Integrating spatial and spectral information for automatic feature identification in high -resolution remotely sensed images

This research used image objects, instead of pixels, as the basic unit of analysis in high-resolution imagery. Thus, not only spectral radiance and texture were used in the analysis, but also spatial context. Furthermore, the automated identification of attributed objects is potentially useful for integrating remote sensing with a vector-based GIS.;A study area in Morgantown, WV was chosen as a site for the development and testing of automated feature extraction methods with high-resolution data. In the first stage of the analysis, edges were identified using texture. Experiments with simulated data indicated that a linear operator identified curved and sharp edges more accurately than square shaped operators. Areas with edges that formed a closed boundary were used to delineate sub-patches. In the region growing step, the similarities of all adjacent subpatches were examined using a multivariate Hotelling T2 test that draws on the classes\u27 covariance matrices. Sub-patches that were not sufficiently dissimilar were merged to form image patches.;Patches were then classified into seven classes: Building, Road, Forest, Lawn, Shadowed Vegetation, Water, and Shadow. Six classification methods were compared: the pixel-based ISODATA and maximum likelihood approaches, field-based ECHO, and region based maximum likelihood using patch means, a divergence index, and patch probability density functions (pdfs). Classification with the divergence index showed the lowest accuracy, a kappa index of 0.254. The highest accuracy, 0.783, was obtained from classification using the patch pdf. This classification also produced a visually pleasing product, with well-delineated objects and without the distracting salt-and-pepper effect of isolated misclassified pixels. The accuracies of classification with patch mean, pixel based maximum likelihood, ISODATA and ECHO were 0.735, 0.687, 0.610, and 0.605, respectively.;Spatial context was used to generate aggregate land cover information. An Urbanized Rate Index, defined based on the percentage of Building and Road area within a local window, was used to segment the image. Five summary landcover classes were identified from the Urbanized Rate segmentation and the image object classification: High Urbanized Rate and large building sizes, Intermediate Urbanized Rate and intermediate building sizes, Low urbanized rate and small building sizes, Forest, and Water

Analisis orientado a objetos de imágenes de teledetección para cartografia forestal : bases conceptuales y un metodo de segmentacion para obtener una particion inicial para la clasificacion = Object-oriented analysis of remote sensing images for land cover mapping : Conceptual foundations and a segmentation method to derive a baseline partition for classification

El enfoque comúnmente usado para analizar las imágenes de satélite con fines cartográficos da lugar a resultados insatisfactorios debido principalmente a que únicamente utiliza los patrones espectrales de los píxeles, ignorando casi por completo la estructura espacial de la imagen. Además, la equiparación de las clases de cubierta a tipos de materiales homogéneos permite que cualquier parte arbitrariamente delimitada dentro de una tesela del mapa siga siendo un referente del concepto definido por su etiqueta. Esta posibilidad es incongruente con el modelo jerárquico del paisaje cada vez más aceptado en Ecología del Paisaje, que asume que la homogeneidad depende de la escala de observación y en cualquier caso es más semántica que biofísica, y que por tanto los paisajes son intrínsecamente heterogéneos y están compuestos de unidades (patches) que funcionan simultáneamente como un todo diferente de lo que les rodea y como partes de un todo mayor. Por tanto se hace necesario un nuevo enfoque (orientado a objetos) que sea compatible con este modelo y en el que las unidades básicas del análisis sean delimitadas de acuerdo a la variación espacial del fenómeno estudiado. Esta tesis pretende contribuir a este cambio de paradigma en teledetección, y sus objetivos concretos son: 1.- Poner de relieve las deficiencias del enfoque tradicionalmente empleado en la clasificación de imágenes de satélite. 2.- Sentar las bases conceptuales de un enfoque alternativo basado en zonas básicas clasificables como objetos. 3.- Desarrollar e implementar una versión demostrativa de un método automático que convierte una imagen multiespectral en una capa vectorial formada por esas zonas. La estrategia que se propone es producir, basándose en la estructura espacial de las imágenes, una partición de estas en la que cada región puede considerarse relativamente homogénea y diferente de sus vecinas y que además supera (aunque no por mucho) el tamaño de la unidad mínima cartografiable. Cada región se asume corresponde a un rodal que tras la clasificación será agregado junto a otros rodales vecinos en una región mayor que en conjunto pueda verse como una instancia de un cierto tipo de objetos que más tarde son representados en el mapa mediante teselas de una clase particular

