静态图像的格式转换与SPT

探讨了静态图像获取及格式转化的几种途径,介绍了DOS下图像格式转换软件HIJAAK,分析了PhotoStyler、STP各自的优势,提出了一个普遍意义的做法.     

VGA／TVGA从中心向周边特技显示256色图像

在计算机软件开发中,图形图像的应用越来越多,许多文章介绍过一些图像格式,如PCX、TIF、GIF、BMP等,并对其格式的特点进行了分析,同时也有大量的软件可以进行格式的相互转换,如Windows下的图像处理系统PhotoStyler、ImageStart等。所以本文仅以VGA200×300×256及TVGA800×600×256或TVGA1024×256方式下TIF256色图像为例,介绍图像的艺术显示效果的编程方法。     

静态图像的格式转换与SPT

本文探讨了静态图像获取及格式转化的几种途径,详细介绍了DOS下图像格式转换软件HIJAAK,分析了PhotoStyler,SPT各自的优势,提出了一个有普遍意义的作法。     

基于迁移学习的遥感测绘图像细节增强方法

为提高遥感图像的清晰程度，研究基于迁移学习的遥感测绘图像细节增强方法。定义邻域边界矩阵，计算细节损失梯度坐标，提取边界函数，重构遥感测绘图像边界；建立转换数据幅值函数，计算拉伸函数不同方向的算子，基于迁移学习校正像素参数；通入低通滤波，计算光照参数，代入光照格式函数，在拉伸处理后获取阴影部分细节纹理。实验结果显示本文的增强方法平均梯度、信息熵、均值均最大。在图像细节增强的实例检测中，可以清晰地看出该图像增强方法较好，得到的图像更清晰。     

基于结构性字典学习的毛儿盖遥感图像去噪研究

遥感图像的噪声分析、评估和滤波作为遥感图像处理的研究重点而一直受到遥感应用领域的关注。为了进一步提高遥感图像的去噪能力，提出一种新的基于聚类的组稀疏字典学习多光谱遥感图像去噪算法，该算法能够综合利用多光谱遥感图像的空间局部性和光谱的全局性，对遥感图像像素进行聚类后划分为不同的组，然后通过字典学习获得多光谱遥感图像的空间、光谱字典和系数。经过阈值处理后，对空间相似的块进行平均处理，实现了对多光谱遥感图像的去噪。该算法用于岷江上游植被和土壤类型典型地区——毛儿盖实验区遥感图像的去噪，峰值信噪比相比band-wise K-SVD算法提高了7.6%左右，同时具有更好的视觉效果。     

高性能遥感图像处理与空间信息网格建模

将空间信息网格技术（SIG）应用于遥感图像处理中，就是利用网格计算的特点，来解决遥感图像数据以及处理算法资源的共享、海量遥感图像数据的实时快速处理等问题。文章以此为主线，在分析高性能遥感图像处理问题的基础上，探讨了在网格环境下构造高性能遥感图像处理系统的可行性及相关关键技术的实现，提出了基于开放网格服务体系结构的系统模型，并实现了原型系统。实验表明，SIG用于遥感图像处理是可行的，并取得了一定的成果。     

大型遥感图像处理应用项目综合数据库开发

对数据高精度、时效性的需求，使遥感图像处理及应用越来越受到普遍的关注。随着解决问题的复杂化，大型遥感图像处理工程及遥感图像处理应用项目逐渐增多。大型遥感图像处理应用项目是在计算机软、硬件支持下，动态地获取、处理遥感影像，综合管理区域内地理环境信息及各...     

地面、图像光谱数据的预处理

在高光谱遥感研究中，需要地面光谱和图像光谱的结合分析处理。地面光谱的正确采集，两种光谱数据的预处理，由于与分析过程的直接联系不大，往往被忽视。其实地面和遥感所采集的原始数据并不能直接用于分析，对其的预处理涉及到格式转换、数据消噪等问题，这些都是高光谱科学分析研究的前提，关系到结果的正确性。虽然目前介绍光谱分析研究的文章和书籍很多，但是完整介绍光谱采集和预处理，并可用于实践的甚少。对于刚刚涉入高光谱领域的同学和老师，对此类问题经常感到迷茫。作者在近些年内参加了多次地面光谱采集和遥感飞行试验，对大量的地面光谱和图像光谱进行预处理。主要讲述作者通过实践过程中摸索比较，总结出的简单易行、能获得较好效果的光谱预处理方法 ,同时介绍光谱采集的正确方法。地面光谱数据为ASD-FR2500采集数据(该种野外光谱仪在国内外比较普遍)，图像数据为OMIS图像。其它光谱仪或者遥感成像仪器的数据可以类似处理。     

