利用颜色进行模式分类与图象检索的方法及应用

本文论述了利用颜色进行模式分类与图象检索的基本概念和一般方法。颜色是图象的一个重要视觉特征。用计算机描述和处理图象颜色首先要选择合适的颜色空间（模型），并提取图像的主要颜色特征。彩色图象模式分类可分三个级别：象素级，区域级及图象级，图象检索主要利用颜色的直方图特征及其各种距离度量来实现。     

一种新的实时彩色目标分割方法

为实现对视频序列图象中彩色目标的识别与跟踪,提出了一种鲁棒的实时分割方法。它分为象素分类与区域特征提取两过程。在象素分类阶段,把每一个目标象素建模成一个二维高斯模型,定义相应的决策函数,将图象中所有象素点分类成为目标和背景,同时对高斯模型的参数进行在线动态修改,使之对变化的光照条件所引起的彩色畸变具有自适应性。在区域特征提取阶段,对象素分类所形成的二值图象实现行程编码,产生行程编码映射图,在此基础上,采用一种基于树的区域搜索算法计算出区域特征量。文中提出的方法已成功地应用于足球机器人视觉子系统中,取得了良好的效果。     

电视录象图象的分类与检索

本文对现有的电视图象分类算法和检索算法作了的分析，并在图象分类方面提出了一种自适应分类算法；在检索方面提出了一种基于颜色的样本与非样本学习相结合的方法。自适应分类算法的计算量与相邻帧间的变化量成正比；对纡绝大多数相邻帧，只要计算数目极小的区域就可完成分类工作。实验证明，本文提出的分类与检索方法在速度与精度上均有明显的提高。     

黄土丘陵区数字地貌模型与遥感影象分类

黄土丘陵区地形破碎，沟壑纵横，为遥感影象的土地利用自动分类带来了很大困难。依靠传统的统计分类方法，难以达到较高的分类精度。本文拟探讨在黄土丘陵区运用大比例尺地琪图数据。建立区域数字地貌模型，并在模糊数学理论的指导下，运用数字地貌模型对影象的分层分类结果进行修正和细化。研究表明，此方法能够有效地提高土地利用分类精度和机助制图比例尺。     

基于小波变换的图象分数维计算方法

图像分数维能够反映图象的纹理特征，是图象分割的重要依据。本文在对图象分数维计算方法分类的基础上，着重研究了基于小波变换的图象分数维计算方法，并与基于盒维数定义的差分计盒维数法进行对比，结果表明，通过该方法计算得到的图象分数维较准确。     

基于基因搜索的模糊图象解释方法

图象解决是计算机视觉的重要组成部分，它涉及图象处理，分类器设计和逻辑推理等许多领域。针对目前图象解释系统要面对的严重噪声、模糊性和不确定性问题。重点研究了一种基于基因搜索的双向推理技术，该算法分为如下两步：首先通过基于分割区域统计/几何特征的模式分类器来得到初始的分类模糊隶属度，并根据经验（或统计）得到的先验空间位置关系模糊规则来构造一种有效表达图象解释信息的模糊图。然后通过基因搜索算法融合上面的两类信息来得到图象的最佳解释，实验结果表明，该方法对具有单一对象或多个对象的区域均有很好的效果，也是对基于概率、证据和模糊推理等单向推理机制图象解释方法的提高。     

一种新的个性化的图象分类方法

图象分类系统的建立是信息检索以及模式识别中一个重要部分，其中，特征选择问题，即确定描述图象的特征参数是需要解决的关键问题，基于和图象检索技术的研究，近来得到了广泛的关注，由图象特征向量维数过高而引起的图象检索困难是基于内容的图象检索技术研究所面临的一个挑战，因此需要寻找一个有效降维技术，为解决此问题，设计了一个新的图象分类标准模型，通过寻找不同的特征组合来作为分类标准，进而提出了一种算法，用于实现此模型，实验结果显示，该模型能实现图象特征向量降维，并且算法能够极大地降低计算所花费的时间，同时，多种不同分类标准的引入，使得本方法能与信息检索技术进行有效的结合，为个性化信息检索提供一种实现思路。     

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。     

视觉导航的旋转不变性图象获取和神经元网络识别方法研究

本文报告了将旋转不变性图象和神经网络用于移动机器人室外道路识别和方向判别的研究结果。本方法采用图象分析技术与自适应的神经元网络相结合；用全方位图象传感器提取图象样本，然后从图象中提取旋转不变性的特征，使用分类网络对道路类型分类，并用其结果选择不同的方向估计神经元网络；分析了神经元网络在选择不同形式的输入数据和不同数目的隐含层结点时的性能。文章给出了在室外环境下拍摄的真实图象实验的结果。     

