多源异质遥感影像的分形特征分析

分形理论已经在影像压缩、分割、去噪等方面表现出了较大的优势,其特点是能够准确地描述影像的空间结构信息和变化规律。分别采用分线法和三角棱柱法对同一实验地区的多种分辨率不同波段的遥感卫星影像进行分析,分别计算了图像中不同地形区域(如城区、湖泊、田地等)的分形维数,并根据两种算法的特性对图像进行了特征分析。多源遥感影像的实验结果证明,分形维数确实可以作为影像特征分析的一个重要工具,并可以指导分析遥感影像的应用情况。此外,在计算分形维数的过程中,采用三折线拟合法自适应地确定影像的无标度区间,提高了分形维数计算结果的可靠性。     

基于分形的灰度图像压缩算法

简述了分形及随机迭代函数系统 (IFS)的基本概念 ,然后从灰度图像自动压缩的角度研究分形的应用 ,提出了多模式自适应压缩的技术 ,设计了建立在图像子块相对矩基础上的分类搜索算法 ,把简单分形压缩改进为自适应快速压缩 ,使恢复图像质量和压缩速度均有大幅度提高 ,并在压缩比为 4倍左右时 ,其结果接近于 JPEG静止图像压缩标准。     

JPEG 2000和JPEG-LS的实现及遥感影像压缩失真分析

JPEG 2000和JPEG-LS是目前应用比较广泛的两种压缩算法。本文介绍了JPEG 2000和JPEG-LS的编码流程、实现方法,通过试验分别分析了遥感影像在不同压缩比下的失真程度大小以及不同的失真类型。结果表明:在相同的中低倍压缩比条件下,JPEG 2000和JPEG-LS在影像细节信息保持方面效果非常接近;在高倍压缩比下,JPEG 2000和JPEG-LS压缩后影像分别出现了明显的模糊失真和间歇性失真现象。本文分析结论对相关算法的使用和改进有一定的借鉴意义。     

基于视觉掩盖效应的自适应图像压缩算法

通过分析压缩过程中图像失真的产生机制，提出了一种基于视觉掩盖效应的自适应压缩算法。由于图像是一种非平稳信源，算法采用分块小波变换，并根据图像块的纹理复杂度进行自适应分解，以适应图像不同区域的统计特性。与JPEG2000的对比实验表明，该算法在高倍率压缩时可有效地提高解压图像的视觉质量，较好地保留了图像中的细节及微弱信息。     

An Evaluation of Fractal Methods for Characterizing Image Complexity

Previously, we developed an integrated software package called ICAMS (Image Characterization and Modeling System) to provide specialized spatial analytical functions for interpreting remote sensing data. This paper evaluates three fractal dimension measurement methods that have been implemented in ICAMS: isarithm, variogram, and a modified version of triangular prism. To provide insights into how the fractal methods compare with conventional spatial techniques in measuring landscape complexity, the performance of two spatial autocorrelation methods, Moran's I and Geary's C, is also evaluated. Results from analyzing 25 simulated surfaces having known fractal dimensions show that both the isarithm and triangular prism methods can accurately measure a range of fractal surfaces. The triangular prism method is most accurate at estimating the fractal dimension of surfaces having higher spatial complexity, but it is sensitive to contrast stretching. The variogram method is a comparatively poor estimator for all surfaces, particularly those with high fractal dimensions. As with the fractal techniques, spatial autocorrelation techniques have been found to be useful for measuring complex images, but not images with low dimensionality. Fractal measurement methods, as well as spatial autocorrelation techniques, can be applied directly to unclassified images and could serve as a tool for change detection and data mining.     

