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Land characteristics should be surveyed in an accurate and objective manner as they are important data to the formation of land prices. The ambiguous survey and estimation guidelines, however, make it possible for surveyors to intervene with their subjective judgments. There is thus a definite need for improvement. It is also extremely important to survey the characteristics of individual land lots in an accurate and objective manner in order to estimate officially assessed land prices in a swift and accurate way. For objective land appraisal and evaluation, this study set out to objectively identify the characteristics of lots according to various topographical conditions by using UAV based high quality geospatial information, panorama VR images, and GIS analysis technique and thus make a contribution to a rational and consistent estimation system of officially assessed land prices. Trying to assess the technique proposed in the study, the investigator analyzed the old data about the officially assessed land prices of the subject areas and then the evaluation data of certified public appraisers.

The main objective of this letter is to improve the accuracy of unsupervised change detection method and minimize registration errors among multi-temporal images in the change detection process. To this end, iteratively regularized multivariate alteration detection (IR-MAD) is applied to synthetically fused images. First, four synthetically fused hyperspectral images are generated using the block-based fusion method. Then, the IR-MAD is applied to three pairs of the fused images using integrated IR-MAD variates, to decrease the falsely detected changes. To focus on the mis-registration effects, we apply the method to both a correctly registered data-set and a data-set with deliberately misaligned images. In this experiment using multi-temporal Hyperion images, the changed areas are more efficiently detected by our method than by the original IR-MAD algorithm.