A Robust Texture Analysis and Classification Approach for Urban Land‐Use and Land‐Cover Feature Discrimination

Abstract

Attempts to analyze urban features and to classify land use and land cover directly from high‐resolution satellite data with traditional computer classification techniques have proven to be inefficient for two primary reasons. First, urban landscapes are composed of complex features. Second, traditional classifiers employ spectral information based on single pixel value and ignore a great amount of spatial information. Texture plays an important role in image segmentation and object recognition, as well as in interpretation of images in a variety of applications. This study analyzes urban texture features in multi‐spectral image data. Recent developments in the very powerful mathematical theory of wavelet transforms have received overwhelming attention by image analysts. An evaluation of the ability of wavelet transform in urban feature extraction and classification was performed in this study, with six types of urban land cover features classified. The preliminary results of this research indicate that the accuracy of texture analysis in classifying urban features in fine resolution image data could be significantly improved with the use of wavelet transform approach.     

基于多时相SAR数据的大区域城镇信息变化检测

多时相SAR变化检测技术,通过分析同一区域不同时相的SAR数据可以检测地表地物目标的变化信息,在土地资源调查及监测管理方面具有广泛应用。本文将长时间SAR图像相干性特征与目标幅度信息进行融合,并采用多时相SAR数据堆栈处理方法进行大区域城建目标分类及变化信息检测。最后采用13景ALOS PALSAR数据对中国天津地区2007~2010年期间的城建区域变化进行了检测实验,得到了良好的实验结果,并验证方法有效性。     

相干变化指数法在震害快速评估中的应用

合成孔径雷达(SAR)具有全天候全天时的特点,为快速展开地震救援,评估震害损失提供了重要的数据支持。本文基于汶川震中东南平原地区的ENVISATASAR影像,利用相位干涉方法处理得到相干变化指数图像,对6个评估区域的像元相干变化指数进行统计分析,以相干变化指数平均值作为区分建筑物基本完好与损毁的阈值,并与实地调查结果相比,精度基本达到70%。验证干涉相干变化指数法对震害快速评估有效的同时,也为震区提供震害救援的依据。     

Urban land-use classification by combining high-resolution optical and long-wave infrared images

Abstract

Multi-sensor and multi-resolution source images consisting of optical and long-wave infrared (LWIR) images are analyzed separately and then combined for urban mapping in this study. The framework of its methodology is based on a two-level classification approach. In the first level, contributions of these two data sources in urban mapping are examined extensively by four types of classifications, i.e. spectral-based, spectral-spatial-based, joint classification, and multiple feature classification. In the second level, an objected-based approach is applied to decline the boundaries. The specificity of our proposed framework not only lies in the combination of two different images, but also the exploration of the LWIR image as one complementary spectral information for urban mapping. To verify the effectiveness of the presented classification framework and to confirm the LWIR’s complementary role in the urban mapping task, experiment results are evaluated by the grss_dfc_2014 data-set.     

基于灰度和方向一致性的遥感影像道路网提取

从遥感影像中提取道路网是一个经典课题,根据遥感影像中的道路具有灰度和方向一致性的特征,提出一种从遥感影像中提取道路网的新方法。首先根据遥感影像的灰度信息和方向方差信息建立灰度和方向一致性准则分割模型,由此可从遥感影像中提取基本的道路网轮廓,然后再针对道路区域存在非道路点:空洞和裂缝等情况,采用膨胀、腐蚀等操作去除杂乱物块,最后通过数学形态学操作提取出道路网。实验结果表明,该方法能够适用道路、建筑物、植被等多种复杂地物的城市遥感影像中提取道路网,且能获得较好地提取效果。     

Use of Principal Component Analysis (PCA) of Remote Sensing Images in Wetland Change Detection on the Kafue Flats,Zambia

