Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping

Semi-automated geomorphological mapping techniques are gradually replacing classical techniques due to increasing availability of high-quality digital topographic data. In order to efficiently analyze such large amounts of data, there is a need for optimizing the processing of automated mapping techniques. In this context, we present a novel approach to semi-automatically map alpine geomorphology using stratified object-based image analysis. We used a 1 m Digital Terrain Model (DTM) derived from laser altimetry data from a mountainous catchment from which we calculated various Land-Surface Parameters (LSPs). The LSPs ‘slope angle’ and ‘topographic openness’ have been combined into a single composite layer for selecting reference material and delineating training samples. We developed a novel method to semi-automatically assess segmentation results by comparing 2D frequency distribution matrices of training samples and image objects. The segmentation accuracy assessment allowed us to automate optimization of the scale parameter and LSPs used for segmentation. We concluded that different geomorphological feature types have different sets of optimal segmentation parameters. The feature-dependent parameters were used in a new approach of stratified feature extraction for classifying karst, glacial, fluvial and denudational landforms. In this way, we have used stratified object-based image analysis to semi-automatically extract contrasting geomorphological features from high-resolution digital terrain data. A further step would be to also automate the optimization of classification rules. We would then be able to create a library of feature characteristics that could be transferred and applied to other mountain regions and further automate geomorphological mapping strategies.     

Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data

Urbanization is commonly accepted as an important contributor to the growth of man-made structures and as a rapid convertor of natural environments to impervious surfaces. Roofs are one class of impervious surface whose materials can highly influence the quality of urban surface water. In this study, two data sources, WorldView-2 (WV-2) imagery and a combination of WV-2 and lidar data, were utilized to map intra-urban targets, with 13 classes. Images were classified using object-based image analysis. Pixel-based classifications using the support vector machine (SVM) and maximum likelihood (ML) methods were also tested for their abilities to use both lidar data and WV-2 imagery. ML and SVM classifications yielded overall accuracies of 72.46% and 75.69%, respectively. The results of these classifiers exhibited mixed pixels and salt-and-pepper effects. Spectral, spatial, and textural attributes as well as various spectral indices were employed in the object-based classification of WV-2 imagery. Feature classification of WV-2 imagery resulted in 85% overall accuracy. Lidar data were added to WV-2 imagery to assist in the spatial and spectral diversities of urban infrastructures. Classified image made from WV-2 imagery and lidar data achieved 92.84% overall accuracy. Rule-sets of these fused datasets effectively reduced the spectral variation and spatial heterogeneities of intra-urban classes, causing finer boundaries among land-cover classes. Therefore, object-based classification of WV-2 imagery and lidar data efficiently improved detailed characterization of roof types and other urban surface materials.     

Land-cover classification of an intra-urban environment using high-resolution images and object-based image analysis

Detailed, up-to-date information on intra-urban land cover is important for urban planning and management. Differentiation between permeable and impermeable land, for instance, provides data for surface run-off estimates and flood prevention, whereas identification of vegetated areas enables studies of urban micro-climates. In place of maps, high-resolution images, such as those from the satellites IKONOS II, Quickbird, Orbview and WorldView II, can be used after processing. Object-based image analysis (OBIA) is a well-established method for classifying high-resolution images of urban areas. Despite the large number of previous studies of OBIA in the context of intra-urban analysis, there are many issues in this area that are still open to discussion and resolution. Intra-urban analysis using OBIA can be lengthy and complex because of the processing difficulties related to image segmentation, the large number of object attributes to be resolved and the many different methods needed to classify various image objects. To overcome these issues, we performed an experiment consisting of land-cover mapping based on an OBIA approach using an IKONOS II image of a southern sector of São José dos Campos city (covering an area of 12 km² with 50 neighbourhoods), which is located in São Paulo State in south-eastern Brazil. This area contains various occupation and land-use patterns, and it therefore contains a wide range of intra-urban targets. To generate the land-cover map, we proposed an OBIA-based processing framework that combines multi-resolution segmentation, data mining and hierarchical network techniques. The intra-urban land-cover map was then evaluated through an object-based error matrix, and classification accuracy indices were obtained. The final classification, with 11 classes, achieved a global accuracy of 71.91%.     