Injecting spatial priors in Earth observation with machine vision

Remote Sensing (RS) imagery with submeter resolution is becoming ubiquitous. Be it from satellites, aerial campaigns or Unmanned Aerial Vehicles, this spatial resolution allows to recognize individual objects and their parts from above. This has driven, during the last few years, a big interest in the RS community on Computer Vision (CV) methods developed for the automated understanding of natural images. A central element to the success of \CV is the use of prior information about the image generation process and the objects these images contain: neighboring pixels are likely to belong to the same object; objects of the same nature tend to look similar with independence of their location in the image; certain objects tend to occur in particular geometric configurations; etc. When using RS imagery, additional prior knowledge exists on how the images were formed, since we know roughly the geographical location of the objects, the geospatial prior, and the direction they were observed from, the overhead-view prior. This thesis explores ways of encoding these priors in CV models to improve their performance on RS imagery, with a focus on land-cover and land-use mapping.</p

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Automatic road network extraction from high resolution satellite imagery using spectral classification methods

Road networks play an important role in a number of geospatial applications, such as cartographic, infrastructure planning and traffic routing software. Automatic and semi-automatic road network extraction techniques have significantly increased the extraction rate of road networks. Automated processes still yield some erroneous and incomplete results and costly human intervention is still required to evaluate results and correct errors. With the aim of improving the accuracy of road extraction systems, three objectives are defined in this thesis: Firstly, the study seeks to develop a flexible semi-automated road extraction system, capable of extracting roads from QuickBird satellite imagery. The second objective is to integrate a variety of algorithms within the road network extraction system. The benefits of using each of these algorithms within the proposed road extraction system, is illustrated. Finally, a fully automated system is proposed by incorporating a number of the algorithms investigated throughout the thesis. CopyrightDissertation (MSc)--University of Pretoria, 2010.Computer Scienceunrestricte

Consistent Density Scanning and Information Extraction From Point Clouds of Building Interiors

Over the last decade, 3D range scanning systems have improved considerably enabling the designers to capture large and complex domains such as building interiors. The captured point cloud is processed to extract specific Building Information Models, where the main research challenge is to simultaneously handle huge and cohesive point clouds representing multiple objects, occluded features and vast geometric diversity. These domain characteristics increase the data complexities and thus make it difficult to extract accurate information models from the captured point clouds. The research work presented in this thesis improves the information extraction pipeline with the development of novel algorithms for consistent density scanning and information extraction automation for building interiors. A restricted density-based, scan planning methodology computes the number of scans to cover large linear domains while ensuring desired data density and reducing rigorous post-processing of data sets. The research work further develops effective algorithms to transform the captured data into information models in terms of domain features (layouts), meaningful data clusters (segmented data) and specific shape attributes (occluded boundaries) having better practical utility. Initially, a direct point-based simplification and layout extraction algorithm is presented that can handle the cohesive point clouds by adaptive simplification and an accurate layout extraction approach without generating an intermediate model. Further, three information extraction algorithms are presented that transforms point clouds into meaningful clusters. The novelty of these algorithms lies in the fact that they work directly on point clouds by exploiting their inherent characteristic. First a rapid data clustering algorithm is presented to quickly identify objects in the scanned scene using a robust hue, saturation and value (H S V) color model for better scene understanding. A hierarchical clustering algorithm is developed to handle the vast geometric diversity ranging from planar walls to complex freeform objects. The shape adaptive parameters help to segment planar as well as complex interiors whereas combining color and geometry based segmentation criterion improves clustering reliability and identifies unique clusters from geometrically similar regions. Finally, a progressive scan line based, side-ratio constraint algorithm is presented to identify occluded boundary data points by investigating their spatial discontinuity