Abstract

The paper describes the use of Principal Component Analysis (PCA) of remote sensing images as a method of change detection for the Kafue Flats, an inland wetland system in southern Zambia. The wetland is under human and natural pressures but is also an important wildlife habitat. A combination of Landsat MSS and TM images were used. The images used were from 24 September 1984 (MSS), 3 September 1988 (MSS), 12 September 1991 (TM) and 20 September 1994 (TM). They were geometrically co‐registered and, in the process, the 80m resolution MSS images were resampled to 30m using nearest neighbour resampling. Preliminary PCA revealed that for the MSS images most of the data variance was in near infrared reflectance while for the TM images it was in mid and thermal infrared bands. Holding sensor type constant, separate inter‐band correlation analysis for each image could indicate whether the wetland was drier or wetter on one date versus another. The 1994 image was made the reference image and equivalent green, red and near infrared bands from the other images were radiometrically normalised with those on the reference image. All the bands, three from each date, were then merged into a twelve‐band image on which PCA for change detection was undertaken. A colour composite of eigen images from the resulting principal components was used in change detection. Hydrological data, indicating long‐term reduced inflow of water into the wetland due to human regulation, help explain some of the wetland change detected. Compared to a classification comparison approach to change detection for this area, PCA was found to be very useful in indicating where change had occurred, though interpretation of the changes was difficult without reference to the input images. The methodology appears to have potential use in habitat monitoring for this wetland area.     

SAR interferometric coherence analysis for snow cover mapping in the western Himalayan region

Abstract

Information of snow cover (SC) over Himalayan regions is very important for regional climatological and hydrological studies. Precise monitoring of SC in the Himalayan region is essential for water supply to hydropower stations, irrigation requirements, and flood forecasting. Microwave remote sensing has all weather, day and night earth observation capability unlike optical remote sensing. In this study, spaceborne synthetic aperture radar interferometric (InSAR) coherence analysis is used to monitor SC over Himalayan rugged terrain. The feasibility of monitoring SC using synthetic aperture radar (SAR) interferometry depends on the ability to maintain coherence over InSAR pair acquisition time interval. ERS-1/2 InSAR coherence and ENVISAT ASAR InSAR coherence images are analyzed for SC mapping. Data sets of winter and of snow free months of the Himalayan region are taken for interferogram generation. Coherence images of the available data sets show maximum decorrelation in most of the area which indicates massive snowfall in the region in the winter season and melting in the summer. Area showing coherence loss due to decorrelation is mapped as a snow-covered area. The result is validated with field observations of snow depth and it is found that standing snow is inversely related to coherence in the Himalayan region.     

A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery

Abstract

Land use/land cover (LULC) classification with high accuracy is necessary, especially in eco-environment research, urban planning, vegetation condition study and soil management. Over the last decade a number of classification algorithms have been developed for the analysis of remotely sensed data. The most notable algorithms are the object-oriented K-Nearest Neighbour (K-NN), Support Vector Machines (SVMs) and the Decision Trees (DTs) amongst many others. In this study, LULC types of Selangor area were analyzed on the basis of the classification results acquired using the pixel-based and object-based image analysis approaches. SPOT 5 satellite images with four spectral bands from 2003 and 2010 were used to carry out the image classification and ground truth data were collected from Google Earth and field trips. In pixel-based image analysis, a supervised classification was performed using the DT classifier. On the other hand, object-oriented (K-NN) image analysis was evaluated using standard nearest neighbour as classifier. Subsequently SVM object-based classification was performed. Five LULC categories were extracted and the results were compared between them. The overall classification accuracies for 2003 and 2010 showed that the object-oriented (K-NN) (90.5% and 91%) performed better results than the pixel-based DT (68.6% and 68.4%) and object-based SVM (80.6% and 78.15%). In general, the object-oriented (K-NN) performed better than both DTs and SVMs. The obtained LULC classification maps can be used to improve various applications such as change detection, urban design, environmental management and zooning.     

基于比值法的多源多时相数据城区变化检测

针对中、低分辨率影像采用常规分类方法进行变化检测无法取得理想效果的问题,本文提出了一种基于比值法的雷达数据和光学影像相结合的城市变化检测方法。该方法综合雷达数据和光学影像的优势,以赣州定南县为研究对象,首先利用比值法分别对两种数据源进行城市变化检测,然后通过分析城区地物目标散射特性,对多个时相变化情况进行真彩色合成,最后结合城市建设规划进行分析验证,从而完成城市实时动态变化检测。试验结果表明,利用多源多时相数据进行比值法可成功地提取出试验区域的变化信息,在城区建筑变化检测中,纹理信息更清晰,准确性更高。     

基于测绘地理信息更新的土地利用变化动态发现

随着城市人口增长、城镇化趋势扩大、可利用资源急剧减少,动态地掌握城市土地利用现状变化,是有效利用城市的土地资源、加强土地资源监管的重要基础,土地利用现状的调查是最基础的土地资源调查。本文通过分析上海土地利用调查现状和测绘地理信息资源情况,基于测绘地理信息更新机制,结合“多测合一”、季度卫星影像、基础测绘、细化调查等测绘工作,探索了一种动态、可靠的多周期、全覆盖的土地利用变化动态发现方法,并通过试点提出了土地利用变化发现的建议。     