基于GDAL框架的多源遥感数据的解析

针对图像软件系统工程项目中涉及的多源遥感数据的预处理问题,研究了当前几种典型卫星数据的不同存储格式,提出了对各种遥感数据进行解析的方法.通过对地理空间数据抽象库( GDAL)原有功能进行完善和扩展,并重新编辑发布动态链接库( DLL)版本,设计了多源遥感数据在GDAL框架体系下的统一解析模式,实现了遥感数据解析结果存储为项目中通用的统一数据格式(如Tiff格式),为后续的遥感数据处理和定量遥感数据产品的生产提供数据基础和技术支持.     

基于GDAL的遥感图像变化检测技术

由于GDAL（Geospatial Data Abstraction Library）具有快速读取多种格式的遥感图像且能有效解析空间元数据等特点,利用它开发遥感图像处理算法具有明显的优势。结合GDAL及相应算法,开发了一套复杂地形山区植被遥感变化检测的技术,其中包括利用阴影消除植被指数（Shadow Elimination Vegetation Index,SEVI）反演植被长势;利用图像差值法及最大类间方差法（OTSU）来提取植被长势明显变化点位;利用[K]均值聚类自动分割识别变化区域。将该方法用于武夷山自然保护区和闽江源自然保护区2016-2017年Landsat8 OLI遥感图像的植被长势变化检测,结果表明,这套遥感图像变化检测技术切实可行,能够有效识别遥感图像变化区域,并在复杂地形山区的植被长势监测中具有良好的应用价值。     

Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images

Super-resolution land-cover mapping is a promising technology for prediction of the spatial distribution of each land-cover class at the sub-pixel scale. This distribution is often determined based on the principle of spatial dependence and from land-cover fraction images derived with soft classification technology. However, the resulting super-resolution land-cover maps often have uncertainty as no information about sub-pixel land-cover patterns within the low-resolution pixels is used in the model. Accuracy can be improved by incorporating supplemental datasets to provide more land-cover information at the sub-pixel scale; but the effectiveness of this is limited by the availability and quality of these additional datasets. In this paper, a novel super-resolution land-cover mapping technology is proposed, which uses multiple sub-pixel shifted remotely sensed images taken by observation satellites. These satellites take images over the same area once every several days, but the images are not identical because of slight orbit translations. Low-resolution pixels in these remotely sensed images therefore contain different land-cover fractions that can provide useful information for super-resolution land-cover mapping. We have constructed a Hopfield Neural Network (HNN) model to solve it. Maximum spatial dependence is the goal of the proposed model, and the fraction maps of all images are constraints added to the energy function of HNN. The model was applied to synthetic artificial images as well as to a real degraded QuickBird image. The output maps derived from different numbers of images at different zoom factors were compared visually and quantitatively to the super-resolution map generated from a single image. The resulting land-cover maps with multiple remotely sensed images were more accurate than was the single image map. The use of multiple remotely sensed images is therefore a promising method for decreasing the uncertainty of super-resolution land-cover mapping. Moreover, remotely sensed images with similar spatial resolution from different satellite platforms can be used together, allowing a fusion of information obtained from remotely sensed imagery.     

Parallel spatiotemporal autocorrelation and visualization system for large-scale remotely sensed images

Current studies on large-scale remotely sensed images are of great national importance for monitoring and evaluating global climate and ecological changes. In particular, real time distributed high-performance visualization and computation have become indispensable research components in facilitating the extraction of remotely sensed image textures to enable mining spatiotemporal patterns and dynamics of landscapes from massive geo-digital information collected from satellites. Remotely sensed images are usually highly correlated with rich landscape features. By exploiting the structures of these images and extracting their textures, fundamental insights of the landscape can be derived. Furthermore, the interdisciplinary collaboration on the remotely sensed image analysis demands multifarious expertise in a wide spectrum of fields including geography, computer science, and engineering.     

基于MPI的遥感影像高效能并行处理方法研究

采用基于不同尺度下的面向特征基元的影像分析方法对高分辨率遥感影像进行基于MPI的处理,即在对常规的影像数据划分方法进行总结分析的基础上,提出了基于特定环境下的非均匀数据划分策略;在进行基于影像数据库的MPI并行处理时,提出了一种新的数据流分配方法。处理结果表明,这两种方法均能够在一定环境下取得比常规方法更高的效率。     

斑块状植被遥感检测研究进展

斑块状植被是世界上干旱—半干旱区常见的景观类型,对于它们的形成、结构和演替研究能够提高人们对干旱—半干旱地区生态系统动态及其重要的生态水文过程的理解,具有重要的理论研究意义和应用价值.传统的基于地面调查和长期定位观测的方法观测范围有限,已无法满足目前区域斑块状植被分布及其空间格局特征研究的需要.利用遥感技术快速重复获取...     

