The improvement of an object-oriented classification using multi-temporal MODIS EVI satellite data

Abstract

This paper investigates the contribution of multi-temporal enhanced vegetation index (EVI) data to the improvement of object-based classification accuracy using multi-spectral moderate resolution imaging spectral-radiometer (MODIS) imagery. In object-oriented classification, similar pixels are firstly grouped together and then classified; the produced result does not suffer the speckled appearance and closer to human vision. EVI data are from the MODIS sensor aboard Terra spacecraft. 69 EVI data (scenes) were collected during the period of three years (2001–2003) in a mountainous vegetated area. These data sets were used to study the phenology of the land cover types. Different land cover types show distinct fluctuations over time in EVI values and this information might be used to improve object-oriented land cover classification. Two experiments were carried out: one was only with single date MODIS multispectral data, and the other one including also the 69 EVI images. Eight classes were distinguished: temperate forest, tropical dry forest, grassland, irrigated agriculture, rain-fed agriculture, orchards, lava flows and human settlement. The two classifications were evaluated with independent verification data, and the results showed that with multi-temporal EVI data, the classification accuracy was improved 5.2%. Evaluated by McNemar's test, this improved was significant, with significance level p=0.01.     

Near real-time characterisation of urban environments: a holistic approach for monitoring dengue fever risk areas

Despite frequent use of digital devices in everyday life, cost-effective measurement of public health issues in urban areas is still challenging. This study was, therefore, planned to extract land-use types using object-based and spatial metric approaches to explore the dengue incidence in relation to the surrounding environment in near real-time using Google and Advanced Land Observation Satellite images. The characterised image showed useful classification of an urban area with 77% accuracy and 0.68 kappa. Geospatial analysis on public health data indicated that most of the dengue cases were found in densely populated areas surrounded by dense vegetation. People living in independent houses having sparsely vegetated surroundings were found to be less vulnerable. Disease incidence was more prevalent in people of 5–24 years of age (67%); while in terms of occupation, mostly students, the unemployed, labourers and farmers (88%) were affected. In general, males were affected slightly more (10%) than females. Proximity analyses indicated that most of the dengue cases were around institutions (40%), religious places (18%) and markets (15%). Thus, usage of Digital Earth scalable tools for monitoring health issues would open new ways for maintaining a healthy and sustainable society in the years ahead.     

Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region

In this study, we test the potential of two different classification algorithms, namely the spectral angle mapper (SAM) and object-based classifier for mapping the land use/cover characteristics using a Hyperion imagery. We chose a study region that represents a typical Mediterranean setting in terms of landscape structure, composition and heterogeneous land cover classes. Accuracy assessment of the land cover classes was performed based on the error matrix statistics. Validation points were derived from visual interpretation of multispectral high resolution QuickBird-2 satellite imagery. Results from both the classifiers yielded more than 70% classification accuracy. However, the object-based classification clearly outperformed the SAM by 7.91% overall accuracy (OA) and a relatively high kappa coefficient. Similar results were observed in the classification of the individual classes. Our results highlight the potential of hyperspectral remote sensing data as well as object-based classification approach for mapping heterogeneous land use/cover in a typical Mediterranean setting.     

