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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/28083

Title: Adaptive Filtering Approaches to Multispectral Image Classification
Authors: Lena Chang;Fu-Chuan Ni
Contributors: NTOU:Department of Communications Navigation and Control Engineering
國立臺灣海洋大學:通訊與導航工程學系
Keywords: multispectral image;image classification;adaptive signal subspace projection(ASSP);eigen-features;artificial neural network (ANN)
Date: 2007-12
Issue Date: 2011-10-21T02:35:49Z
Publisher: Journal of Photogrammetry and Remote Sensing
Abstract: abstract:In the study, two adaptive classifiers based on image eigen-features are proposed for multispectral image classificationm. One is based on a liner filter with weights adaptively updated by the principal eigencompoments, and the other is an artificial neural network (ANN) with weights trained by the image eigen-features. We first propose an adaptive signal subspace projection (ASSP) approach to detect and extract target signatures in unknown background. The weights of ASSP are adjusted adaptively by using the eigen-features which are updated recursively by the adaptive eigen-decomposition algorithm. Then, we proposed an ANN classifier based on back propagation multilayer perception (BPMLP) with weights trained by the image eigen-features. Simulation results validate the image eigen-features can alleviate the noise effect in classification and the proposed ASSP and ANN classifiers have lower detection error and fast convergence rate than conventional Wiener filter and per-pixel ANN methods.
摘要:本研究針對多頻帶影像分類提出以影像特徵爲基礎之適應性濾波法。首先考慮以線性濾波器的架構進行影像分類,提出了適應性信號子空間投影法(Adaptive Signal Subspace Projection, ASSP),此法可於未知背景訊號情況下偵測目標物。ASSP使用適應性特徵分解演算法進行特徵值及特徵向量的遞迴式更新,藉此,達到適應性影像分類之目的。接著,利用萃取的特徵資料作爲倒傳遞類神經網路的輸入,以改善類神經網路的收斂速度及影像分類的效能。論文中,我們提出以特徵值結合特徵向量之權重化特徵向量法,可改善多頻譜影像目標物之偵測及分類之效能。 模擬結果顯示,適應性信號子空間投影法及以權重化特徵向量爲輸入之類神經網路在影像分類上能夠有效的減少雜訊效應,與傳統的韋恩濾波器法及以像素輸入法爲基礎之倒傳遞網路相比,論文提出的適應性濾波法具有較低的錯誤偵測率及快速的收斂速度。
Relation: 12(4), pp.343-351
URI: http://ntour.ntou.edu.tw/handle/987654321/28083
Appears in Collections:[通訊與導航工程學系] 期刊論文

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