不同影像分類方法應用於水稻辨識之探討

The Evaluation of Image Classification Methods for Rice Paddy Interpretation

簫國鑫(Kuo-Hsin Hsiao) ； 劉治中(Chi-Chung Lau) ； 徐偉城(Wei-Chen Hsu)

《航測及遙測學刊》 9卷1期 (2004/03) Pp. 13-26

https://doi.org/10.6574/JPRS.2004.9(1).2

摘要

本研究結合遙測多時影像與耕地資料，利用①NDVI 指數區間判釋法②貝氏機率法③統計機率法④物件導向模糊分類等方法辨識水稻圻塊分布。後三種分類方法完成對應坵塊被辨識為水稻的機率後，再轉換為硬式二分類別，以判釋該圻塊是否為水稻田，並比對航照判釋資料評估分類精度。研究結果顯示以兩幅1/5,000像片基本圖為測試範圍，評估二分類別辨識水稻的全體精度與k指標，以貝氏機率分類法最佳(96%.27%與0.92),NDVI指數區間判釋法次之(92.22%與0.83)，統計機率分類法再次之(91.89%及0.82),物件導向模糊分類法則為90.97%與0.81。前兩種分類方法推廣到苗栗、台南與屏東地區的水稻田判釋分類，貝氏機率分類的全體精度與k指標平均較NDVI指數區間判釋法高1~4%及0.04~0.15。所以利用貝氏機率分類除了可以得到坵塊被辨識為水稻的機率外，轉換為二分類別亦可得到較佳的分類結果。

關鍵字

水稻坵塊辨識 ； 遙測 ； 影像分類 ； 正規化差異植生指數

並列摘要

Multi-temporal imageries and cadastral GIS datasets were combined to interpret the rice paddy distributions, by applying the following means:(1) NDVI intervals approach on basis of multi-temporal SPOT images, (2) the Bayesian classifier based on spectral reflectance curve measured from different growth stages, (3) the statistic probability classifier, (4) the object oriented fuzzy classifier. Results of last three classification approaches were coupled with a prediction of probability. A selected threshold was applied to the results to obtain a dichotomy category. These results were compared with visual interpreting data from aerial photos for assessing accuracy. It shows that the overall accuracies and k index for the four means are 92.22℅&0.83, 96.27℅&0.92, 91.89%&0.82 and 90.97℅&0.81, respectively. Similar approach is applied to a contrast test site for the rice paddy in the first season. The results of the above-mentioned classifiers show overall accuracies and k index of 94.27℅&0.83, 95.43℅&0.87 and 92.75℅&0.79 that deducted from the object oriented fuzzy classifier. For the rice paddy in the second season, results of the above-mentioned three classifiers show overall accuracies and k index of 93.99%&0.79, 95.33℅&0.84 and 93.47℅&0.77. There are the same classified results that applied to Tainan and Pindong County for rice paddy interpretation. It is concluded that the Bayesian classifier not only can generate probability of each class, but also give better classified results after thresholding process.

並列關鍵字

Rice Paddy Interpreted ； Remote Sensing ； Image Classification ； Normalized Difference Vegetation Index (NDVI)