Geographic Object-Based Image Analysis of Changes in Land Cover in the Coastal Zones of the Red River Delta (Vietnam)

The majority of the population and economic activity of the northern half of Vietnam is clustered in the Red River Delta and about half of the country’s rice production takes place here. There are significant problems associated with its geographical position and the intensive exploitation of resources by an overabundant population (population density of 962 inhabitants/km2). Some thirty years after the economic liberalization and the opening of the country to international markets, agricultural land use patterns in the Red River Delta, particularly in the coastal area, have undergone many changes. Remote sensing is a particularly powerful tool in processing and providing spatial information for monitoring land use changes. The main methodological objective is to find a solution to process the many heterogeneous coastal land use parameters, so as to describe it in all its complexity, specifically by making use of the latest European satellite data (Sentinel-2). This complexity is due to local variations in ecological conditions, but also to anthropogenic factors that directly and indirectly influence land use dynamics. The methodological objective was to develop a new Geographic Object-based Image Analysis (GEOBIA) approach for mapping coastal areas using Sentinel-2 data and Landsat 8. By developing a new segmentation, accuracy measure, in this study was determined that segmentation accuracies decrease with increasing segmentation scales and that the negative impact of under-segmentation errors significantly increases at a large scale. An Estimation of Scale Parameter (ESP) tool was then used to determine the optimal segmentation parameter values. A popular machine learning algorithms (Random Forests-RFs) is used. For all classifications algorithm, an increase in overall accuracy was observed with the full synergistic combination of available data sets.

Object-based classification of cloudy coastal areas using medium-resolution optical and SAR images for vulnerability assessment of marine disaster

Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover,coastal areas often encounter significant cloud cover,especially in tropical areas,which makes the classification in those areas non-ideal.To solve this problem,we proposed a framework of combining medium-resolution optical images and synthetic aperture radar(SAR)data with the recently popular object-based image analysis(OBIA)method and used the Landsat Operational Land Imager(OLI)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)images acquired in Singapore in 2017 as a case study.We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size,and the other is the classification feature.Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80,and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features,especially in areas with cloud cover.Based on the land cover generated by this framework,we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade.To clarify the disaster response plan for different geographical environments,we classified risk based on altitude and distance from shore.The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk;these regions may need to focus on strengthening disaster prevention construction.This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters,especially those in cloudy coastal areas.

An Integrated Framework for Road Detection in Dense Urban Area from High-Resolution Satellite Imagery and Lidar Data

Automatic road detection, in dense urban areas, is a challenging application in the remote sensing community. This is mainly because of physical and geometrical variations of road pixels, their spectral similarity to other features such as buildings, parking lots and sidewalks, and the obstruction by vehicles and trees. These problems are real obstacles in precise detection and identification of urban roads from high-resolution satellite imagery. One of the promising strategies to deal with this problem is using multi-sensors data to reduce the uncertainties of detection. In this paper, an integrated object-based analysis framework was developed for detecting and extracting various types of urban roads from high-resolution optical images and Lidar data. The proposed method is designed and implemented using a rule-oriented approach based on a masking strategy. The overall accuracy (OA) of the final road map was 89.2%, and the kappa coefficient of agreement was 0.83, which show the efficiency and performance of the method in different conditions and interclass noises. The results also demonstrate the high capability of this object-based method in simultaneous identification of a wide variety of road elements in complex urban areas using both high-resolution satellite images and Lidar data.

Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore satisfy the true demands of night scene target detection applications.We very much believe that our method will help future research.

Detecting Slums from SPOT Data in Casablanca Morocco Using an Object Based Approach

