查詢結果分析
來源資料
相關文獻
- Classifications of Multisource Remote Sensing Images
- 整合遙測資訊於山區雨量推估
- 戰場動態電磁管理之雷截目標偵追融合研究
- Neural Network Procedures for Taguchi's Dynamic Problems
- A Fast and Efficient Competitive Learning Design Algorithm Based on Weight Vector Training in Transform Domain
- 專家系統振動訊號圖型判別之研究
- 反傳遞模糊類神經網路於流量推估之應用
- 類神經網路(Neural Networks)的種類及其在影像處理上的應用
- C++Fuzzy類神經網路物件導向發展系統之建立
- 臺灣汽保費率之估計--對數線性費率模式與類神經網路之比較
頁籤選單縮合
題 名 | Classifications of Multisource Remote Sensing Images=多源遙測影像之分類 |
---|---|
作 者 | 范國泰; 曾裕強; | 書刊名 | 聯大學報 |
卷 期 | 7:2 2010.12[民99.12] |
頁 次 | 頁279-298 |
分類號 | 440.98 |
關鍵詞 | 資料融合; 類神經網路; 多分類器系統; Data fusion; Neural networks; Multiple classifiers system; |
語 文 | 英文(English) |
中文摘要 | 同時使用由多個不同感測器所取得之遙測影像,預期可改善土地覆蓋分類的準確度。資料融合可利用多源資料間互補資訊的優點,而得到比僅用單一資料源更高的整體準確度,故尋求不同資料源間的互補資訊,是資料融合的一項非常重要的工作。本論文將採用多源分類器,以充分利用不同資料源間的互補資訊。但由於各資料源之單位不必然相同,因此當融合多源資料時,必須面臨資料尺度的問題。本論文採用兩種可克服資料尺度的問題的多源分類器(類神經網路及多分類器系統),並將其應用於多源遙測影像之分類,以展示並比較其分類效能。就多分類器系統而言,更以三種不同權重策略(均方根距離、平均距離、適應性閾值)來比較其分類效能。實驗結果顯示,類神經網路及多分類器系統均可大幅改善分類準確度,而多分類器系統的表現又優於類神經網路。此外,在多分類器系統中,適應性閾值權重策略則優於距離權重策略,且平均距離距離權重策略又略優於均方根距離權重策略。 |
英文摘要 | The use of remote sensing images from various sensors is supposed to be able to improve land cover classification accuracies. The important prospect of data fusion is focused on exploiting the complementary information among different sensors. Data fusion can take advantage of the use of complementary information to obtain a better overall accuracy than using single data source only. In this paper, multisouce classifiers are adopted to fully utilize the complementary information among different data sources. Because the multisource data are not necessarily in common units and therefore scaling problems may arise at fusing different sources of data. To overcome the scaling problems, two types of the multisouce classifier, neural networks and multiple classifiers systems, are introduced. The performances of utilizing the multisouce classifiers to the application of multisource remote sensing images classification are demonstrated and compared. For the multiple classifiers systems, three different weighting policies (rms distance weighting, average distance weighting, and adaptive thesholding) are examined. Experimental results show that both the neural networks and multiple classifiers systems approaches can dramatically improve the classification accuracy. In addition, the multiple classifiers systems approaches outperforms the neural networks approach. Besides, the classification performance of the adaptive thresholding multiple classifiers system is better than those of the distance weighted multiple classifiers systems. Moreover, the classification performance of the average distance weighting multiple classifiers system is slightly better than that of the rms distance weighting multiple classifiers system. |
本系統中英文摘要資訊取自各篇刊載內容。