基于整数小波变换的多光谱图像无损压缩

多光谱图像一般都采用预测方法去除空间冗余和谱内冗余实现无损压缩。通过用提升方法构造整数小波变换, 将变换方法用于去除空间冗余；通过分类方法构造谱间预测器，用预测方法去除谱间冗余，两者相结合，实现无损压缩。由于变换方法的去相关性能良好，使该方法压缩效果大大改善。     

基于误差补偿预测树的多光谱遥感图像无损压缩方法

预测树方法是一种有效的无损多光谱图像压缩技术,将自适应线性预测方法与传统预测树方法相结合,提出了一种多光谱遥感图像的误差补偿预测树压缩方法。该方法利用多光谱图像谱间的局部统计冗余和结构冗余建立自适应预测器,对传统预测树方法产生的误差进行补偿,从而进一步减少了多光谱图像的数据量;并且利用多光谱图像的局部平稳特性对算法进行了简化。实验结果表明,该方法得到的压缩比与原始预测树方法相比有明显提高,同时算法简化后可以使计算复杂度大幅度降低。     

基于KLT／TW和谱特征矢量量化三维谱像数据压缩

提出了基于ＫＬＴ／ＷＴ和谱特征矢量量化（ＳＦＣＶＱ）三维谱像数据压缩的新方法。在对多光谱图像数据进行Ｋａｒｈｕｎｅｎ－Ｌｅｏｖｅ变换（ＫＬＴ）消除谱相关性,再应用小波变换（ＷＴ）对ＫＬＴ后的多光谱图像数据进行消除空间相关性。采用ＳＦＯＶＱ编码对每个谱像数据进行压缩,获得较高的压缩性能。实验结果表明：ＫＬＴ／ＷＴ／ＳＦＣＶＱ方法和ＫＬＴ／ＷＴ／ＶＱ压缩方法比在同样压缩比（ＣＲ）条件下,峰值信噪比（Ｐ     

基于分类K—L变换的多波段遥感图像近无损压缩方法

去除空间和谱间相关性是多波段遥感图像压缩中的重要环节，为了得到更好的去相关效果，将矢量量化方法引入多波段遥感图像压缩中，以去除对应同一地物的波段矢量间的相关性，再通过分类K－L变换去除量化误差图像的变间相关性，对K－L变换后的特征图像采用预测树的方法进一步去除谱间结构相关性和空间相关性，实验结果表明，该方法可以取得很好的压缩效果。     

一种高光谱遥感影像无损压缩方法与应用

针对遥感影像巨大数据量给传输、存储造成巨大压力和压缩比低的问题,本文提出了一种改进的自适应波段重排和最小均方误差预测的高效无损压缩方法。该方法能自适应地确定波段的最佳顺序,并根据最小均方误差预测充分利用这种排序相关性消除影像冗余。首先,该方法对高光谱影像波段自适应分组,在每个组内利用最小生成树算法排序,以提升相邻波段的谱间相关性。然后,对组内波段自适应地选择上下文进行谱间和谱内预测,去除高光谱影像的冗余。最后,对预测残差进行二进制算术编码去除统计冗余,完成高光谱影像无损压缩。基于资源一号高光谱影像的试验结果表明,本文方法有效利用了谱内、谱间相关性,改善了预测性能,优于常用的压缩方法。     

基于KLT/WT和谱特征矢量量化三维谱像数据压缩

提出了基于KLT/WT和谱特征矢量量化 (SFCVQ)三维谱像数据压缩的新方法。在对多光谱图像数据进行Karhunen Leove变换 (KLT)消除谱相关性 ,再应用小波变换 (WT)对KLT后的多光谱图像数据进行消除空间相关性。采用SFCVQ编码对每个谱像数据进行压缩 ,获得较高的压缩性能。实验结果表明 :KLT/WT/SFCVQ方法和KLT/WT/VQ压缩方法比在同样压缩比 (CR)条件下 ,峰值信噪比 (PSNR)没明显变化 ,而速度提高了 30倍 ,比KLT/WT/FSVQ也提高了 5倍 ,整体压缩性能有较大的提高。     

基于小波包变换与IHS变换的遥感图像融合

针对多光谱图像与全色图像的融合,提出了基于IHS变换和小波包变换的遥感图像融合新方法.该方法首先对多光谱图像作IHS变换得到3个分量:亮度I、色度H和饱和度S;其次利用小波包变换融合方法融合多光谱图像的亮度分量与全色图像,并用融合后的图像替代多光谱图像的亮度分量;最后作IHS反变换得到新的多光谱图像.实验分析表明,新方法的性能优于IHS变换融合方法、小波变换融合方法和基于小波变换与IHS变换融合方法,在保留多光谱图像光谱信息的基础上,增强了融合图像的空间细节表现能力.     