Detecting and Enumerating New Building Structures Utilizing Very‐High Resolution Imaged Data and Image Processing

Abstract

Information on the number and type of new building structures is required by urban and transportation planners and the real estate industry. The goal of this paper is to explore the potential of high resolution imagery for meeting public and private sector demands for information on new buildings. The value of 1 m, 5 m, and 10 m panchromatic and 1 m color scanned aerial photography images acquired in 1997 and 1998 for a study area within the City of San Diego, California is assessed for general change detection and building enumeration. Both semi‐automated and interactive change‐detection approaches are evaluated. We demonstrate that interactive, visual‐based approaches appear to be the most accurate (within 1% of actual count) and efficient approach for generating information on the number of new buildings associated with single family residential land use. More automated approaches to detecting and enumerating image microfeatures may be useful as enhancements for visual‐based assessments and may be practical in areas composed mostly of large buildings associated with commercial and industrial land use. The highest accuracy for automated approaches was an undercounting of 11% for residential buildings and overcounting of 20% for those associated with commercial and industrial land use.     

结合IR-MAD与均值漂移算法的密集城区遥感影像变化检测

不同时相遥感影像变化检测已成为土地利用变更调查、城市扩张分析、自然灾害分析及其他环境问题必不可少的技术手段之一。本文提出了一种结合IR-MAD与均值漂移算法的密集城区遥感影像变化检测方法。该方法通过伪不变特征法完成两期影像的相对辐射校正,有效改善影像间的配准误差,并利用IR-MAD算法对校正后的影像进行迭代运算,采用均值漂移算法对迭代后的影像进行分割,同时运用形态学方法处理分割后的影像,最终提取变化图斑。试验结果表明,该方法可以有效检测出变化区域,可应用于城市地表覆盖的变化检测。     

A new kernel method for hyperspectral image feature extraction

Abstract

Hyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers. However, the increasing spectral dimensions, as well as the information redundancy, make the analysis and interpretation of hyperspectral images a challenge. Feature extraction is a very important step for hyperspectral image processing. Feature extraction methods aim at reducing the dimension of data, while preserving as much information as possible. Particularly, nonlinear feature extraction methods (e.g. kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing, due to their good preservation of high-order structures of the original data. However, conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction, and this leads to poor performances for post-applications. This paper proposes a novel nonlinear feature extraction method for hyperspectral images. Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window), the proposed method explores the use of image segmentation. The approach benefits both noise fraction estimation and information preservation, and enables a significant improvement for classification. Experimental results on two real hyperspectral images demonstrate the efficiency of the proposed method. Compared to conventional KMNF, the improvements of the method on two hyperspectral image classification are 8 and 11%. This nonlinear feature extraction method can be also applied to other disciplines where high-dimensional data analysis is required.     

Detecting vehicle traffic patterns in urban environments using taxi trajectory intersection points

Abstract

Detecting and describing movement of vehicles in established transportation infrastructures is an important task. It helps to predict periodical traffic patterns for optimizing traffic regulations and extending the functions of established transportation infrastructures. The detection of traffic patterns consists not only of analyses of arrangement patterns of multiple vehicle trajectories, but also of the inspection of the embedded geographical context. In this paper, we introduce a method for intersecting vehicle trajectories and extracting their intersection points for selected rush hours in urban environments. Those vehicle trajectory intersection points (TIP) are frequently visited locations within urban road networks and are subsequently formed into density-connected clusters, which are then represented as polygons. For representing temporal variations of the created polygons, we enrich these with vehicle trajectories of other times of the day and additional road network information. In a case study, we test our approach on massive taxi Floating Car Data (FCD) from Shanghai and road network data from the OpenStreetMap (OSM) project. The first test results show strong correlations with periodical traffic events in Shanghai. Based on these results, we reason out the usefulness of polygons representing frequently visited locations for analyses in urban planning and traffic engineering.     

Detection of leaf structures in close-range hyperspectral images using morphological fusion

Abstract

Close-range hyperspectral images are a promising source of information in plant biology, in particular, for in vivo study of physiological changes. In this study, we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information. The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation, disease infections, and environmental conditions have in plants. We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing. Experimental results demonstrate the efficiency of our fusion approach, with significant improvements over some conventional methods.     

