博碩士論文 89522055 詳細資訊




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姓名 呂維哲(Wei-Zhe Lu)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 模糊類神經網路為架構之遙測影像分類器設計
(A Neuro-fuzzy-based Approach to the Classification of Remotely Sensed Images)
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摘要(中) 遙測影像可以對地區進行規劃、自然資源的開發、環境監測、變遷偵測等提供很多資訊。這幾年來,大量的遙測影像取得並不困難,雖然分析者擅長辨識遙測影像的分類,但是常因為面對如此龐大的資料而不知所措。因此,大量的遙測影像並沒有真正地被有效應用,其原因在於沒有被有效的處理。基於這個理由,針對遙測影像發展一套自動化分類技術是必要的。在本論文中,我們首先使用多維度矩形複合式類神經網路來設計遙測影像分類器,透過足夠的訓練,可以直接由訓練樣本中萃取規則並以If-Then的方式表示出來,這些所萃取出的規則有助於分類的判斷使結果具有更高的可信度。此外,我們提出了一種新的分類器,稱為改良式簡化模糊適應共振理論映射圖(Modified Simplified Fuzzy Adaptive Resonance Theory Map,MSFAM),此網路是將模糊適應共振理論映射圖(Fuzzy Adaptive Resonance Theory Map,Fuzzy ARTMAP)作相當的簡化和改良而成。本論文最後以兩組遙測影像的資料集來驗證這兩種不同分類器的分類效果。
摘要(英) Remotely sensed images offer much information on planning or exploitation of natural resources, monitoring environmentally sensitive areas, detecting sudden changes of areas, etc. Over the years, an extremely large volume of remotely sensed images is currently available. Although human interpreters often are superior in identifying land-cover/land-use on remotely sensed images, they may be overwhelmed by the amount of data. Therefore, a substantial part of these images is not optimally used because it has not been properly indexed. For this reason, it is necessary to develop a technique to automatically classify remotely sensed images. In this thesis, we first report the application of a class of HyperRectangular Composite Neural Networks (HRCNNs) for classification of remotely sensed multi-spectral image data. After sufficient training, the classification knowledge embedded in the numerical weights of trained HRCNNs can be successfully extracted and represented in the form of If-Then rules. These extracted rules are helpful to justify their responses so the classification results can be more trustable. In addition, we propose a new class of classifiers called Modified SFAM (MSFAM). MSFAM is a modified and simplified version of the well-known Fuzzy ARTMAP. Two sets of remotely sensed images are used to verify the performance of the two different classes of classifiers.
關鍵字(中) ★ 監督式分類法
★ 影像分類
★ 遙測影像
★ 類神經網路
關鍵字(英) ★ remotely sensed images
★ Neural Networks
★ supervised classification
★ image classification
論文目次 圖目錄 III
表目錄 V
第一章 緒論 1
1.1研究動機 1
1.2論文架構 2
第二章 遙測影像 3
2.1遙測影像的特性 3
2.2衛星的介紹 4
2.2.1法國史波特衛星 4
2.2.2美國大地衛星五號 6
2.3遙測影像的分類方式 6
2.3.1監督式分類法 7
2.3.2非監督式分類法 8
2.3.3監督式卅非監督式混合方法論 8
第三章 分類器設計 10
3.1多維矩形複合式類神經網路 11
3.1.1網路架構 11
3.1.2 監督式決定導向學習演算法 12
3.1.3 模糊化 14
3.2模糊適應共振理論映射圖 17
3.2.1網路架構 17
3.2.2 模糊適應共振理論演算法 18
3.3簡化模糊適應共振理論映射圖 21
3.3.1網路架構 21
3.3.2監督式學習演算法 22
3.4模糊最小-最大分類器 24
3.4.1網路架構 24
3.4.2學習演算法 25
3.5 巢狀推廣範例系統 26
3.6.1模糊規則間的遞迴關係 28
3.6.2網路架構 30
3.7分類器設計之特性分析 31
第四章 改良式簡化模糊適應共振理論映射圖 35
4.1網路架構 35
4.2學習演算法 36
4.3網路測試 42
4.4 範例說明 43
4.5網路特點 47
4.6模擬 49
4.6.1二維資料─579 49
4.6.2二維資料─雙螺旋 52
第五章 實驗結果 58
5.1大地衛星多頻譜衛星影像 58
5.2空載多譜掃描儀DS-1260多頻譜遙測影像 65
第六章 結論和展望 83
第七章 參考文獻 86
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指導教授 蘇木春(Mu-Chun Su) 審核日期 2002-7-4
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