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摘 要

多源遙感圖像融合是圖像融合中的一個(gè)重要分支部分?，F(xiàn)代信息時(shí)代的今天，多源遙感圖像融合已成為圖像內(nèi)容和圖像處理領(lǐng)域中必不可少的技術(shù)，它在很多軍用的方面和民用方面有著舉足輕重的地位。

    隨著傳感器技術(shù)的發(fā)展，遙感技術(shù)為對(duì)地觀測(cè)提供的遙感圖像數(shù)據(jù)越來越豐富。遙感圖像融合技術(shù)可以將多源遙感數(shù)據(jù)所包含的信息優(yōu)勢(shì)或互補(bǔ)性有機(jī)地結(jié)合起來，并且能有效的提高遙感圖像的空間分辨率，增強(qiáng)圖像的特征性，提高分類精度和動(dòng)態(tài)監(jiān)測(cè)的能力。本文介紹了圖像處理的幾種方法，研究了遙感圖像的融合方法。對(duì)圖像的數(shù)據(jù)進(jìn)行融合處理可以提高圖像的分辨率、圖像的準(zhǔn)確度和置信度。本文的主要研究內(nèi)容和工作總結(jié)如下：

1、介紹了圖像融合中的基本原理，研究了圖像預(yù)處理過程圖像增強(qiáng)、圖像配準(zhǔn)等問題，為開展圖像融合做準(zhǔn)備。

2、研究了傳統(tǒng)的圖像融合方法：IHS變換和PCA變換，將兩種融合方法應(yīng)用到Landsat 8衛(wèi)星多光譜與全色圖像融合之中。研究結(jié)果表明：經(jīng)過兩種融合方法后融合圖像更加清晰，分辨率明顯提高，圖像的信息量更加豐富。從相關(guān)系數(shù)、偏差定量指標(biāo)來看PCA變換優(yōu)于HIS變換。從平均梯度、交叉熵和熵定量指標(biāo)來看IHS變換優(yōu)于PCA變換。

3、針對(duì)IHS變換和PCA變換融合方法存在的光譜扭曲，圖像的清晰度不夠等問題，研究了基于靜態(tài)小波變換（SWT）的圖像融合方法。結(jié)果表明：從相關(guān)系數(shù)、信息熵、偏差和交叉熵定量指標(biāo)來看，SWT變換明顯優(yōu)于IHS、PCA變換。

關(guān)鍵詞：圖像融合、遙感、IHS變換、PCA變換、質(zhì)量評(píng)價(jià)

ABSTRACT

Multi-source remote sensing image fusion is an important part of image fusion technology, and it is essential for image content preservation and image analysis. It has frequently been utilized in military and civil fields.

With the development of remote sensing sensor technology, remote sensing has provided more and more abundant remote sensing image data so as to observe earth surface. Image fusion technique can combine multi-source information, and can effectively improve the spatial resolution and enhance image features. Therefore, it can improve classification accuracy and promote dynamic monitoring. This paper describes image pre-processing and several image fusion methods. The summary is as follows:

1. The paper introduced the basic theory about image enhancement, image registration and image fusion.

2. The paper studied traditional image fusion method, such as IHS transform and PCA transform, and then utilized them to Landsat 8 multi-spectral and panchromatic image fusion processing. The experimental results had shown that the pan-sharpened images were clearer than original multi-spectral images, moreover, they had higher spatial resolution and more abundant image information. From correlation coefficient and deviation quantitative indexes point of view, it was shown that PCA transform was better than HIS transform, and from mean gradient, cross entropy and entropy indexes point of view, the IHS transform was better than PCA transform.

3. For solving spectral distortion introduced by IHS transform and PCA transform, and not enough image definition, the static wavelet transform (SWT) was proposed in this paper. The experimental results had shown that from correlation coefficient, information entropy, deviation and cross entropy indexes point of view, SWT transform was obviously better than IHS and PCA transform.

Keywords: image fusion, remote sensing, IHS transform, PCA transform, quality assessment.