基于小波包变换与IHS变换的遥感图像融合

针对多光谱图像与金色图像的融合，提出了基于IHS变换和小波包变换的遥感图像融合新方法。该方法首先对多光谱图像作IHS变换得到3个分量：亮度I、色度H和饱和度S；其次利用小波包变换融合方法融合多光谱图像的亮度分量与金色图像，并用融合后的图像替代多光谱图像的亮度分量；最后作IHS反变换得到新的多光谱图像。实验分析表明，新方法的性能优于IHS变换融合方法、小波变换融合方法和基于小波变换与IHS变换融合方法，在保留多光谱图像光谱信息的基础上，增强了融合图像的空间细节表现能力。     

基于HRIS光谱图像帧序列相关性的D2PCM无损压缩方法

分析了HRIS光谱图像帧序列的相关性，并针对这一特性，提出了利用D^2PCM方法，降低光谱图像序列的谱相关和空间相关，减少图像中的冗余，从而实现光谱图像的压缩。该方法提高了压缩比和压缩效率。     

河口表层悬浮泥沙气象卫星遥感定量模式研究

分析了HRIS光谱图像帧序列的相关性 ,并针对这一特性 ,提出了利用D2 PCM方法 ,降低光谱图像序列的谱相关和空间相关 ,减少图像中的冗余 ,从而实现光谱图像的压缩。该方法提高了压缩比和压缩效率 ,算法运算量小 ,快速。     

Sentinel-2A与Landsat 8O LI逐像元辐射归一化方法研究

考虑不同传感器光谱响应函数差异及不同地物类型反射率光谱的差异，提出了一种逐像元辐射归一化方法，并以2017年7月17日内蒙古达里诺尔湖地区准同步过境的Sentinel-2A及Landsat 8数据为例，对两类数据可见-近红外波段（VNIR）地表反射率结果进行归一化。首先采用Sen2cor方法及NASA官方提供大气校正算法，分别对Sentinel-2A及Landsat 8 OLI影像进行大气校正并重采样到同一空间分辨率；然后基于光谱库计算匹配因子并构建图像与光谱库之间的匹配转换模型，实现像元尺度上从Sentinel-2影像到Landsat 8影像地表反射率相似波段之间的转换。结果表明，经逐像元归一化的影像相比原始影像及经HLS光谱归一化的影像，与Landsat 8 VNIR波段的相关性明显提高，辐射一致性增强。该转换模型为多源中高分辨率遥感图像高精度辐射归一化提供了新思路。     

An Efficient Reordering Prediction-Based Lossless Compression Algorithm for Hyperspectral Images

In this letter, we propose an efficient lossless compression algorithm for hyperspectral images; it is based on an adaptive spectral band reordering algorithm and an adaptive backward previous closest neighbor (PCN) prediction with error feedback. The adaptive spectral band reordering algorithm has some strong points. It can adaptively determine the range of spectral bands needed to be reordered, and it can efficiently find the optimum branches. Hyperspectral images have a large number of spectral bands, which express the same land cover structure and have high correlation. The adaptive backward PCN prediction with error feedback can sufficiently make use of this correlation. Experiments show that implementing both the reordering of the spectral bands before prediction and the prediction with error feedback improve compression performance     

面向对象的遥感影像模糊分类方法研究

传统的基于像素的遥感影像处理方法都是基于遥感影像光谱信息极其丰富，地物间光谱差异较为明显的基础上进行的。对于只含有较少波段的高分辨率遥感影像，传统的分类方法，就会造成分类精度降低，空间数据的大量冗余，并且其分类结果常常是椒盐图像，不利于进行空间分析。本文采用面向对象的影像分类方法，考虑了对象的不同特征值，例如光谱值，形状和纹理，结合上下文关系和语义的信息，这种分类技术不仅能够使用影像属性，而且能够利用不同影像对象之间的空间关系。在对诸多对象进行分类后，再进行精度分析。在此研究提出了一种面向对象的方法结合模糊理论把许多的对象块分成不同的类别。这一过程主要有两个步骤：第一个步骤是分割。图像分割将整个图像分割成若干个对象，在这个过程中，分割尺度的选择会影响到后续的分类结果和精度。第二个步骤是分类。在这个步骤中，特征值的选择和隶属度函数的选择都对分类结果有着至关重要的影响。     

Fractal compression of satellite images

Fractal geometry provides a means for describing and analysing the complexity of various features present in digital images. In this paper, characteristics of Fractal based compression of satellite data have been tested for Indian Remote Sensing (IRS) images (of different bands and resolution). The fidelity and efficiency of the algorithm and its relationship with spatial complexity of images is also evaluated. Results obtained from fractal compression have been compared with popularly used compression methods such as JPEG 2000, WinRar. The effect of bands and pixel resolution on the compression rate has also been examined. The results from this study show that the fractal based compression method provides higher compression rate while maintaining the information content of RS images to a great extent than that of JPEG. This paper also asserts that information loss due to fractal compression is minimal. It may be concluded that fractal technique has many potential advantages for compression of satellite images.     