高光谱遥感图像的单形体分析方法

将n个波段的高光谱图像像元与n维空间里的散点联系起来，结合凸体几何中单形体概念研究高光谱遥感图像纯净像元提取方法，实现图像的地物精确分类识别及像元波谱分解。寻找高光谱遥感图像n维空间里的单形体并认知分析单形体是该研究方法的重要环节。通过MNF(minimum noise fraction)变换和PPI(pixel purity index)计算技术寻找到单形体，基于单形体进行像元分解分析单形体，并结合应用实例和SAM(spectral angle mapper)分类技术完成高光谱图像地物精确分类制图，验证了该研究方法的可操作性。该研究方法的优点在于不需要用户提供地物波谱信息，用于制图和波谱分解的终端单元可由图像本身得到，并由用户控制分类制图和波谱分解的详细程度。     

Influence of image fusion approaches on classification accuracy: a case study

While many studies in the field of image fusion of remotely sensed data aim towards deriving new algorithms for visual enhancement, there is little research on the influence of image fusion on other applications. One major application in earth science is land cover mapping. The concept of sensors with multiple spatial resolutions provides a potential for image fusion. It minimises errors of geometric alignment and atmospheric or temporal changes.

This study focuses on the influence of image fusion on spectral classification algorithms and their accuracy. A Landsat 7 ETM+ image was used, where six multispectral bands (30 m) were fused with the corresponding 15 m panchromatic channel. The fusion methods comprise rather common techniques like Brovey, hue‐saturation‐value transform, and principal component analysis, and more complex approaches, including adaptive image fusion, multisensor multiresolution image fusion technique, and wavelet transformation. Image classification was performed with supervised methods, e.g. maximum likelihood classifier, object‐based classification, and support vector machines. The classification was assessed with test samples, a clump analysis, and techniques accounting for classification errors along land cover boundaries. It was found that the adaptive image fusion approach shows best results with low noise content. It resulted in a major improvement when compared with the reference, especially along object edges. Acceptable results were achieved by wavelet, multisensor multiresolution image fusion, and principal component analysis. Brovey and hue‐saturation‐value image fusion performed poorly and cannot be recommended for classification of fused imagery.     

A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis

Rapid urban growth in developing countries is causing a great number of urban planning problems. To control and analyse this growth, new and better methods for urban land use mapping are needed. This article proposes a new method for urban land-use mapping, which integrates spatial metrics and texture analysis in an object-based image analysis classification. A high-resolution satellite image was used to generate spatial and texture metrics from the machine learning algorithm of Random Forests land-cover classification. The most meaningful spatial indices were selected by visual inspection and then combined with the image and texture values to generate the classification. The proposed method for land-use mapping was tested using a 10-fold cross-validation scheme, achieving an overall accuracy of 92.3% and a kappa coefficient of 0.896. These steps produced an accurate model of urban land use, without the use of any census or ancillary data, and suggest that the combined use of spatial metrics and texture is promising for urban land-use mapping in developing countries. The maps produced can provide the land-use data needed by urban planners for effective planning in developing countries.     

Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data

Accurate mapping of land-cover diversity within riparian areas at a regional scale is a major challenge for better understanding the influence of riparian landscapes and related natural and anthropogenic pressures on river ecological status. As the structure (composition and spatial organization) of riparian area land cover (RALC) is generally not accessible using moderate-scale satellite imagery, finer spatial resolution imagery and specific mapping techniques are needed. For this purpose, we developed a classification procedure based on a specific multiscale object-based image analysis (OBIA) scheme dedicated to producing fine-scale and reliable RALC maps in different geographical contexts (relief, climate and geology). This OBIA scheme combines information from very high spatial resolution multispectral imagery (satellite or airborne) and available spatial thematic data using fuzzy expert knowledge classification rules. It was tested over the Hérault River watershed (southern France), which presents contrasting landscapes and a total stream length of 1150 km, using the combination of SPOT (Système Probatoire d'Observation de la Terre) 5 XS imagery (10 m pixels), aerial photography (0.5 m pixels) and several national spatial thematic data. A RALC map was produced (22 classes) with an overall accuracy of 89% and a kappa index of 83%, according to a targeted land-cover pressures typology (six categories of pressures). The results of this experimentation demonstrate that the application of OBIA to multisource spatial data provides an efficient approach for the mapping and monitoring of RALC that can be implemented operationally at a regional or national scale. We further analysed the influence of map resolution on the quantification of riparian spatial indicators to highlight the importance of such data for studying the influence of landscapes on river ecological status at the riparian scale.     

A performance evaluation of a burned area object-based classification model when applied to topographically and non-topographically corrected TM imagery

Operational use of remote sensing as a tool for post-fire Mediterranean forest management has been limited by problems of classification accuracy arising from confusion between burned and non-burned land, especially within shaded areas. Object-oriented image analysis has been developed to overcome the limitations and weaknesses of traditional image processing methods for feature extraction from high spatial resolution images. The aim of this work was to evaluate the performance of an object-based classification model developed for burned area mapping, when applied to topographically and non-topographically corrected Landsat Thematic Mapper (TM) imagery for a site on the Greek island of Thasos. The image was atmospherically and geometrically corrected before object-based classification. The results were compared with the forest perimeter map generated by the Forest Service. The accuracy assessment using an error matrix indicated that the removal of topographic effects from the image before applying the object-based classification model resulted in only slightly more accurate mapping of the burned area (1.16% increase in accuracy). It was concluded that topographic correction is not essential prior to object-based classification of a burned Mediterranean landscape using TM data.     