面向对象的高分辨率SAR图像处理及应用

目的随着合成孔径雷达(SAR)技术和分辨率的不断提高,越来越多的空间细节呈现在高分辨率SAR影像上。与此同时,SAR图像的数据量越来越大,人们对其应用需求也越来越高,这使得传统的基于像素的SAR处理方法不再适用。面向对象分析技术以像元集合——"对象"为分析单元,为高分辨率遥感图像处理提供了有效的思路,并日渐成为遥感、摄影测量以及GIS等领域所关注的对象和研究热点之一。目前该技术在光学遥感中已经得到了广泛的应用,但在SAR图像处理中的应用还处于起步阶段。方法本文在简要阐述面向对象分析技术起源和特点的基础上,对SAR图像面向对象技术中常用的多尺度分割算法进行了分类分析,接着对面向对象技术在SAR遥感的应用方向进行全面介绍,最后对面向对象技术在SAR上的应用进行了总结与展望。结果面向对象分析技术在SAR图像处理中的应用主要分为以下五个方面:地物分类、城市信息提取、变化检测、海洋应用、森林应用。结论面向对象分析技术在解决高分辨率SAR图像尺度效应、抑制噪声等方面有着重要作用。目前,国外学者在基于SAR的面向对象分析技术研究上已经取得了一定的进展,但总体上该技术仍面临诸多问题,需要进一步的研究和完善。     

Impervious surface extraction from high-resolution satellite image using pixel- and object-based hybrid analysis

High-resolution satellite imaging provides a wealth of details about the Earth's surface, but it is still a challenge to determine the complex, impervious surface from high-resolution satellite images. A pixel- and object-based hybrid analysis (POHA) method is proposed for the extraction task. Pixel-based analysis is first applied to provide prior knowledge; then, based on prior knowledge, the subsequent object-based analysis is simply to find similar rather than new impervious objects using a weighted minimum distance strategy. In order to combine different image analysis methods, the segmentation masking strategy was introduced to transform the image analysis from pixel level to object level. A QuickBird image of Hangzhou City in China was used to test POHA. Furthermore, POHA was compared with both the pixel-based analysis and object-based image analysis (OBIA) methods, showing that POHA runs with limited human–computer interactions, and can provide accurate impervious surface mapping.     

Classification of impervious land-use features using object-based image analysis and data fusion

The proportion of impervious area within a watershed is a key indicator of the impacts of urbanization on water quality and stream health. Research has shown that object-based image analysis (OBIA) techniques are more effective for urban land-cover classification than pixel-based classifiers and are better suited to the increased complexity of high-resolution imagery. Focusing on five 2-km² study areas within the Black Creek sub-watershed of the Humber River, this research uses eCognition® software to develop a rule-based OBIA workflow for semi-automatic classification of impervious land-use features (e.g., roads, buildings, Parking Lots, driveways). The overall classification accuracy ranges from 88.7 to 94.3%, indicating the effectiveness of using an OBIA approach and developing a sequential system for data fusion and automated impervious feature extraction. Similar accuracy results between the calibrating and validating sites demonstrates the strong potential for the transferability of the rule-set from pilot study sites to a larger area.     

The application of discriminant analysis for mapping cereals and pasture using object-based features

High mapping accuracies occur where crops differ spectrally (e.g.>90.0%; canola, corn, soybeans) and vice versa (e.g. <75.0%; cereals and pasture). Developing improved mapping methods has been an ongoing priority of Agriculture and Agri-Food Canada (AAFC) remote-sensing science. To this end, this study tests a data-driven object-based classification method using Discriminant Analysis (DA) method for mapping cereals and pasture from satellite data. In this approach, variables (number >400) derived from the image segmentation and object-based feature extraction of multi-date and multi-band optical (RapidEye) and microwave (RADARSAT-2) imagery were applied in a data-driven approach. We use in situ and satellite information collected over two study sites with different levels of heterogeneity (Winnipeg, Brandon) situated in the Canadian Prairies during the 2013 growing season to assess: (a) the type of DA model that most accurately classifies the cereals and pasture cover classes; and (b) how the classification accuracies obtained by the application of this DA model compare to those obtained from more traditional Maximum Likelihood (ML), Decision Tree (DT), and Random Forest (RF) classifications. We found that our DA-based approach was able to map cereals and pastures at our two study sites with the highest accuracies, but these accuracies did not improve significantly with the use of more complex DA model (including priori classification probabilities, more input principle components (PCs), the use of weights proportional to field area). Our results are encouraging for the wider application of the data-driven pre-processing of the inputs to the image classification by DA.     

Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests

The random forest (RF) classifier is a relatively new machine learning algorithm that can handle data sets with large numbers and types of variables. Multi-scale object-based image analysis (MOBIA) can generate dozens, and sometimes hundreds, of variables used to classify earth observation (EO) imagery. In this study, a MOBIA approach is used to classify the land cover in an area undergoing intensive agricultural development. The information derived from the elevation data and imagery from two EO satellites are classified using the RF algorithm. Using a wrapper feature selection algorithm based on the RF, a large initial data set consisting of 418 variables was reduced by ～60%, with relatively little loss in the overall classification accuracy. With this feature-reduced data set, the RF classifier produced a useable depiction of the land cover in the selected study area and achieved an overall classification accuracy of greater than 90%. Variable importance measures produced by the RF algorithm provided an insight into which object features were relatively more important for classifying the individual land-cover types. The MOBIA approach outlined in this study achieved the following: (i) consistently high overall classification accuracies (>85%) using the RF algorithm in all models examined, both before and after feature reduction; (ii) feature selection of a large data set with little expense to the overall classification accuracy; and (iii) increased interpretability of classification models due to the feature selection process and the use of variable importance scores generated by the RF algorithm.     

Content-based image retrieval techniques for the analysis of dermatological lesions using particle swarm optimization technique

This method presents extraction of effective color and shape features for the analysis of dermatology images. We employ three phases of operation in order to perform efficient retrieval of images of skin lesions. Our proposed algorithm used color and shape feature vectors and the features are normalized using Min–Max normalization. Particle swarm optimization (PSO) technique for multi-class classification is used to converge the search space more efficiently. The results using receiver operating characteristic (ROC) curve proved that the proposed architecture is highly contributed to computer-aided diagnosis of skin lesions. Experiments on a set of 1450 images yielded a specificity of 98.22% and a sensitivity of 94%. Our empirical evaluation has a superior retrieval and diagnosis performance when compared to the performance of other works. We present explicit combinations of feature vectors corresponding to healthy and lesion skin.     

Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination of IKONOS images

Predicting structural organization and biomass of tropical forest from remote sensing observation constitutes a great challenge. We assessed the potential of Fourier-based textural ordination (FOTO) to estimate mangrove forest biomass from very high resolution (VHR) IKONOS images. The FOTO method computes texture indices of canopy grain by performing a standardized principal component analysis (PCA) on the Fourier spectra obtained for image windows of adequate size. For two distinct study sites in French Guiana, FOTO indices derived from a 1 m panchromatic channel were able to consistently capture the whole gradient of canopy grain observed from the youngest to decaying stages of mangrove development, without requiring any intersite image correction. In addition, a multiple linear regression based on the three main textural indices yielded accurate predictions of mangrove total aboveground biomass. Since FOTO indices did not saturate for high biomass values, predictions were furthermore unbiased, even for levels above 450 t of dry matter per hectare. Maps of canopy texture (with RGB coding) and biomass were then produced over 8000 ha of unexplored, low accessibility mangrove. Applying the FOTO method to the 4 m near-infrared channel yielded acceptable results with some limitations for characterization of juvenile mangrove types. We finally discuss the influence of technical aspects pertaining to VHR images and to FOTO implementation (especially the size of the window used to compute Fourier spectra) and we evoke the interesting prospect of broad regional validity offered by the method to characterize high biomass tropical forest from standardized measures of canopy grain.     