基于FFT的星载SAR复数图像压缩及其码率控制算法

分析了成像后的星载SAR复数图像压缩的必要性及复数图像特点, 对已有压缩算法做了简单分析。在此基础上, 提出一种基于FFT变换和加权量化的星载SAR复数图像压缩算法, 同时建立了适合SAR复数图像压缩的码率控制模型, 提出一种精确码率控制方法。实验结果表明, 以幅值PSNR和平均结构相似度以及平均相位误差为性能指标, 提出的SAR复数图像压缩算法优于现有压缩算法(JPEG2000、H.264等); 同时, 提出的码率控制算法能够实现精确码率控制。     

Unified Lossy and Near-Lossless Hyperspectral Image Compression Based on JPEG 2000

We propose a compression algorithm for hyperspectral images featuring both lossy and near-lossless compression. The algorithm is based on JPEG 2000 and provides better near-lossless compression performance than 3D-CALIC. We also show that its effect on the results of selected applications is negligible and, in some cases, better than JPEG 2000.      

多尺度字典学习的高光谱图像压缩算法

结合小波变换及字典学习提出了一种针对高光谱图像的压缩算法。该算法首先通过小波变换构建多尺度样本集,在小波域使用K-均值奇异值分解(K-SVD)方法学习得到原子尺寸不同的多尺度字典,然后在稀疏表示的过程中,定义一个原子使用频次筛选因子,通过统计局部最优波段稀疏表示时原子使用情况,结合筛选因子对字典原子进行优化筛选,使用精简后的字典对其余波段进行稀疏求解,最后针对不同尺度的表示系数采用自适应的量化编码。实验结果表明,与目前常用的3D-SPIHT和其他的多尺度字典学习算法相比,本文算法在中低比特率下,具有更好的重建性能。     

Effects of jpeg 2000 compression on automated dsm extraction: evidence from aerial photographs

This paper evaluates the effects of JPEG 2000 compression on automated digital surface model (DSM) extraction, using the area-based matching technique. Two stereopairs of aerial photographs, which have different photo scale, pixel size, and topographic type, are used as the images for the experiments. In the experiments, the DSM generated from the uncompressed image is set as the reference, and the elevation accuracy is computed for a range of compression ratios (2:1 to 100:1). The results show that the employed indices of image quality based on the JPEG 2000 compression are clearly superior to the commonly used JPEG compression technique—especially for high compression ratios. However, the elevation accuracy varies with the compression ratios. Also, JPEG 2000 compression does not have a significant influence on the percentage of the successful matching rate. In addition, a near linear fall-off is observed in extracted DSM accuracy with increasing compression ratios.     

Fusion of multispectral and panchromatic Satellite images using the curvelet transform

A useful technique in various applications of remote sensing involves the fusion of different types of satellite images, namely multispectral (MS) satellite images with a high spectral and low spatial resolution and panchromatic (Pan) satellite image with a low spectral and high spatial resolution. Recent studies show that wavelet-based image fusion provides high-quality spectral content in fused images. However, the results of most wavelet-based methods of image fusion have a spatial resolution that is less than that obtained via the Brovey, intensity-hue-saturation, and principal components analysis methods of image fusion. We introduce an improved method of image fusion which is based on the amelioration de la resolution spatiale par injection de structures (ARSIS) concept using the curvelet transform, because the curvelet transform represents edges better than wavelets. Because edges are fundamental in image representation, enhancing the edges is an effective means of enhancing spatial resolution. Curvelet-based image fusion has been used to merge a Landsat Enhanced Thematic Mapper Plus Pan and MS image. The proposed method simultaneously provides richer information in the spatial and spectral domains.     

基于分形方法的图像压缩在WebGIS上的应用

万维网地理信息系统（WebGIS）是地理信息系统（GIS）发展的必然趋势，但在图像传输和保存方面还有很多尚等解决的问题。分形方法在图像压缩方面有很强优势，利用分形方法对地图图像进行压缩以减少数据量，在WebGIS上具有非常广阔的应用前景。     