Alternative spatially enhanced integrative techniques for mapping seagrass in Florida's marine ecosystem

Seagrass is an important component of coastal marine ecosystems. Seagrass mapping provides a means for assessing seagrass health by monitoring the spatial distribution and density of seagrass habitat in coastal waters. Recent image processing and satellite technologies present the opportunity to leverage quantitative techniques that have the potential to improve upon traditional photo-interpretation techniques in terms of cost, mapping fidelity, and objectivity. Integrated spatial and spectral processing techniques were identified as an alternative method for mapping seagrass extent and density from an IKONOS satellite image of Springs Coast, Florida. These spatially enhanced integrative mapping techniques objectively standardize seagrass-monitoring efforts and enhance mapping capabilities by characterizing spatial seagrass density gradients. A combination of water column correction, pixel classification, and image segmentation techniques provided a seagrass density index map that represented seagrass density and distribution with high spatial detail and overall accuracy (77%) comparable to photo-interpretation techniques. Satellite imagery-based spatially enhanced image processing techniques were found to provide a consistent, quantitative, and cost-effective alternative for seagrass mapping in Springs Coast with the potential to be transferred to other parts of the world. A cost savings analysis concluded that there was a 13% cost saving using satellite photo-interpretation and a 47% cost saving using enhanced satellite classification when compared to aerial photo-interpretation.     

Optimizing spatial image support for quantitative mapping of natural vegetation

High spatial resolution images allow us to assess the spatial structure and variability of natural vegetation characteristics. In order to optimize quantitative image analysis, we looked for the optimal spatial resolution to map natural vegetation in a Mediterranean environment. The optimal spatial resolution was then related to the spatial characteristics of the scene and the mapped parameters. Based on the relation between airborne hyperspectral imagery and field dataset consisting of 227 measured plots of Leaf Area Index (LAI) and aboveground biomass, we found an optimal pixel size of 55 and 95 m for the mapping of LAI and aboveground biomass, respectively. The optimal spatial resolution we found is determined by the spatial structure of the mapped parameters on the one hand, and by the effects of shading patterns and gaps in the canopy on the other. The spatial properties of scene elements influencing the optimal spatial resolution can be detected effectively using average local variance functions and variograms, provided that these are studied at a wide spatial range. The use of an optimally sized mapping unit instead of the original 5 m pixels resulted in an improvement of mapping accuracy of 7 to 17%. It is therefore recommended that the image support used is considered carefully in all quantitative mapping projects based on remotely sensed imagery.     

Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central America

While mapping vegetation and land cover using remotely sensed data has a rich history of application at local scales, it is only recently that the capability has evolved to allow the application of classification models at regional, continental and global scales. The development of a comprehensive training, testing and validation site network for the globe to support supervised and unsupervised classification models is fraught with problems imposed by scale, bioclimatic representativeness of the sites, availability of ancillary map and high spatial resolution remote sensing data, landscape heterogeneity, and vegetation variability. The System for Terrestrial Ecosystem Parameterization (STEP) - a model for characterizing site biophysical, vegetation and landscape parameters to be used for algorithm training and testing and validation - has been developed to support supervised land cover mapping. This system was applied in Central America using two classification systems based on 428 sites. The results indicate that: (1) it is possible to generate site data efficiently at the regional scale; (2) implementation of a supervised model using artificial neural network and decision tree classification algorithms is feasible at the regional level with classification accuracies of 75-88%; and (3) the STEP site parameter model is effective for generating multiple classification systems and thus supporting the development of global surface biophysical parameters.     

The development of an operational procedure for burned-area mapping using object-based classification and ASTER imagery

Although burned-area mapping at a regional level is traditionally based on the use of Landsat data, the potential gap in the sensor's data collection emphasizes the need to find alternative data sources to be used in the operational mapping of burned areas. This work aims to investigate whether it is possible to develop a transferable object-based classification model for burned-area mapping using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. The initial step in the investigation involved the development of an object-based classification model for accurately mapping burned areas in central Portugal using an ASTER image, and subsequently an examination of its performance when mapping a burned area located on the island of Rhodes, Greece, using a different ASTER image. Results indicate that the combined use of object-based image analysis and ASTER imagery can provide an alternative operational tool that could be used to identify and map burned areas and thus fill a potential gap in Landsat data collection.     

河流枯水区域参数遥感地学分析应用

以云南李仙江布固江流域为例,利用空间遥感信息,通过水文下垫面要素的地学分析与制图,在其水文下垫面单元分区的基础上,确定区域水文模型参数,参照实测水文资料,进行河流枯水资源量的估算。这对缺资料地区的枯水综合研究具有重要的意义。