遥感图象分类的统计融合方法及性能分析

基于数据层的统计数据融合方法,以提高遥感图象的分类性能为目的,实现了一种新的可调参数的图象分类方法。用这种方法对ＴＭ图象和ＳＡＲ图象进行了一系列的实验,并对实验的结果进行了分析,从而得出关于数据层统计信息融合方法的有益的结论。     

遥感图像融合的研究现状、困境及发展趋势探讨

从像素级、特征级和决策级遥感图像融合的角度,概述了遥感图像融合的研究现状处于瓶颈发展时期。提出遥感图像融合研究的三方面主要困境:缺乏统一的融合理论框架作指导;缺乏面向应用的融合算法设计;融合数据源的选择没有针对性,缺乏针对不同传感器数据和不同分辨率的融合研究。指出目前遥感图像融合研究在特征级和决策级融合,多角度融合,数据预处理精度,以及不确定性分析方面的研究不足。最后,提出了未来遥感图像融合的发展趋势和研究热点。     

Distributed computing model for processing remotely sensed images based on grid computing

With advances in remote-sensing technology, the large volumes of data cannot be analyzed efficiently and rapidly, especially with arrival of high-resolution images. The development of image-processing technology is an urgent and complex problem for computer and geo-science experts. It involves, not only knowledge of remote sensing, but also of computing and networking. Remotely sensed images need to be processed rapidly and effectively in a distributed and parallel processing environment. Grid computing is a new form of distributed computing, providing an advanced computing and sharing model to solve large and computationally intensive problems. According to the basic principle of grid computing, we construct a distributed processing system for processing remotely sensed images. This paper focuses on the implementation of such a distributed computing and processing model based on the theory of grid computing. Firstly, problems in the field of remotely sensed image processing are analyzed. Then, the distributed (and parallel) computing model design, based on grid computing, is applied. Finally, implementation methods with middleware technology are discussed in detail. From a test analysis of our system, TARIES.NET, the whole image-processing system is evaluated, and the results show the feasibility of the model design and the efficiency of the remotely sensed image distributed and parallel processing system.     

FasART模糊神经网络用于遥感图象监督分类的研究

说明了遥感图象数据的非线性性质，目视的图象分类实践是一个模糊推理的过程，模糊神经网络遥感图象分类符合其事物的内在规律，具有理论优势，分析了模糊ART，模糊ARTMAP和FasART模型的结构和原理，详细地阐述了FasART是一种基于模糊逻辑系统的神经网络，提出了一种简化的FasART模型，改变了一般遥感数据的模糊化方法，采用中巴资源一号卫星数据进行测试实验，结果表明，该简化的FasART模型能用于遥感图象的监督分类，其分类精度高于模糊ARTMAP神经网络和K均值算法，且性能稳定，有较好的抗干扰能力，尤其具有良好的处理两组相似程度比较接近的，和同组数据模式变化较大的非线性数据的能力。     

A new algorithm for remotely sensed image texture classification and segmentation

In this paper, we propose a new algorithm for remotely sensed image texture classification and segmentation. We observe that the traditional method least square error (LSE) is unstable in practical applications. This motivates us to develop a more stable method. We have proposed the regularization technique to suppress the instability of LSE in previous research. Our contribution in this paper is that we propose a new stable method, which is based on the total variation (TV) for reducing instability in texture analysis, and apply it to remotely sensed image texture classification and segmentation. Experimental results on remotely sensed images demonstrate that our new algorithm is superior to LSE and seems promising in applications.     

Adaptive thresholding of texture towards white region normalization

Human colour vision has the ability to recover a measure of contextdependent surface reflectance from observed areas. This can be seen as an analogue to radiometric corrections employed in remote sensing. Procedures based on human colour vision applied in the context of remote sensing could simplify image preprocessing and classification. This study evaluates an algorithm based on Land's colour vision or 'retinex' theory. When tested on remotely sensed images, the algorithm had difficulty coping with the presence of image texture. A new framework is presented that adapts Land's procedure to processing of remotely sensed images using adaptive thresholding techniques.