Mapping vegetation in a late Quaternary landform of the Amazonian wetlands using object-based image analysis and decision tree classification

Fan-shaped morphologies related to late Quaternary residual megafan depositional systems are common features over wide areas in northern Amazonia. These features were formed by ancient distributary drainage systems that are in great contrast to tributary drainage networks that typify the modern Amazon basin. The surfaces of the Amazonian megafans constitute vegetacional mosaic wetlands with different campinarana types. A fine-scale-resolution investigation is required to provide detailed classification maps for the various campinarana and surrounding forest types associated with the Amazonian megafans. This approach remains to be presented, despite its relevance for analysing the relationship between stages of plant succession and sedimentary dynamics associated with the evolution of megafans. In this work, we develop a methodology for classifying vegetation over a fan-shaped megafan palaeoform from a northern Amazonian wetland. The approach included object-based image analysis (OBIA) and data-mining (DM) techniques combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, land-cover fractions derived by the linear spectral mixing model, synthetic aperture radar (SAR) images, and the digital elevation model (DEM) acquired during the Shuttle Radar Topography Mission (SRTM). The DEM, vegetation fraction, and ASTER band 3 were the most useful parameters for defining the forest classes. The normalized difference vegetation index (NDVI), ASTER band 1, vegetation fraction, and the Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR) transmitting and receiving horizontal polarization (HH) and transmitting horizontal and receiving vertical polarization (HV) were all effective in distinguishing the wetland classes campinarana and Mauritia. Tests of statistical significance indicated the overall accuracies and kappa coefficients (κ) of 88% and 0.86 for the final map, respectively. The allocation disagreement coefficient of 5% and a quantity disagreement value of 7% further attested the statistical significance of the classification results. Hence, in addition to water, exposed soil, and deforestation areas, OBIA and DM were successful for differentiating a large number of open (forest, wood, shrub, and grass campinaranas), forest (terra firme, várzea, igapó, and alluvial), as well as Mauritia wetland classes in the inner and outer areas of the studied megafan.     

Identification and mapping of minerals in drill core using hyperspectral image analysis of infrared reflectance spectra

A Portable Infrared Mineral Analyzer II (PIMA II) field spectrometer was used to measure infrared reflectance spectra (1·3-2·5 μm) of split drill core at 1 cm intervals in both the along-core and cross-core directions. These data were formatted into an image cube similar to that acquired by an imaging spectrometer with 600 spectral channels, and multi-spectral and hyperspectral analysis techniques were used for analysis. Colour images and enhancements provided visual displays of the spectral information, while real-time digital extraction of individual spectra allowed identification of minerals. Absorption band-depth mapping and spectral classification were used to map the spatial distribution of specific minerals in the core. Linear spectral unmixing provided estimated mineral abundances. Analysis results demonstrate that multi-spectral and hyperspectral image analysis methods can be used to produce detailed mineralogical maps of drill core. They suggest that the concepts and analytical techniques developed for analysis of hyperspectral image data can be applied to field and laboratory spectra in a variety of disciplines, and raise the question of the use of hyperspectral scanners in the laboratory.     

Derivation of surface topography and terrain parameters from single satellite image using shape-from-shading technique

This paper evaluates and compares two area-based shape-from-shading algorithms for deriving detailed surface topography and terrain parameters. A Fourier transform-based linear algorithm and an iterative minimization algorithm with integrability constraint are devised and implemented using C++ programming language, and applied to a SPOT panchromatic image. The relative height measurements from the shape-from-shading technique are calibrated into an absolute elevation model using a parametric least-squares adjustment procedure based on a number of ground control points. Surface slope, orientation, and structural lines are also calculated based on the digital elevation model derived from the shape-from-shading technique. The comparative advantages of the two algorithms are discussed, and the combination of two algorithms is also explored. The fundamental requirement for the shape-from-shading algorithms is that the terrain surface under investigation has a relatively homogenous land cover. Source codes in C++ and test data sets are available.     