Casablanca, Morocco's economic capital continues today to fight against the proliferation of informal settle- ments affecting its urban fabric illustrated especially by the slums. Actually Casablanca represents 25% of the total slums of Morocco [1]. These are the habitats of all deprived of healthy sanitary conditions and judged precarious from the perspective humanitarian and below the acceptable. The majority of the inhabi- tants of these slums are from the rural exodus with insufficient income to meet the basic needs of daily life. Faced with this situation and to eradicate these habitats, the Moroccan government has launched since 2004 an entire program to create cities without slums (C.W.S.) to resettle or relocate families. Indeed the process control and monitoring of this program requires first identifying and detecting spatial habitats. To achieve these tasks, conventional methods such as information gathering, mapping, use of databases and statistics often have shown their limits and are sometimes outdated. It is within this framework and that of the great German Morocco project “Urban agriculture as an integrative factor of development that fits our project de- tection of slums in Casablanca. The use of satellite imagery, particulary the HSR, has the advantage of providing the physical coverage of urban land but it raises the difficulty of choosing the appropriate method to apply.This paper is actually to develop new approaches based mainly on object-oriented classification of high spatial resolution satellite images for the detection of slums.This approach has been developed for mapping the urban land through by integration of several types of information (spectral, spatial, contextual ...) (Hofmann, P ., 2001, Herold et al. 2002b;Van Der Sande et al., 2003, Benz et al., 2004, Nobrega et al., 2006). In order to refine the result of classification, we applied mathematical morphology and in particular the closing filter. The data from this classification (binary image), which then will be used in a spatial data- base (ArcGIS).

基于CNN-OBIA的黄河源区水体提取及时空变化

准确识别水体信息是分析地表水时空动态变化的重要技术手段。针对目前各种长时序水体信息提取方法精度低的问题,基于Landsat遥感影像,选用1986~2022年5484景黄河源区遥感影像,分别运用卷积神经网络结合面向对象(CNN-OBIA)和多指数水体检测规则(MIWDR)两种方法提取了黄河源区的地表水体,并对两种方法的提取精度进行了对比分析。在此基础上,探究了1986~2022年黄河源区水体信息的时空变化特征,并对其主要气候因素进行相关分析。结果表明:①CNN-OBIA的总体精度和Kappa系数分别为96.78%和0.93,MIWDR的总体精度和Kappa系数分别为94.28%和0.88,总体而言,CNN-OBIA的提取精度高于MIWDR方法。CNN-OBIA的提取结果可以很好地保持水体边界完整性和有效去除山体阴影,可以较好地对细小河流进行提取。②研究区水体总面积呈现出先减少(1986~2001年)后增加(2001~2022年)的变化趋势。③相关性分析表明,降水和气温与水体面积的变化均表现出显著正相关。

Evaluation of semivariogram features for objectbased image classification

Inclusion of textures in image classification has been shown beneficial.This paper studies an efficient use of semivariogram features for object-based high-resolution image classification.First,an input image is divided into segments,for each of which a semivariogram is then calculated.Second,candidate features are extracted as a number of key locations of the semivariogram functions.Then we use an improved Relief algorithm and the principal component analysis to select independent and significant features.Then the selected prominent semivariogram features and the conventional spectral features are combined to constitute a feature vector for a support vector machine classifier.The effect of such selected semivariogram features is compared with those of the gray-level co-occurrence matrix(GLCM)features and window-based semivariogram texture features(STFs).Tests with aerial and satellite images show that such selected semivariogram features are of a more beneficial supplement to spectral features.The described method in this paper yields a higher classification accuracy than the combination of spectral and GLCM features or STFs.

地理空间中的空间关系表达和推理

针对地理空间中的应用,归纳了在空间关系的表达与推理中不同于人工智能领域研究的一些特点:在人工智能领域,更注重建立形式化的推理系统;而在地理信息科学中,则需更关注地理空间的特点以及地物的地理语义。该文基于地理空间和地理现象的本质且顾及地理空间认知,总结了地理空间中空间关系表达和推理的特点,具体包括空间的有限性、地球的球面特征、地物的地理语义、地物形状的复杂性、面状地物、特殊的空间关系、空间关系的层次性与尺度相应原则、不确定性、三维与时态特性九方面;进而介绍了地理空间关系表达的两个应用,即地理信息检索和基于对象的图像分析。该文的探讨可为地理信息科学中的相关研究提供方向性指导。

遥感模式分类中的空间统计学应用——以面向对象的遥感影像农田提取为例

如何有效地从遥感图像中提取所需信息,是遥感图像处理和应用的关键,而尺度选择问题一直是影响遥感信息提取精度的关键问题之一。本文论述了利用空间统计学方法解决遥感影像模式分类中的尺度问题的理论基础。针对面向对象影像分析问题,将影响遥感影像多尺度分割的尺度分割参数概括为空间属性分割参数、光谱属性分割参数和影像对象面积阈值参数,并分别提出了基于统计学的尺度参数估计方法。以SPOT-5影像面向对象农田提取为例,基于变异函数方法进行了尺度优选试验,系列尺度分类试验结果表明基于空间统计学尺度估计得到的尺度分割结果进行分类能得到最高的精度,进而证明了基于空间统计学方法进行面向对象信息提取尺度估计的有效性。该方法是完全数据驱动的方法,基本不需要先验知识参与。不同于以往分割后评价的尺度选择方法会占用大量计算资源且耗费大量时间,本文提出的方法不仅能在一定程度上保证面向对象信息提取的精度,而且在一定程度上也提高了面向对象信息提取的效率和自动化程度。

