小波变换用于高分辨率全色影像与多光谱影像的融合研究

将小波的多分辩率分析与ＩＨＳ变换相结合,提出了叠加融合的新方法。它先对高分辨率影像进行了小波分解,得到的各小波面叠加到多光谱影像经ＩＨＳ变换后的强度Ｉ影像像中,使得融合影像最大限度地保留了多光谱影像的光谱信息,保持了原多光谱影像的反差,同时提高了它的清晰度和空间分辨率。     

选择最佳彩色变换用于遥感影像复合的定量评价方法

本文采用四种彩色变换对空间分解力为１０ｍ的航片数字化影像和同一地区的ＬａｎｄｓａｔＴＭ（２，３，４）三波段影像进行了复合，并提出了熵，联合熵和平均梯度指标评价复合效果。     

SAR与TM影像的IHS变换复合及其质量定量评价

本文采用四种典型的IHS变换方法,对同一地区的SAR与TM影像进行了复合,并对复合后图像的信息量及清晰度作了定性和定量分析。结果表明,用熵、联合熵和平均梯度这三个定量指标客观评价SAR与TM影像的复合效果,比目视评价结果准确有效,从而为选择最佳IHS变换方法进行多源遥感数据复合提供了依据。     

遥感影像融合方法的精度评价

目前,高分辨率全色遥感影像和低空间分辨率的多光谱遥感影像融合是影像融合技术应用的主流.EN-VI 4.4遥感影像处理软件影像融合处理的工具--SPEAR提供了PCA变换、Gram-Schmid变换、Brovey变换和HSV变换4种专门用于全色与多光谱遥感影像融合的算法.以ETM+全色与多光谱遥感影像融合为例,选择熵、偏差和相关系数3个定量指标,对采用4种融合方法得到的影像进行评价.经综合评价和比较,实验的4种影像融合算法中,Gram-Schmidt变换效果最好.     

多源遥感影像融合效果的定量评价研究

在分析总结当前常用的遥感影像融合结果定量评价方法的基础上,本文描述了亮度信息,清晰程度,光谱信息,信息量等定量评价参数,并编程实现了这些评价指标的计算。以航空SAR影像和Landsat TM多光谱影像融合作为实验数据,进行了基于Brovey变换,HIS变换和小波变换方法的影像融合,并对结果进行了定量评价分析。结果表明,所提出的定量评价参数能够较准确地反映影像融合情况,可为选择恰当的融合方法提供科学依据。     

利用快速离散Curvelet变换的遥感影像融合

提出了一种基于快速离散Curvelet变换的遥感影像融合方法。首先,对经过空间配准的多光谱和全色影像分别进行快速离散Curvelet变换。然后,对低频子带采用局部标准差加权策略,对中高频子带采用绝对值最大策略,对高频子带采用直接替换策略,反变换后即可得到融合影像。IKONOS、QuickBird、World-View-2多光谱和全色影像的融合实验和定量评价结果表明,该方法较传统方法有明显优势。     

小波变换在SAR与TM图像的IHS变换复合方法中的应用

提出了一种改进的 IHS变换复合方法 ,即将小波变换与 IHS变换相结合的方法。它先将 SAR与 I进行小波融合 ,然后将融合数据代替 IHS反变换中的 I,最后进行 IHS反变换。文中对比分析了 4种复合方法 ,并且用熵、联合熵及平均梯度进行了定量评价。     

基于像元的遥感影融合方法的比较

阐述了融合ＴＭ和航片数字化影像的ＨＩＳ变换、主分量分析和高通滤波三种方法，并定性和定量地比较了三种方法融合的影像。结果表明：ＨＩＳ方法导致影像数据的光谱特性变化最大；主分量分析法居中；高通滤波法导致光谱特性变化最小。     

基于HIS和小波变换的IKONOS影像融合

许多对地观测卫星能提供高分辨率的全色波段影像和低分辨率的多光谱影像,因而影像融合已成为遥感图像空间分辨率提高的一个重要工具.目前多种影像融合技术已发展起来,然而对于高分辨率的IKONOS影像,现有的算法很难产生满意的融合效果.本文提出了一种新的融合方法,结合了HIS变换和小波变换的优势来减弱IKONOS融合中的光谱扭曲.定量评价证明HIS和小波相结合的融合方法相比常规的HIS变换和小波变换在提高融合质量上有着重要意义.     

