運用影像分塊方法於高解析衛星影像土地利用判釋精度之研究

The Study on Classification Accuracy of Landuse Categories from High Resolution Satellite Images Using Image Segmentation Algorithm

李瑞陽(Re-Yang Lee) ； 莊佳文(Chia-Wen Chuang)

《航測及遙測學刊》 11卷4期 (2006/12) Pp. 403-415

https://doi.org/10.6574/JPRS.2006.11(4).4

摘要

在全面性土地利用調查研究中，以人力調查的方式，既耗時又不符合效益，若利用遙測影像進行自動化土地利用判釋，其正確率又無法滿足實務上需求。因在判釋上，傳統多採單一像元為基礎之影像分類演算法，但此法並未考慮相鄰像元間光譜反應的關係，因此無法準確判釋地物類別。
在影像判釋中，為了克服上述光譜分類問題，透過對影像進行分塊(Segmentation)，可得到較有意義的資訊。所以本研究於判釋衛星影像土地利用類別時，將運用分塊方法，來彌補他人方法上的不足，而分塊方法考量了灰階值、區塊大小以及相鄰影像之相關性，並引入兩種自動決定分塊門檻之方法，再撰寫程式萃取分塊資訊，經實證後，加入適合度法之分塊門檻成果，於後續判釋中，其分類整體準確度為84%，證明影像分塊確實可應用於土地利用判釋上。

關鍵字

影像分塊 ； 高解析衛星影像 ； 土地利用

並列摘要

In the research of land-use information survey and monitoring broad and simultaneously, traditional land surveying methods, such as field surveys, are time-consuming and costly. In the past, per-pixel classification algorithms were used for research, which did not consider about the location of pixel within the image and the relationship between its spectral response and neighborhoods. So, it can't classify land use categories accurately.
So, in this research on classification of land use categories from high resolution satellite images, we will use a segmentation method to improve image classification accuracy and make up the shortage on the other methods. In this method, on the one hand we considered three factors of gray level, pixel size, and the relationship between neighbor images, and on the other hand we use two automatic threshold selections for segmentation. Therefore, in this research, we utilized image segmentation to improve the classification results of land use categories from QuickBird satellite images. Then, we composed the program to extract the information in the image space for the application of practice. Finally, we obtained good image classification accuracy (overall=84 percent; Kappa coefficient=0.81) by using degree of fitting threshold selection for segmentation in QuickBird satellite images and segmented image automatically.

並列關鍵字

Image Segmentation ； High Resolution Satellite Image ； Landuse Categories