SegOptim—A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data

Geographic Object-based Image Analysis (GEOBIA) is increasingly used to process high-spatial resolution imagery, with applications ranging from single species detection to habitat and land cover mapping. Image segmentation plays a key role in GEOBIA workflows, allowing to partition images into homogenous and mutually exclusive regions. Nonetheless, segmentation techniques require a robust parameterization to achieve the best results. Frequently, inappropriate parameterization leads to sub-optimal results and difficulties in comparing distinct methods.Here, we present an approach based on Genetic Algorithms (GA) to optimize image segmentation parameters by using the performance scores from object-based classification, thus allowing to assess the adequacy of a segmented image in relation to the classification problem. This approach was implemented in a new R package called SegOptim, in which several segmentation algorithms are interfaced, mostly from open-source software (GRASS GIS, Orfeo Toolbox, RSGISLib, SAGA GIS, TerraLib), but also from proprietary software (ESRI ArcGIS). SegOptim also provides access to several machine-learning classification algorithms currently available in R, including Gradient Boosted Modelling, Support Vector Machines, and Random Forest.We tested our approach using very-high to high spatial resolution images collected from an Unmanned Aerial Vehicle (0.03 – 0.10 m), WorldView-2 (2 m), RapidEye (5 m) and Sentinel-2 (10 – 20 m) in six different test sites located in northern Portugal with varying environmental conditions and for different purposes, including invasive species detection and land cover mapping. The results highlight the added value of our novel comparison of image segmentation and classification algorithms. Overall classification performances (assessed through cross-validation with the Kappa index) ranged from 0.85 to 1.00. Pilot-tests show that our GA-based approach is capable of providing sound results for optimizing the parameters of different segmentation algorithms, with benefits for classification accuracy and for comparison across techniques. We also verified that no particular combination of an image segmentation and a classification algorithm is suited for all the tasks/objectives. Consequently, it is crucial to compare and optimize available methods to understand which one is more suited for a certain objective.Our approach allows a closer integration between the segmentation and classification stages, which is of high importance for GEOBIA workflows. The results from our tests confirm that this integration has benefits for comparing and optimizing both processes. We discuss some limitations of the SegOptim approach (and potential solutions) as well as a future roadmap to expand its current functionalities.     

极化SAR参数优化与光学波谱相结合的面向对象土地覆盖分类

提出一种基于极化参数优化的面向对象分类方法。该方法结合光学和SAR数据,有效提高了对地物的识别能力。本文方法的关键在于:在■分解中,使用光学影像指导SAR影像选择同质点,使其更精确地估计极化参数并结合光学波谱信息作为输入特征;使用面向对象的分类方法,仅将光学影像作为分割输入,避免SAR噪声引起的分割错误。以美国Bakersfield地区的Sentinel-1/2数据为例,确定7种地物类型,对比分析不同输入与不同分类器对分类结果的影响。研究表明,优化输入参数在纹理丰富区域能够有效提高分类精度;面向对象的分类结果更加稳定并较好地维持地表几何特征;改进分类方法较传统分类方法总体精度提高了近10%,达到92.6%。     

“21·7”河南极端暴雨多模式预报性能综合评估

采用CRA、邻域TS评分、FSS等多种空间检验方法,对多个不同尺度业务数值模式在“21·7”河南极端暴雨过程中的预报性能进行了综合检验评估,并从低空急流、水汽辐合和热力条件等方面对模式偏差原因进行诊断分析。结果表明:1)在24 h大暴雨降水位置预报偏差上,WARMS预报性能最优,GRAPES_3 km次优;大暴雨降水预报范围与实况相当时,RMAPS降水强度预报较实况明显偏强,且落区较实况出现持续偏西的特征;2) GRAPES_3 km和WARMS预报3 h累积降水的位置偏差更多表现在经向方向上,且离散度较大,但在更临近预报时效经向偏差明显减小,而纬向偏差则随预报时效变化较小;RMAPS的位置偏差主要表现在纬向方向上,19—21日分别有86.7％、91.3％、72.7％的降水个体预报偏西;3) WARMS对低空急流和水汽辐合预报偏弱导致其对19—20日降水强度估计不足,EC和RMAPS对19—20日低空急流预报明显偏西是导致降水落区位置存在偏差的主要原因;MESO对20日急流和水汽辐合发生时间及位置预报较好,但明显偏弱的热力条件导致其缺乏对极端强降水的预报能力;4)21日,MESO和RMAPS预报低空急流过多的偏东分量导致其在太行山陡峭地形处预报了偏强的地形增幅降水;EC和WARMS预报低空急流风向更接近实况,但对低层水汽辐合强度和时间的预报偏差导致预报降水个体出现了较明显的经向位置偏差。     