基于GF-2影像融合方法的棉花种植面积提取

首先,针对研究区GF-2影像进行Brovey变换、G-S变换、NNDpansharp变换、PC变换4种融合,对融合结果进行定量评价;其次,利用随机森林分类方法对研究区作物进行分类,并进行精度验证,提出了研究区域农作物信息。结果表明：1)对研究区进行4种方法融合,提高遥感影像分辨率;2)从评价结果可知,4种融合影像中,NNDpansharp融合影像质量最佳。分类结果说明,NNDpansharp融合影像的随机森林分类总精度和Kappa系数最高,该方法和结果可为农业部门将高分二号遥感影像融合提取棉花面积方法提供选择性参考。     

隐伏煤田的TM信息提取及地质效果

在遥感图像上,被巨厚第四系冲积层覆盖的煤田区隐伏地质构造和火成岩分布信息是一种非直接的、微弱的隐伏信息,本文以淮北煤田为实例,探讨了从TM图像上提取这种信息的可行性与方法,并分析了其地质效果。     

基于IHS变换的最优树分解在遥感图像融合中的应用

探讨了遥感多光谱与全色波段图像的融合问题，分析了基于IHS变换的小波包变换分解的遥感图像融合方法，提出了基于最优树分解的融合方法。此方法首先将多光谱图像进行IHS变换，然后对I分量和全色图像进行小波包分解和最优树分解，再进行融合，最后进行IHS 逆变换得到融合图像。此方法不仅得到较好的图像主观视觉效果，而且兼顾了客观上熵最大的原则。     

遥感影像像素级融合方法比较研究

遥感影像数据的融合对于利用影像进行的分类、特征提取和目标识别具有重要的意义。文中阐述了IHS彩色空间变换融合法、主成分分析法（PCA）、Brovey法及Gram-schmidt法的算法实现,在此基础上对QuickBird全色波段和多光谱波段进行融合实验,最后从信息熵、灰度均值、相关系数、标准差和视觉效果5个方面综合进行定量与定性评价。分析结果表明,IHS整体上清晰,色调协调,保留了较多的空间信息,细节特征明显,质量较好。     

FFT-enhanced IHS transform method for fusing high-resolution satellite images

Existing image fusion techniques such as the intensity–hue–saturation (IHS) transform and principal components analysis (PCA) methods may not be optimal for fusing the new generation commercial high-resolution satellite images such as Ikonos and QuickBird. One problem is color distortion in the fused image, which causes visual changes as well as spectral differences between the original and fused images. In this paper, a fast Fourier transform (FFT)-enhanced IHS method is developed for fusing new generation high-resolution satellite images. This method combines a standard IHS transform with FFT filtering of both the panchromatic image and the intensity component of the original multispectral image. Ikonos and QuickBird data are used to assess the FFT-enhanced IHS transform method. Experimental results indicate that the FFT-enhanced IHS transform method may improve upon the standard IHS transform and the PCA methods in preserving spectral and spatial information.     

不同遥感影像融合方法的质量评价

近年来,随着遥感技术的迅猛发展,多源遥感影像数据越来越多,有效利用这些海量遥感数据是遥感领域的一大热门话题,同时,对融合后的遥感影像进行质量评价使融合处理过程得到优化,也是影像融合中必不可少的一个重要环节。重点利用ERDAS软件,分别采用Brovey变换法、IHS变换法、高通滤波法以及主成分分析法对影像进行融合实验,并对融合后的影像进行主观评价;采用Matlab编写程序,提取了融合影像的定量评价指标均值、标准差、平均梯度、信息熵和相关系数,完成了对融合影像的客观评价;最后对融合后的遥感影像进行了对比分析,得出主成分分析法的影像在光谱保持特性上最好,Brovey法在信息量的保持和清晰度上都优于其他的融合方法,而通过IHS变换法的融合影像与原始影像的相关性最好的结论。     

