臺灣博碩士論文加值系統

摘 要
高頻譜衛星影像具有大量的頻譜資訊，提供了更精確的物體辨識，比其他類型的遙測影像資料更為豐富，因此廣泛的應用於偵測與地圖顯示的辨識上,像是地質學，大氣學、生態學、以及農業學等方面。但高頻譜影像的高解析度，相對的要處理的資料量也變的相當的龐大。如何在這樣龐大的資料量裡，能夠正確的達到目標物辨識，就變得相當重要。本論文針對高頻譜影像的資料模式做一簡單的分析，並提出一個結合訊號子空間投影法與部分濾波法來偵測目標物。
本論文首先取得影像資料頻帶間的相關性，利用此關聯性，將頻帶切割為數個群組，並提出以部分濾波器的合成方法來取代原本的濾波器偵測。接著，我們將部分濾波法結合訊號子空間投影法，藉由訊號與雜訊之分離，來抑除雜訊干擾，以提升目標物偵測之效能。
由模擬的結果顯示本論文所提之結合訊號子空間投影法與部分濾波法，除了可以改善高頻譜影像偵測之運算複雜度，也可以提高目標物偵測之效能。與現有的目標物偵測法，如Wiener濾波法或是雜訊子空間投影法比較，本論文所提之方法即使對於非訓練影像都有極佳的偵測效能。
關鍵字：高頻譜衛星影像、訊號子空間投影法、Wiener濾波器、雜
訊子空間投影法

Abstract

As new sensor technology has emerged over the past few years, hyperspectral images with hundreds of bands have become available. Increasing the number of spectral bands potentially provides more information and offers a better spectral resolution. Therefore, hyperspectral images have been extensively applying to target detection and classification. The positive effect of high resolution provided by hyperspectral imagery is diluted by the huge computations required in the data processing. One of the major issues of hyperspectral image classification is how to improve the accuracy of target detection with less computation complexity. In the thesis, we propose a novel classifier to detect and extract target signatures in unknown background by signal subspace projection (SSP) approach. The SSP-based classifier can be implemented as an adaptive filter combined by some partial filters.
We first analyze the correlations between the hyperspectral image bands. Some bands with highly correlated features are grouped into a small set. By the way, the hyperspectral image is partitioned into several groups. We then design a partial filter to extract the corresponding signatures for each image group by signal subspace projection approach. The SSP partial filter can be designed to extract or detect target signatures for each image group without no priori knowledge of signatures or background required. Finally, the SSP partial filters are combined to be a target detector, called a combined SSP partial filter (CSSPF). Simulations validate the proposed CSSPF can eliminate the interference efficiently and improve the accuracy of the target detection with less computation complexity than conventional target detection schemes. Generally, the proposed detector requires only 1/K2 computations of conventional Wiener filter, if image is partitioned into K groups.

Keyword ：Hyperspectral image、Signal subspace projection、Wiener
filter。

目錄
第一章 緒論.........................................11
1.1 簡介............................................11
1.2 研究背景與動機 .................................13
1.3 各章節內容概述 ...............................16
第二章 高頻譜影像簡介...............................17
2.1高頻譜影像簡介...................................17
2.2 AVIRIS 概念.....................................21
第三章 高頻譜影像資料模式及基本原理簡介.............27
3.1 線性混合模型....................................27
3.2 目標物偵測法之簡介..............................29
3.3 電腦模擬........................................37
第四章結合訊號子空間投影法和部份濾波之目標物偵測法..39
4.1 部份濾波法......................................40
4.2 訊號子空間投影法於目標物之偵測..................44
4.3 多目標特徵之目標物偵測..........................48
4.4 結合訊號子空間投影法和部份濾波偵測法............53
4.5 電腦模擬........................................55
4.6 討論............................................88
第五章 結論與建議...................................90
參考文獻............................................92

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