Towards refined estimation of vegetation carbon stock in Auckland,New Zealand using WorldView-2 and LiDAR data: the impact of scaling

ABSTRACT

It is necessary to estimate carbon (C) stored in urban vegetation for the purpose of carbon accounting and trading. This study aims to develop a refined method for reliably estimating above-ground carbon (AGC) stock of urban vegetation from integrated WorldView-2 imagery and Light Detection And Ranging (LiDAR) data in Auckland, New Zealand. Also assessed in this study is the impact of image resolution on regional AGC estimates by vegetation type. The integration of WorldView-2 imagery with a 2-m digital surface model produced from LiDAR data enables urban vegetation to be mapped into trees (101.5 km²), shrubs (64.9 km²), and grasses (172.2 km²) at a producer’s accuracy over 95.9%. The AGC stock of trees, shrubs and grasses is estimated at 1,134,287, 207,606, and 127,427 Mg C, respectively, from the vegetation map. Overall, the total AGC of all types of vegetation does not vary significantly with image spatial resolution over the range of 5 to 30 m if estimated using the same model. This is because high AGC densities are generalised at a coarser resolution, but the larger pixel size compensates for the decrease. Although the spatial resolution does not affect the most significant spectral predicators of plot-level AGC noticeably, it has an obvious effect on both model accuracy and complexity. Thus, the impact of image resolution on AGC would be pronounced if it were estimated using different models that were the best at a given resolution. Of the three vegetation types, the AGC of shrubs is the most variable with spatial resolution, followed by trees. Thus, the AGC of relatively small but more spatially fragmented vegetation parcels is more susceptible to change in image spatial resolution. The estimation model based on spectral features of vegetation has the lowest room-mean-square-error at 15 m. More research is needed to confirm whether it is true in other natural environments in future studies.     

An analysis of muscles from ultrasound image using morphological information of fascia and thoracic vertebra

In this paper, we propose a new fully computerized image analysis method for measuring the thickness of muscles from ultrasound image obtained by muscle endurance test using morphological information of fascia and thoracic vertebra. Firstly, we divide the image into lumbar region and thoracolumbar region by the difference of density in image for measuring the thickness of muscles. In lumbar region, we notice that the intensity of fascia is relatively higher than other parts. Thus, we measure the thickness of muscles surrounding the fascia area. In the process, we apply median filter to candidate fascia areas for extracting candidate muscle layers between fascias. Then, the thickness of muscles we measure is that of the third layer. In thoracolumbar region, we apply region expansion method for classifying the region into subcutaneous fat part and part including thoracic vertebra. Then, we apply counting method and evolutionary computation search model to find the measuring location that is in between subcutaneous fat area and thoracic vertebra. In experiment, the proposed method is effective in measuring the thickness of muscles and avoids failures of previous studies. The performance of this approach is sufficiently comparable to that of medical experts.     

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.     

Medical imaging technique using curvelet transform and machine learning for the automated diagnosis of breast cancer from thermal image

Pattern Analysis and Applications - Thermography is a useful imaging tool using infrared for the early diagnosis of breast cancer. Screening cancer aims to outstrip prognosis by seeing the...     

Enhancing clustering quality of geo-demographic analysis using context fuzzy clustering type-2 and particle swarm optimization

Geo-Demographic Analysis, which is one of the most interesting inter-disciplinary research topics between Geographic Information Systems and Data Mining, plays a very important role in policies decision, population migration and services distribution. Among some soft computing methods used for this problem, clustering is the most popular one because it has many advantages in comparison with the rests such as the fast processing time, the quality of results and the used memory space. Nonetheless, the state-of-the-art clustering algorithm namely FGWC has low clustering quality since it was constructed on the basis of traditional fuzzy sets. In this paper, we will present a novel interval type-2 fuzzy clustering algorithm deployed in an extension of the traditional fuzzy sets namely Interval Type-2 Fuzzy Sets to enhance the clustering quality of FGWC. Some additional techniques such as the interval context variable, Particle Swarm Optimization and the parallel computing are attached to speed up the algorithm. The experimental evaluation through various case studies shows that the proposed method obtains better clustering quality than some best-known ones.