| Summary: | 碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 92 === Change detection in remote sensing has become an interesting topic of image processing. Conventionally, change detection in remote sensing depends on expert operators to visually analyze a series of multitemporal images. It usually takes a lot of human labor and time in order to achieve good performance. With development of computer technology, the work by human labor has gradually been accomplished by image processing. We still find it helpful to incorporate some operators’ domain knowledge into the early learning processing for successful change detection. For example, it depends on human operators for the selection of representative training samples in a supervised approach and the identification of change types resulting from an unsupervised approach. Nevertheless, an automatic change identification procedure is still in developing. In this study, a framework is proposed in order to fulfill the requirement of automation for change identification of remotely sensed images. First of all, a change detection process is performed to roughly discriminate between change and low-change areas based on textural and spectral features. Given a condition that class information in the first image is sufficient, those low-change samples are used for radiometric calibration and further provide class statistics for the second and subsequent images. By a post classification means, a series of multi-temporal images thus produce the change types with time. Our experimental results have demonstrated the feasibility of the proposed method.
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