假設檢定及衛星遙測影像應用於地表覆蓋變遷偵測之研究

Abstract

遙測影像及技術已廣泛應用於土地利用分類和變遷偵測等環境監測。然而，過去隱含於這些變遷偵測中的不確定性卻未被完整提出。本研究提出二個以假設檢定為基礎的變遷偵測方法，雙變數聯合機率分配法及條件機率分配法，藉由訂定的顯著水準來處理變遷偵測的不確定性，二種方法皆要求一組以土地利用類別為基礎的未變遷像元資料做為假設檢定的基礎。 本研究以中台灣的德基集水區為例，所提出的研究方法在變遷偵測的整體正確率方面明顯優於其他二種普遍應用的分類後比較法及影像差值法。條件機率分配法考慮到前、後期影像光譜值間的相關性，以及在前期影像為已知、固定的情況下，考慮後期影像光譜值的變動範圍；同時，本研究也證明以土地利用類別為基礎的變遷偵測，對於精確的土地利用變遷偵測至關重要。 因此，從假設檢定的觀點來看，處理變遷偵測的不確定性，針對變遷偵測設定不同的顯著水準如1％、5％及10％，則偵測誤判為變遷的機率很低，可做為設定緊急處理區域的優先順序，應用於天然災害防災、救災管理時，提供緊急應變處理之參考。

Remote sensing images and technologies have been widely applied to environmental monitoring, in particular landuse/landcover classification and change detection. However, the uncertainties involved in such applications have not been fully addressed. In this paper two hypothesis-test-based change detection methods, namely the bivariate joint distribution method and the conditional distribution method, are proposed to tackle the uncertainties in change detection by making decisions based on the desired level of significance. Both methods require a data set of class-dependent no-change pixels to form the basis for class-dependent hypothesis test. Using an exemplar study area in central Taiwan, performance of the proposed methods are shown to be significantly superior to two other commonly applied methods (the post-classification comparison and the image differencing methods) in terms of the overall change detection accuracies. The conditional distribution method takes into consideration the correlation between digital numbers of the pre- and post-images and the effect of the known pre-image digital number on the range of the post-image digital number, and therefore yields the highest change detection accuracy. It is also demonstrated that the class-dependent change detection is crucial for accurate landuse/landcover change detection. Therefore, from a hypothesis test point of view, dealing with the uncertainty of change detection, change detection settings for different significant level such as 1%, 5% and 10%, then the probability of false positives is very low, can be used for making decision on disaster management.