Function, and Median CVA (MCVA), are effective LULC change detection tools. This paper presents a systematic survey on recently developed CVA algorithms along with their characteristics, features and shortcomings. This paper also summarized the necessary pre-processing steps such as geometric corrections, atmospheric corrections, radiometric corrections and topographic corrections for flat surface as well as rugged mountain terrain to correct the estimated spectral reflectance value. It is expected that this reviewed paper on different CVA techniques gives an effective guidance to algorithm designers for modifying and developing CVA based change detection techniques that effectively use the diverse and complex remotely sensed data for detection of flat as well as undulating surface changes.