Hierarchical segmentation, object detection and classification in remotely sensed images

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent Univiversity, 2007.Thesis (Master's) -- Bilkent University, 2007.Includes bibliographical references leaves 68-76Automatic content extraction and classification of remotely sensed images have become highly desired goals by the advances in satellite technology and computing power. The usual choice for the level of processing image data has been pixelbased analysis. However, spatial information is an important element to interpret the land cover because pixels alone do not give much information about image content. Automatic segmentation of high-resolution remote sensing imagery is an important problem in remote sensing applications because the resulting segmentations can provide valuable spatial and structural information that are complementary to pixel-based spectral information in classification. In this thesis, we first present a method that combines structural information extracted by morphological processing with spectral information summarized using principal components analysis to produce precise segmentations that are also robust to noise. First, principal components are computed from hyper-spectral data to obtain representative bands. Then, candidate regions are extracted by applying connected components analysis to the pixels selected according to their morphological profiles computed using opening and closing by reconstruction with increasing structuring element sizes. Next, these regions are represented using a tree, and the most meaningful ones are selected by optimizing a measure that consists of two factors: spectral homogeneity, which is calculated in terms of variances of spectral features, and neighborhood connectivity, which is calculated using sizes of connected components. Experiments on three data sets show that the method is able to detect structures in the image which are more precise and more meaningful than the structures detected by another approach that does not make strong use of neighborhood and spectral information.Then, we introduce an unsupervised method that combines both spectral and structural information for automatic object detection. First, a segmentation hierarchy is constructed and candidate segments for object detection are selected by the proposed segmentation method. Given the observation that different structures appear more clearly in different principal components, we present an algorithm that is based on probabilistic Latent Semantic Analysis (PLSA) for grouping the candidate segments belonging to multiple segmentations and multiple principal components. The segments are modeled using their spectral content and the PLSA algorithm builds object models by learning the objectconditional probability distributions. Labeling of a segment is done by computing the similarity of its spectral distribution to the distribution of object models using Kullback-Leibler divergence. Experiments on three data sets show that our method is able to automatically detect, group, and label segments belonging to the same object classes. Finally, we present an approach for classification of remotely sensed imagery using spatial information extracted from multi-scale segmentations. Different structuring element size ranges are used to obtain multiple representations of an image at different scales to capture different details inherently found in different structures. Then, pixels at each scale are grouped into contiguous regions using the proposed segmentation method. The resulting regions are modeled using the statistical summaries of their spectral properties. These models are used to cluster the regions by the proposed grouping method, and the cluster memberships assigned to each region at multiple scales are used to classify the corresponding pixels into land cover/land use categories. Final classification is done using decision tree classifiers. Experiments with three ground truth data sets show the effectiveness of the proposed approach over traditional techniques that do not make strong use of region-based spatial information.Akçay, Hüseyin GökhanM.S

Techniques for automatic large scale change analysis of temporal multispectral imagery

Change detection in remotely sensed imagery is a multi-faceted problem with a wide variety of desired solutions. Automatic change detection and analysis to assist in the coverage of large areas at high resolution is a popular area of research in the remote sensing community. Beyond basic change detection, the analysis of change is essential to provide results that positively impact an image analyst\u27s job when examining potentially changed areas. Present change detection algorithms are geared toward low resolution imagery, and require analyst input to provide anything more than a simple pixel level map of the magnitude of change that has occurred. One major problem with this approach is that change occurs in such large volume at small spatial scales that a simple change map is no longer useful. This research strives to create an algorithm based on a set of metrics that performs a large area search for change in high resolution multispectral image sequences and utilizes a variety of methods to identify different types of change. Rather than simply mapping the magnitude of any change in the scene, the goal of this research is to create a useful display of the different types of change in the image. The techniques presented in this dissertation are used to interpret large area images and provide useful information to an analyst about small regions that have undergone specific types of change while retaining image context to make further manual interpretation easier. This analyst cueing to reduce information overload in a large area search environment will have an impact in the areas of disaster recovery, search and rescue situations, and land use surveys among others. By utilizing a feature based approach founded on applying existing statistical methods and new and existing topological methods to high resolution temporal multispectral imagery, a novel change detection methodology is produced that can automatically provide useful information about the change occurring in large area and high resolution image sequences. The change detection and analysis algorithm developed could be adapted to many potential image change scenarios to perform automatic large scale analysis of change

A methodology to produce geographical information for land planning using very-high resolution images