利用城市公共摄像头分析夜间灯光变化规律

与卫星影像相比,摄像机影像数据具有更高的时间和空间分辨率。互联网共享的摄像头视频提供了丰富的夜间灯光信息,但数据的获取难度较大。针对实时视频数据设计特定爬虫程序进行数据获取,再基于获取的视频进行数据挖掘。通过分析日本东京某实验区的影像,发现城市夜间灯光表现复杂且存在一定的随机性,但是整夜的夜间灯光随着时间呈现明显减弱的趋势,并且部分区域呈现出先增加后减少等更为复杂的变化规律,此外不同建筑物侧面灯光趋势也存在差异。     

Analysis of large-scale UAV images using a multi-scale hierarchical representation

Abstract

Unmanned aerial vehicle (UAV)-based imaging systems have many superiorities compared with other platforms, such as high flexibility and low cost in collecting images, providing wide application prospects. However, the acquisition of the UAV-based image commonly results in very high resolution and very large-scale images, which poses great challenges for subsequent applications. Therefore, an efficient representation of large-scale UAV images is necessary for the extraction of the required information in a reasonable time. In this work, we proposed a multi-scale hierarchical representation, i.e. binary partition tree, for analyzing large-scale UAV images. More precisely, we first obtained an initial partition of images by an oversegmentation algorithm, i.e. the simple linear iterative clustering. Next, we merged the similar superpixels to build an object-based hierarchical structure by fully considering the spectral and spatial information of the superpixels and their topological relationships. Moreover, objects of interest and optimal segmentation were obtained using object-based analysis methods with the hierarchical structure. Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake and the mosaic of images in the South-west of Munich demonstrate the effectiveness and efficiency of our proposed method.     

结合像元级和目标级的高分辨率遥感影像建筑物变化检测

快速、精准的建筑物变化检测对城市规划建设等业务管理具有重要意义。随着卫星遥感技术的快速发展,基于高分辨率遥感影像的建筑物变化检测得到了广泛关注。针对像元级建筑物变化检测方法往往精度不足而目标级建筑物变化检测方法过程烦琐等问题,本文提出结合像元级和目标级的高分辨率遥感影像建筑物变化检测方法。首先综合高分辨率遥感影像的多维特征,利用随机森林分类器进行影像集分类,以获取像元级建筑物变化检测结果;然后对后时相遥感影像进行图像分割,获得影像对象;最后融合像元级建筑物变化检测结果和影像对象,识别变化的建筑物目标。利用双时相QuickBird高分辨率遥感影像进行建筑物变化检测试验,结果表明：本文提出的方法能够削弱光照、观测角度等环境差异对建筑物变化检测的影响,显著改善建筑物变化的检测精度。     

干旱—半干旱地区地表特征探测的成像雷达干涉测量方法与模型

从估测干涉测量雷达数据相关性出发，通过利用欧洲资源卫星1号和2号获取的重轨干涉测量雷达数据，开展新疆喀什试验区地表土地类型的识别与分类，区分和识别出裸土，盐碱地，灌丛，裸岩/戈壁，沼泽和水体6类土地类型。并对不同土地类型的后向散射特性和相关性进行定量分析。探讨干涉测量数据相关性干旱-半干旱地区地表特征的关系，从而建立了干旱-半干旱地区地表变化的干涉测量时相关模型。为干旱-半干旱地区利用干涉测量数据进行地表变化监测提供理论依据。     

基于综合指数分析的城市建筑用地信息提取研究

以西安市长安区TM影像为例,研究关于城市建筑用地信息快速、准确提取的方法。通过对归一化差异型指数构成原理的分析,选取土壤调节植被指数（ SAVI ）、归一化水体指数（ NDWI ）和归一化差异型建筑指数（ NDBI ）来提取植被、水体和城市建筑用地专题影像,并将其构建为一幅新影像,分析新影像谱间特征,运用逻辑运算将城市建筑用地信息提取出来。本文方法总体提取效果十分有效,尤其是对于面积较大的城市建筑用地,总精度高达85．3％。综合指数法弥补了单靠某一指数提取城市建筑用地信息的不足,提取结果客观可信,是一种不经人为干预、快速有效的提取城市建筑用地的方法。