基于倾斜摄影三维影像的建筑物震害特征分析

精确的建筑物震害评估对地震应急、救灾和经济损失评估有着重要的作用。倾斜摄影可从多角度获取建筑物侧面和顶部的信息,建模形成的高精度三维影像解决了传统二维影像只能从垂直视角进行分析的局限性。根据欧洲98地震烈度表(EMS-98),建筑物的震害等级分为5类,而二维遥感影像无法获取建筑物外墙以及墙体结构、裂纹等信息,建筑物的震害程度往往分为2/3类,造成遥感影像提取与地面调查不一致。通过研究从三维模型中提取的纹理影像,结合面向对象影像分析提取混凝土结构外墙及其裂纹,并对提出的外墙、裂纹进行了分析。

1990～2015年韩国土地覆被变化及其驱动因素

以中等分辨率Landsat系列影像为数据源,利用面向对象的图像分析(OBIA)方法,研究1990~2015年韩国土地覆被变化的主要特征与驱动因素。研究发现:近25 a来,韩国人工表面、林地、湿地、耕地和水体面积变化较大。人工表面扩张最为明显,面积增加了1 847.24 km2(+38.97%),主要发生在以首尔为中心的首都圈地区,多由耕地和林地转化而来。林地、湿地和耕地面积分别减少776.71 km2、707.32 km2和426.65 km2。过去25 a间韩国土地覆被变化主要集中分布在海拔较低(<100 m)和坡度较小(<3°)的区域。人类活动因素,如人口增长、城市扩张、经济发展及政策调控等是造成韩国土地覆被变化的主要原因。

应用高分辨率影像的城市固体废弃物提取

日益增多的城市固体废弃物不断威胁着城市的生态环境。应用遥感技术的城市固体废弃物监测与管理比实地调查的方法省时省力。然而,分布不规律的城市固体废弃物成分复杂,在高分辨率遥感影像上表现出极强的异质性,无论是针对像素,还是针对运用对象的影像分析方法都难以实现计算机自动提取。针对城市固废堆在重采样低分辨率影像上异质性得到减弱、同质性增强,而在高分辨率上细节丰富、位置准确的特点,提出了一种融合多分辨率对象的城市固废提取方法。以北京市海淀区的QuickBird影像进行实验,结果表明,露天城市固废堆的识别精度可达75%,表明这种多分辨率信息提取策略对于监测露天的城市固废堆十分有效。

面向对象的形态学建筑物指数及其高分辨率遥感影像建筑物提取应用

高分辨率遥感影像建筑物提取是摄影测量与遥感领域的一个热门研究主题。本文综合利用影像分割、基于图的数学形态学top-hat重建技术,提出了面向对象的形态学建筑物指数OBMBI,并将其应用于高分辨率遥感影像建筑物提取。首先,建立像素-对象-图节点的双向映射关系;然后,基于图的白top-hat重建和上述映射关系来构建OBMBI图像;接着,对该OBMBI图像二值化、矢量化以获取建筑物多边形;最后,对结果进行后处理优化。使用一景航空、一景卫星全色影像对本文方法和PanTex方法进行性能测试。试验表明,本文方法的建筑物提取精度显著的优于PanTex方法。其中,本文方法平均比PanTex方法的正确率高9.49%、完整率高11.26%、质量高14.11%。

基于高分遥感影像的黄土高原地区水体高精度提取

黄土高原地区水体特征复杂多样,基于高空间分辨率遥感影像在大面积范围内,现有提取方法很难兼顾精度与效率,故提出一种新型的水体精细化自动提取方法。首先,在全局范围内根据不同的水体类型使用面向对象的影像分析技术按照不同的规则方法初步提取水体的主体部分,形成水体的种子区域;然后,在局部范围内根据同一地物的光谱相似性,进行区域种子生长,实现水体的高精度提取。结果表明,使用该方法提取的水体边缘可以较好地与影像吻合,更能保证水体的完整性,有效减少干扰信息,提升运算效率。