彩色变换及其在浙江括苍山地区的应用研究

本文利用色度坐标,研究了IHS彩色变换,提出并试验了一种便于在遥感数字图像处理中应用的变换式,在浙江括苍山地区试验中,取得了令人满意的结果。试验表明:IHS变换不仅为图像彩色增强提供了一种有效的新方法,而且为各种遥感与非遥感图像的复合和综合显示开辟了一条理想的新途径。     

Landslide inventory using image fusion techniques in Brazil

The present work aims to assess the accuracy of six fusion techniques (Brovey, IHS, HSV, PCA, WTYO and WTVE) in order to compile landslide inventories using orbital images (ETM+ and PAN HRV). The study area is characterized by steep terrain and dense forest in Caraguatatuba, São Paulo State, Brazil. In terms of spatial quality, the Wavelet Transform technique provided the best results, presenting correlations above 90%. As for spectral quality, the best results were obtained with the IHS fusion. Based on the results, it may be concluded that the IHS is the best technique for preserving spatial and spectral information from the original images, so as to more clearly identify landslide scars. However, it was still not possible to typify the landslides from remote sensing data. Nonetheless, it is believed that image fusion techniques adequately met expectations in terms of their capacity to identify landslide for the creation of an inventory for the studied area.     

A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques

Normally, to detect surface water changes, water features are extracted individually using multi-temporal satellite data, and then analyzed and compared to detect their changes. This study introduced a new approach for surface water change detection, which is based on integration of pixel level image fusion and image classification techniques. The proposed approach has the advantages of producing a pansharpened multispectral image, simultaneously highlighting the changed areas, as well as providing a high accuracy result. In doing so, various fusion techniques including Modified IHS, High Pass Filter, Gram Schmidt, and Wavelet-PC were investigated to merge the multi-temporal Landsat ETM+ 2000 and TM 2010 images to highlight the changes. The suitability of the resulting fused images for change detection was evaluated using edge detection, visual interpretation, and quantitative analysis methods. Subsequently, artificial neural network (ANN), support vector machine (SVM), and maximum likelihood (ML) classification techniques were applied to extract and map the highlighted changes. Furthermore, the applicability of the proposed approach for surface water change detection was evaluated in comparison with some common change detection methods including image differencing, principal components analysis, and post classification comparison. The results indicate that Lake Urmia lost about one third of its surface area in the period 2000–2010. The results illustrate the effectiveness of the proposed approach, especially Gram Schmidt-ANN and Gram Schmidt-SVM for surface water change detection.     

An ensemble pansharpening approach for finer-scale mapping of sugarcane with Landsat 8 imagery

We tested the effects of three fast pansharpening methods – Intensity-Hue-Saturation (IHS), Brovey Transform (BT), and Additive Wavelet Transform (AWT) – on sugarcane classification in a Landsat 8 image (bands 1–7), and proposed two ensemble pansharpening approaches (band stacking and band averaging) which combine the pixel-level information of multiple pansharpened images for classification. To test the proposed ensemble pansharpening approaches, we classified “sugarcane” and “other” land cover in the unsharpened Landsat multispectral image, the individual pansharpened images, and the band-stacked and band-averaged ensemble images using Support Vector Machines (SVM), and assessed the classification accuracy of each image. Of the individual pansharpened images, the AWT image achieved higher classification accuracy than the unsharpened image, while the IHS and BT images did not. The band-stacked ensemble images achieved higher classification accuracies than the unsharpened and individual pansharpened images, with the IHS-BT-AWT band-stacked image producing the most accurate classification result, followed by the IHS-BT band-stacked image. The ensemble images containing averaged pixel values from multiple pansharpened images achieved lower classification accuracies than the band-stacked ensemble images, but most still had higher accuracies than the unsharpened and individual pansharpened results. Our results indicate that ensemble pansharpening approaches have the potential to increase classification accuracy, at least for relatively simple classification tasks. Based on the results of the study, we recommend further investigation of ensemble pansharpening for image analysis (e.g. classification and regression tasks) in agricultural and non-agricultural environments.