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題名 | 動態學習類神經網路於遙測影像合併及變遷偵測之應用=Image Mosaic and Change Detection using Dynamic Learning Neural Network |
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作 者 | 陳漢臣; 曾裕強; | 書刊名 | 技術學刊 |
卷期 | 12:4 1997.12[民86.12] |
頁次 | 頁601-608 |
分類號 | 440.98 |
關鍵詞 | 輻射效應; 輻射校正; 影像合併; 變遷偵測; Radiation effect; Radiometric calibration; Image mosaic; Change detection; |
語文 | 中文(Chinese) |
中文摘要 | 因幅射效應造成不同時、空環境條件下的衛星影像區塊有不同的灰階值分佈。 此 現象會使得影像合併時,影像區塊接合處不連續。因此為了消除不連續的邊緣,在影像合併 前須先進行幅射校正。另外,對於影像之變遷偵測,也同樣地必須面臨幅射校正的問題。然 而,輻射較正是件相當困難的工作,以致影像合併與變遷偵測變得非常不易處理。本研究提 出以類神經網路來處理幅射效應的問題。由三個頻道 SPOT 影像的實驗結果發現,類神經網 路確實能處理影像合併與變遷偵測所遭遇之輻射效應的問題。結合影像合併與變遷偵測的結 果,可應用於土地利用之監測,它不但可以防止不適當的土地開發,而且對於國土之整體開 發與規劃極有助益。 |
英文摘要 | Satellite image blocks have different histograms under some temporal and spatial conditions due to radiation effects, and this phenomenom will result in a discontinuous edge between two adjacent an image blocks for an image mosaic. Therefore, to remove the discontinuous edge, a radiometric calibration has to be performed before the image mosaic. In addition, change detection applications also encounter the radiometric calibration problem. However, the radiometric calibration is so difficult that the tasks of image mosaic and change detection become intractable. In this study, we propose a novel approach that make use of the neural network to deal with the radiometric calibration problem. Experimental results obtained from processing the three bands SPOT images show that the neural network is capable of solving this problem in image mosaic and change detectin. Combining the image mosaic and change detection is very useful for land-use monitoring. It not only avoids the improper development of land, but is also helpful in countrywide development and planning of land. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。