基于区域合并的高分辨率遥感图像多尺度分割

图像分割是高分辨率遥感图像处理和分析的关键环节.本文探讨了将区域合并方法应用于高分辨率遥感图像多尺度分割的技术要点,旨在提升分割的精度和效率,获得地物对象的多尺度表达.主要研究内容包含如下五个方面:(1)图模型的构建,包括区域邻接图和最近邻图,以提高分割效率;(2)合并准则的确定,选择能有效表征区域同质性、形状和边界的图像特征并加以组合,提升分割精度;(3)合并策略的选择,针对寻优范围不同列出面向全局、面向局部以及混合区域合并等三种合并策略,并分析各自的特点;(4)尺度参数的设置,针对面向局部的区域合并策略提出递增的尺度参数序列控制方法,生成边界一致的多尺度分割结果;(5)分割树的设计,利用树中不同层级的节点表达不同尺度的分割区域,可快速输出多尺度分割结果.研究成果可应用于高分辨率遥感图像面向对象分析、地物目标识别和信息提取.

MPEG-4编码的现状和研究

随着计算机和通信技术的发展，多媒体编码进入了一个新的时代，即将公布的ＭＰＥＧ－４国际标准表明基于对象的编码、基于模型的编码等第二代编码技术趋于成熟．文中首先从ＭＰＥＧ－４所支持的各种视频对象及其特点、场景的描述和不同应用的框架／级别等３个方面讨论了新的编码标准的主要内容和现状．新的标准用于实际应用还需要提供大量的标准之外的配套工具和研究．在随后的内容中，文中讨论了图像和视频的分割、全景图像的生成、人脸的检测与跟踪、２Ｄ网格模型的建立与跟踪以及３Ｄ人脸的分析和合成等相关领域的研究和进展情况．

综合运用影像对象多种特征的土地利用/土地覆被分类方法探讨——以兰州秦王川地区为例

运用基于对象的影像分析方法,对秦王川地区1994、2001、2011、2013年同时相的4期Landsat 5 TM、Landsat 8 OLI卫星影像数据进行影像分割,综合运用影像对象的光谱、纹理、形状和邻域特征,构建了分类规则集,实现了各期土地利用/土地覆被专题制图.结果表明4期影像的分类结果精度均满足制图要求与动态监控,准确地反映了过去20 a来秦王川地区土地利用/土地覆被的状况.

基于对象的最优尺度建筑物信息提取方法

针对基于像素分析方法不适用于高分辨率影像信息提取的问题,提出一种基于对象的图像分析方法来进行城市建筑信息提取。采用多分辨率图像分割方法得到图像对象,提出非监督的最优尺度判定方法解决单尺度分割造成的欠分割和过分割问题。在对象分类提取过程中,结合LiDAR数据的地形表面高程信息和光谱信息对建筑物进行提取,并利用尺寸、空间位置等信息进行误分类修正。实验区域共提取出18个建筑目标,结果表明所提出的方法有效可行。

结合尺度空间的面向对象高分辨率影像城市道路提取

基于遥感影像的城市道路提取对于城市建设、规划和地图更新等有重要意义。针对高分辨率遥感影像城市道路网的复杂性,结合尺度空间思想提出一种面向对象的城市道路自动提取算法。在此基础上,使用Canny算子获取像元簇梯度图,并进行标记分水岭分割得到区域对象;建立城市道路与几何、光谱特征相关的道路规则,从分割结果中筛选出道路区域对象;使用形态学方法提取道路区域的骨架,并对骨架进行连接、光滑等后处理,最后输出道路网提取结果。实验结果表明,该方法用于复杂城市道路的高精度自动提取,对城市道路网更新有一定参考意义。

语义网络引导的遥感图像分类

为将领域知识与基于对象的图像分析范式有效地结合起来,以解决遥感图像分析中的语义鸿沟问题,提出了一种语义网络引导的遥感图像分类方法。该方法包含分割、预分类、结果修正3个步骤。采用语义网络引导的分类方法进行预分类,使用定性匹配度进行图像对象与概念匹配判定,定义了一种定量匹配度,并将其用于对预分类结果的修正。方法减小了图像对象与概念进行匹配的搜索空间,降低了运算复杂度。实验结果证明了该方法的有效性。