Toward reduction of artifacts in fused images

Most fusion satellite image methodologies at pixel-level introduce false spatial details, i.e. artifacts, in the resulting fused images. In many cases, these artifacts appears because image fusion methods do not consider the differences in roughness or textural characteristics between different land covers. They only consider the digital values associated with single pixels. This effect increases as the spatial resolution image increases. To minimize this problem, we propose a new paradigm based on local measurements of the fractal dimension (FD). Fractal dimension maps (FDMs) are generated for each of the source images (panchromatic and each band of the multi-spectral images) with the box-counting algorithm and by applying a windowing process. The average of source image FDMs, previously indexed between 0 and 1, has been used for discrimination of different land covers present in satellite images. This paradigm has been applied through the fusion methodology based on the discrete wavelet transform (DWT), using the à trous algorithm (WAT). Two different scenes registered by optical sensors on board FORMOSAT-2 and IKONOS satellites were used to study the behaviour of the proposed methodology. The implementation of this approach, using the WAT method, allows adapting the fusion process to the roughness and shape of the regions present in the image to be fused. This improves the quality of the fused images and their classification results when compared with the original WAT method.     

基于遥感影像融合的不同权限信息隐藏盲算法

提出了一种基于遥感影像融合的不同权限机密信息隐藏盲算法,算法利用奇偶嵌入法和JPEG量化表,根据用户的不同权限进行融合影像中机密信息的不同程度的隐藏,且在提取机密信息和恢复原始融合影像时,不需要原始遥感影像,是一种盲算法。实验结果表明:算法具有强的透明性,大的信息隐藏量,并对各种常见的影像处理具有较强的鲁棒性。此外,算法对不同程度隐藏机密信息的遥感影像的各种应用均没有影响。     

Variogram Fractal Dimension Based Features for Hyperspectral Data Dimensionality Reduction

In this paper a new approach for fractal based dimensionality reduction of hyperspectral data has been proposed. The features have been generated by multiplying variogram fractal dimension value with spectral energy. Fractal dimension bears the information related to the shape or characteristic of the spectral response curves and the spectral energy bears the information related to class separation. It has been observed that, the features provide accuracy better than 90 % in distinguishing different land cover classes in an urban area, different vegetation types belonging to an agricultural area as well as various types of minerals belonging to the same parent class. Statistical comparison with some conventional dimensionality reduction methods validates the fact that the proposed method, having less computational burden than the conventional methods, is able to produce classification statistically equivalent to those of the conventional methods.     

Spectral unmixing based spatiotemporal downscaling fusion approach

Time-series remote sensing data are important in monitoring land surface dynamics. Due to technical limitations, satellite sensors have a trade-off between temporal, spatial and spectral resolutions when acquiring remote sensing images. In order to obtain remote sensing images with high spatial resolution and high temporal frequency, spatiotemporal fusion methods have been developed. In this paper, we propose a Linear Spectral Unmixing-based Spatiotemporal Data Fusion Model (LSUSDFM) for spatial and temporal data fusion. In this model, the endmember abundance of the low-resolution image pixel is calculated based on that of the high-resolution image by the spectral mixture analysis. The endmember spectrum signals of low-resolution images are then calculated continuously within an optimized moving window. Subsequently, the fused image is reconstructed according to the endmember spectrum and its corresponding abundance map. A simulated dataset and real satellite images are used to test the fusion model, and the fusion results are compared with a current spectral unmixing based downscaling fusion model (SUDFM). Our experimental work shows that, compared to the SUDFM, the proposed LSUSDFM can achieve better quality and accuracy of fused images, especially in effectively eliminating the “plaque” phenomenon in the results by the SUDFM. The LSUSDFM has great potential in generating images with both high spatial resolution and high temporal frequency, as well as increasing the number of spectral bands of the high spatial resolution data.     