Detail focused fusion of hyperspectral and multispectral imagesEI北大核心CSCD

Hyperspectral image (HSI) and multispectral image (MSI) are two types of images widely used in the field of remote sensing. These images are useful in certain applications, such as environmental monitoring, target detection, and mineral exploration. HSI contains a large amount of spectral information. Photons are typically collected in a larger spatial area on the sensor to ensure a sufficiently high signal-to-noise ratio (SNR). Accordingly, the HSI spatial resolution is much lower compared with MSI. This low spatial resolution greatly affects the practicality of HSI. Accordingly, fusing a low-spatial resolution HSI (LR-HSI) with a high-spatial resolution MSI (HR-MSI) in the same scene to obtain a high-resolution HSI (HR-HSI) is a method for solving such problems, which resolves the contradiction that the spatial resolution and the spectral resolution cannot simultaneously maintain a high level. From the analysis of fusion effect, the spatial and spectral reconstruction errors of the existing algorithms are mainly reflected in the edge and detail areas. The method proposed in this work was a fusion algorithm for dictionary construction and image reconstruction based on detail attention. In terms of maintaining spectral characteristics, the spectral distribution in the detail area is complex and diverse because of the proximity effect of the image. This work proposes to perform dictionary learning on the image and detail layers. The detail perception error terms and a constraint of edge adaptive directional total variation are proposed for spatial characteristic enhancement, which is combined with a local low rank constraint in the same fusion framework to estimate the sparse coefficient. Experiments were conducted on two datasets, namely, Pavia University and Indian Pine, to verify the effectiveness of the proposed method. The quantitative evaluation metrics contain peak SNR, relative dimensionless global error in synthesis, spectral angle map, and universal image quality index. Based on the experimental comparison, the fusion result of the algorithm proposed in this work is significantly improved compared with those of the other algorithms in terms of spatial and spectral characteristics. This work uses dictionary learning to propose a fusion algorithm for dictionary construction and image reconstruction with attention to details through the analysis of the existing hyperspectral and multispectral image fusion algorithms. A hierarchical dictionary learning algorithm is proposed to address the problem of large reconstruction error in the detail part of the existing algorithms. The detail perception error term and the direction adaptive full variational regularization term are used to improve the spectral dictionary solution and coefficient estimation, respectively. The result of the fusion is the error in the spectral characteristics and spatial texture of the detail, which achieves an accurate representation of the edge detail. © 2022 National Remote Sensing Bulletin. All rights reserved.     

空间与谱间相关性分析的NMF高光谱解混

非负矩阵分解(NMF)技术是高光谱像元解混领域的研究热点。为了充分利用高光谱图像中丰富的空间与光谱相关性特征,改善基于NMF的高光谱解混算法性能,提出一种结合了空间与谱间相关性分析的NMF解混算法。算法针对NMF的通用性和局部极小问题,引入并结合高光谱图像两种典型的相关性特征,具体包括:基于马尔可夫随机场(MRF)模型,建立描述相邻像元空间相关特征的约束;通过复杂度映射技术,建立描述相邻波段谱间相关(光谱分段平滑)特征的约束;并将上述两种约束同时引入NMF解混目标函数中。实验结果表明,对于一般自然地物场景或人造地物场景,相对于分段平滑和稀疏约束的非负矩阵分解(PSNMFSC)、交互投影子梯度的非负矩阵分解(APSNMF)和最小体积约束的非负矩阵分解(MVCNMF)这3种代表性NMF解混参考算法,该算法可进一步提高高光谱解混精度;对于空间相关或谱间相关特征中某一种不显著的特殊场景,也具有更好的适应能力。通过将空间相关和谱间相关特征相结合,较全面地反映了高光谱数据与解混相关的重要特征,能够对绝大多数真实高光谱数据进行高精度解混,对高光谱解混及后续应用领域相关研究均具有参考价值。     

Hue Adjustment to IHS Pan-Sharpened IKONOS Imagery for Vegetation Enhancement

Fused or pan-sharpened IKONOS images are invaluable to the visual interpretation of large-area-scale applications. Frequently, the paramount objective is merely to obtain an image for visualization purposes, with no further image processing or analysis in mind. In such cases, the fused image should resemble reality as much as possible, with objects represented by their true colors at surface reflectance. In this letter, a technique for image fusion of IKONOS, or similar imagery, is proposed for when the main purpose of a specific image is vegetation visualization. The technique consists of a hue spectral adjustment scheme integrated with an intensity-hue-saturation transformation. Experimental evidence to support the technique is provided with a subset of IKONOS data available from the University of Alcalaacute campus. In the final fused image, deciduous and evergreen vegetation can be clearly differentiated. The high quality of the fused image can also be appreciated in relation to other types of cartographic objects; the quantitative evaluation of spectral and spatial information results in a high color correlation and excellent spatial information quality