Artificial Intelligence Based Classification for Urban Surface Water Modelling

Estimations and predictions of surface water runoff can provide very useful insights, regarding flood risks in urban areas. To automatically predict the flow behaviour of the rainfall-runoff water, in real-world satellite images, it is important to precisely identify permeable and impermeable areas. This identification indicates and helps to calculate the amount of surface water, by taking into account the amount of water being absorbed in a permeable area and what remains on the impermeable area. In this research, a model of surface water has been established, to predict the behavioural flow of rainfall-runoff water. This study employs a combination of image processing, artificial intelligence and machine learning techniques, for automatic segmentation and classification of permeable and impermeable areas, in satellite images. These techniques investigate the image classification approaches for classifying three land-use categories (roofs, roads, and pervious areas), commonly found in satellite images of the earth’s surface. Three different classification scenarios are investigated, to select the best classification model. The first scenario involves pixel by pixel classification of images, using Classification Tree and Random Forest classification techniques, in 2 different settings of sequential and parallel execution of algorithms. In the second classification scenario, the image is divided into objects, by using Superpixels (SLIC) segmentation method, while three kinds of feature sets are extracted from the segmented objects. The performance of eight different supervised machine learning classifiers is probed, using 5-fold cross-validation, for multiple SLIC values, while detailed performance comparisons lead to conclusions about the classification into different classes, regarding Object-based and Pixel-based classification schemes. Pareto analysis and Knee point selection are used to select SLIC value and the suitable type of classification, among the aforementioned two. Furthermore, a new diversity and weighted sum-based ensemble classification model, called ParetoEnsemble, is proposed, in this classification scenario. The weights are applied to selected component classifiers of an ensemble, creating a strong classifier, where classification is done based on multiple votes from candidate classifiers of the ensemble, as opposed to individual classifiers, where classification is done based on a single vote, from only one classifier. Unbalanced and balanced data-based classification results are also evaluated, to determine the most suitable mode, for satellite image classifications, in this study. Convolutional Neural Networks, based on semantic segmentation, are also employed in the classification phase, as a third scenario, to evaluate the strength of deep learning model SegNet, in the classification of satellite imaging. The best results, from the three classification scenarios, are compared and the best classification method, among the three scenarios, is used in the next phase of water modelling, with the InfoWorks ICM software, to explore the potential of modelling process, regarding a partially automated surface water network. By using the parameter settings, with a specified amount of simulated rain falling, onto the imaged area, the amount of surface water flow is estimated, to get predictions about runoff situations in urban areas, since runoff, in such a situation, can be high enough to pose a dangerous flood risk. The area of Feock, in Cornwall, is used as a simulation area of study, in this research, where some promising results have been derived, regarding classification and modelling of runoff. The correlation coefficient estimation, between classification and runoff accuracy, provides useful insight, regarding the dependence of runoff performance on classification performance. The trained system was tested on some unknown area images as well, demonstrating a reasonable performance, considering the training and classification limitations and conditions. Furthermore, in these unknown area images, reasonable estimations were derived, regarding surface water runoff. An analysis of unbalanced and balanced data-based classification and runoff estimations, for multiple parameter configurations, provides aid to the selection of classification and modelling parameter values, to be used in future unknown data predictions. This research is founded on the incorporation of satellite imaging into water modelling, using selective images for analysis and assessment of results. This system can be further improved, and runoff predictions of high precision can be better achieved, by adding more high-resolution images to the classifiers training. The added variety, to the trained model, can lead to an even better classification of any unknown image, which could eventually provide better modelling and better insights into surface water modelling. Moreover, the modelling phase can be extended, in future research, to deal with real-time parameters, by calibrating the model, after the classification phase, in order to observe the impact of classification on the actual calibration