變遷偵測在遙測影像處理的領域是一門有趣的課題。傳統上，遙測影像之變遷偵測仰賴專業人員對多時期影像做視覺化的分析，而為了要獲得較好的分析結果，常常需要耗費很多時間、精力在這些工作上。由於計算機技術的改良，像這類的工作逐漸被機器取代。我們也發覺，將專業人員的知識輸入早期的學習程序對變遷偵測是有幫助的。在非監督式的方法，變遷偵測的結果依然需要人員分析變遷的種類；而在監督式的方法，則需要人員選出可靠度高的訓練樣本。因此，我們提出一個自動化程度很高的變遷識別程序。假設第一時期的影像有類別資訊，我們可以先利用紋理與光譜特徵做變遷偵測，找出改變量較低的區域。然後，利用低改變量的樣本對影像做輻射校正，第二時期與後來的影像就有類別資訊可以使用。經過分類後，比較所有影像的分類結果就可得到類別的變遷資訊。由實驗結果可知我們提出的方法是可行的。

　　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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