遥感高时空融合方法的研究进展及应用现状

针对遥感图像的"时空矛盾",评述了当前解决这一问题最主要的方法即遥感时空信息融合的方法,包括基于变化模型的融合、基于重建模型的融合以及基于学习模型的融合。通过分析各个模型的研究现状,指出了每种模型方法的优劣,特别重点介绍了影响较大的自适应时空融合方法的理论以及对其的改进算法。同时本文总结了当前时空融合模型在长时间序列模拟以及大区域数据集生成等方面的实际应用的效果,以及分析了影响时空融合结果的主要因素。最后基于这些问题和影响因素提出了今后时空融合模型发展的目标和方向。     

面向土地利用分类的HJ-1 CCD影像最佳分形波段选择

环境一号卫星（HJ-1）CCD影像光谱波段较少,地物之间的准确分类识别有一定困难。采用分形纹理辅助地物分类识别是一种有效方法,而波段选择是提高分类识别精度的关键。本文以江西赣州定南县土地利用分类为例,采用双毯覆盖模型对HJ卫星CCD影像6类典型地物的波谱分形特征进行了分析,利用不同地物在不同波段上的分形区分度差异构建了最佳分形波段选择模型,并利用该模型挑选出最佳分形波段来辅助土地利用分类,最后对分类结果进行检验。结果表明：最佳分形波段选择模型能够综合权衡不同地物在不同波段上的分形区分度差异,利用挑选出来的最佳分形波段来辅助分类,其分类总体精度相对于原始影像分类提高了11.77%,相对于第1主成分分形辅助下的分类提高了1.56%。     

Utilizing the Potential of World View −2 for Discriminating Urban and Vegetation Features Using Object Based Classification Techniques

With the availability of very high resolution multispectral imagery, it is possible to identify small features in urban environment. Because of the multiscale feature and diverse composition of land cover types found within the urban environment, the production of accurate urban land cover maps from high resolution satellite imagery is a difficult task. This paper demonstrates the potential of 8 bands capability of World View 2 satellite for better automated feature extraction and discrimination studies. Multiresolution segmentation and object based classification techniques were then applied for discrimination of urban and vegetation features in a part of Dehradun, Uttarakhand, India. The study demonstrates that scale, colour, shape, compactness and smoothness have a significant influence on the quality of image objects achieved, which in turn governs the classified result. The object oriented analysis is a valid approach for analyzing high spatial and spectral resolution images. World View 2 imagery with its rich spatial and spectral information content has very high potential for discrimination of the less varied varieties of vegetation.     

Change detection method based on fractal model and wavelet transform for multitemporal SAR images

The interaction is quite complex between a ground object and an electromagnetic wave transmitted by synthetic aperture radar (SAR). In a ground resolution cell illuminated by a radar beam, there are many chaotic scatterers and the whole scattering echo has the chaotic characteristics which is usually described with the fractal theory, and the fractal dimension can be used to detect the change information for multitemporal SAR images. In order to improve the change detection effect with fractal model, this paper proposes a new multitemporal SAR image change detection algorithm based on the fractal model and wavelet transform (called FMWT algorithm). The FMWT algorithm has two advantages. One is insensitive to speckle noise; the other is that the change detection accuracy is improved, comparing with a general fractal change detection (GFCD) algorithm. Since the FMWT algorithm adopts the two-dimensional discrete stationary wavelet transform (TDDSWT) technique, it can obtain different direction sub-images and avoid down-sampling of the discrete wavelet transform (DWT). In the paper, not only the simulative data test has been carried out, but also the real natural disaster SAR images have been checked. Experimental results verify that the FMWT algorithm is feasible for multitemporal SAR image change detection, it is not sensitive to speckle noise of SAR images, and the performance of it is better than that of the GFCD algorithm. At the same time, the size of a sliding window will bring some affection in counting fractal dimensions.     

树模型的三维可视化研究

树的表达无论是在3DGIS，VRGIS还是在3DCM中，都是一个难点问题。与一般实体对象不同的是，树明显具有分形空间特征，因此用常规简单的建模方法很难表达一颗树。现在分形算法常用来生成树模型，具有逼真的可视效果，但所产生的数据量对于GIS环境来说太大，可视化困难；另外，用树的图像替代树模型也是一种常用的方法，但在近视点视觉效果差，同时也没有表达出树木所固有的空间特征。本文作者提出一种方法，使用LOD技术，将树的分形模型与其图像替代模型两者结合起来，初步解决树模型在GIS中的可视化问题，可达到逼真、高速的可视效果。