台风“巴威”不同类型降水多模式预报与空间检验对比评估

以传统的检验方法和基于对象诊断评估方法(MODE), 对首次以台风级别影响辽宁的“巴威”台风(2008)的台风暴雨过程不同性质降水的多模式(ECMWF、CMA_MESO 10KM、CMA_MESO 3km和睿图东北模式)预报结果进行了检验评估。结果表明: 受“巴威”远距离和本体影响, 辽宁省先后出现对流型降水和稳定型降水, 传统检验和MODE检验结果均表明, 多模式的对流型降水预报效果要优于稳定型降水, 这很可能是多模式对于台风北上减弱产生稳定型降水的影响系统的预报强度偏差大导致, 在今后预报中务必注意台风强度预报偏差对本体稳定型为主的降水的影响。传统检验结果中, CMA_MESO 3km和ECMWF模式的评分较高, 并且对于对流型降水雨带的形状、范围和质心距离、交集面积预报效果最好, ECMWF模式对稳定型降水也有着较高的目标相似度评分。尽管睿图东北模式由于对10.0 mm以上量级降水较高的空报和漏报率, 而导致TS评分偏低; 但在MODE检验结果中, 东北模式预报的强降水雨带的中心位置、降水强度和范围均接近实况, 目标相似度更高, 尤其在对流型降水阶段目标相似度达到了1.00, 模式对于对流型降水预报有着较好的可参考性。     

Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China

Previous studies have demonstrated urban built-up areas can be derived from nighttime light satellite (DMSP-OLS) images at the national or continent scale. This paper presents a novel object-based method for detecting and characterizing urban spatial clusters from nighttime light satellite images automatically. First, urban built-up areas, derived from the regionally adaptive thresholding of DMSP-OLS nighttime light data, are represented as discrete urban objects. These urban objects are treated as basic spatial units and quantified in terms of geometric and shape attributes and their spatial relationships. Next, a spatial cluster analysis is applied to these basic urban objects to form a higher level of spatial units – urban spatial clusters. The Minimum Spanning Tree (MST) is used to represent spatial proximity relationships among urban objects. An algorithm based on competing propagation of objects is proposed to construct the MST of urban objects. Unlike previous studies, the distance between urban objects (i.e., the boundaries of urban built-up areas) is adopted to quantify the edge weight in MST. A Gestalt Theory-based method is employed to partition the MST of urban objects into urban spatial clusters. The derived urban spatial clusters are geographically delineated through mathematical morphology operation and construction of minimum convex hull. A series of landscape ecologic and statistical attributes are defined and calculated to characterize these clusters. Our method has been successfully applied to the analysis of urban landscape of China at the national level, and a series of urban clusters have been delimited and quantified.     

基于面向对象技术的建筑物震害识别方法研究

2010年1月12日海地发生7.3级大地震,造成首都太子港大量建筑物损毁.从震后甚高分辨率遥感影像中可以发现在倒塌和部分倒塌建筑物的周围存在很多瓦砾.因此,可以将瓦砾作为建筑物倒损的震害特征.分别采用基于像元的方法、面向对象的方法、综合地统计学纹理的面向对象方法自动提取建筑物瓦砾,并对3种方法的分类精度进行了评价,研究...     

面向地面点识别的机载LiDAR点云分割方法研究

提出一种面向地面目标识别的机载LiDAR点云分割方法。方法首先求每个激光脚点的法向量和残差,由此确定种子点和种子平面;然后对种子点进行区域生长,生长的过程中以邻接点到种子平面的距离和邻接点与种子点的法向量角度差作为相似性的度量标准;当全部的扫描点都被划分,则算法终止。实验表明,文中提出的分割方法,对于城区区域和农村区域的地面目标有很好的识别效果。