Actualmente, os municípios são obrigados a produzir, no âmbito da elaboração dos instrumentos de gestão territorial, cartografia homologada pela autoridade nacional. O Plano Director Municipal (PDM) tem um período de vigência de 10 anos. Porém, no que diz respeito à cartografia para estes planos, principalmente em municípios onde a pressão urbanística é elevada, esta periodicidade não é compatível com a dinâmica de alteração de uso do solo. Emerge assim, a necessidade de um processo de produção mais eficaz, que permita a obtenção de uma nova cartografia de base e temática mais frequentemente. Em Portugal recorre-se à fotografia aérea como informação de base para a produção de cartografia de grande escala. Por um lado, embora este suporte de informação resulte em mapas bastante rigorosos e detalhados, a sua produção têm custos muito elevados e consomem muito tempo. As imagens de satélite de muito alta-resolução espacial podem constituir uma alternativa, mas sem substituir as fotografias aéreas na produção de cartografia temática, a grande escala. O tema da tese trata assim da satisfação das necessidades municipais em informação geográfica actualizada. Para melhor conhecer o valor e utilidade desta informação, realizou-se um inquérito aos municípios Portugueses. Este passo foi essencial para avaliar a pertinência e a utilidade da introdução de imagens de satélite de muito alta-resolução espacial na cadeia de procedimentos de actualização de alguns temas, quer na cartografia de base quer na cartografia temática. A abordagem proposta para solução do problema identificado baseia-se no uso de imagens de satélite e outros dados digitais em ambiente de Sistemas de Informação Geográfica. A experimentação teve como objectivo a extracção automática de elementos de interesse municipal a partir de imagens de muito alta-resolução espacial (fotografias aéreas ortorectificadas, imagem QuickBird, e imagem IKONOS), bem como de dados altimétricos (dados LiDAR). Avaliaram-se as potencialidades da informação geográfica extraídas das imagens para fins cartográficos e analíticos. Desenvolveram-se quatro casos de estudo que reflectem diferentes usos para os dados geográficos a nível municipal, e que traduzem aplicações com exigências diferentes. No primeiro caso de estudo, propõe-se uma metodologia para actualização periódica de cartografia a grande escala, que faz uso de fotografias aéreas vi ortorectificadas na área da Alta de Lisboa. Esta é uma aplicação quantitativa onde as qualidades posicionais e geométricas dos elementos extraídos são mais exigentes. No segundo caso de estudo, criou-se um sistema de alarme para áreas potencialmente alteradas, com recurso a uma imagem QuickBird e dados LiDAR, no Bairro da Madre de Deus, com objectivo de auxiliar a actualização de cartografia de grande escala. No terceiro caso de estudo avaliou-se o potencial solar de topos de edifícios nas Avenidas Novas, com recurso a dados LiDAR. No quarto caso de estudo, propõe-se uma série de indicadores municipais de monitorização territorial, obtidos pelo processamento de uma imagem IKONOS que cobre toda a área do concelho de Lisboa. Esta é uma aplicação com fins analíticos onde a qualidade temática da extracção é mais relevante.Currently, the Portuguese municipalities are required to produce homologated cartography, under the Territorial Management Instruments framework. The Municipal Master Plan (PDM) has to be revised every 10 years, as well as the topographic and thematic maps that describe the municipal territory. However, this period is inadequate for representing counties where urban pressure is high, and where the changes in the land use are very dynamic. Consequently, emerges the need for a more efficient mapping process, allowing obtaining recent geographic information more often. Several countries, including Portugal, continue to use aerial photography for large-scale mapping. Although this data enables highly accurate maps, its acquisition and visual interpretation are very costly and time consuming. Very-High Resolution (VHR) satellite imagery can be an alternative data source, without replacing the aerial images, for producing large-scale thematic cartography. The focus of the thesis is the demand for updated geographic information in the land planning process. To better understand the value and usefulness of this information, a survey of all Portuguese municipalities was carried out. This step was essential for assessing the relevance and usefulness of the introduction of VHR satellite imagery in the chain of procedures for updating land information. The proposed methodology is based on the use of VHR satellite imagery, and other digital data, in a Geographic Information Systems (GIS) environment. Different algorithms for feature extraction that take into account the variation in texture, color and shape of objects in the image, were tested. The trials aimed for automatic extraction of features of municipal interest, based on aerial and satellite high-resolution (orthophotos, QuickBird and IKONOS imagery) as well as elevation data (altimetric information and LiDAR data). To evaluate the potential of geographic information extracted from VHR images, two areas of application were identified: mapping and analytical purposes. Four case studies that reflect different uses of geographic data at the municipal level, with different accuracy requirements, were considered. The first case study presents a methodology for periodic updating of large-scale maps based on orthophotos, in the area of Alta de Lisboa. This is a situation where the positional and geometric accuracy of the extracted information are more demanding, since technical mapping standards must be complied. In the second case study, an alarm system that indicates the location of potential changes in building areas, using a QuickBird image and LiDAR data, was developed for the area of Bairro da Madre de Deus. The goal of the system is to assist the updating of large scale mapping, providing a layer that can be used by the municipal technicians as the basis for manual editing. In the third case study, the analysis of the most suitable roof-tops for installing solar systems, using LiDAR data, was performed in the area of Avenidas Novas. A set of urban environment indicators obtained from VHR imagery is presented. The concept is demonstrated for the entire city of Lisbon, through IKONOS imagery processing. In this analytical application, the positional quality issue of extraction is less relevant.GEOSAT – Methodologies to extract large scale GEOgraphical information from very high resolution SATellite images (PTDC/GEO/64826/2006), e-GEO – Centro de Estudos de Geografia e Planeamento Regional, da Faculdade de Ciências Sociais e Humanas, no quadro do Grupo de Investigação Modelação Geográfica, Cidades e Ordenamento do Territóri

Physics-Based Detection of Subpixel Targets in Hyperspectral Imagery

Hyperspectral imagery provides the ability to detect targets that are smaller than the size of a pixel. They provide this ability by measuring the reflection and absorption of light at different wavelengths creating a spectral signature for each pixel in the image. This spectral signature contains information about the different materials within the pixel; therefore, the challenge in subpixel target detection lies in separating the target's spectral signature from competing background signatures. Most research has approached this problem in a purely statistical manner. Our approach fuses statistical signal processing techniques with the physics of reflectance spectroscopy and radiative transfer theory. Using this approach, we provide novel algorithms for all aspects of subpixel detection from parameter estimation to threshold determination. Characterization of the target and background spectral signatures is a key part of subpixel detection. We develop an algorithm to generate target signatures based on radiative transfer theory using only the image and a reference signature without the need for calibration, weather information, or source-target-receiver geometries. For background signatures, our work identifies that even slight estimation errors in the number of background signatures can severely degrade detection performance. To this end, we present a new method to estimate the number of background signatures specifically for subpixel target detection. At the core of the dissertation is the development of two hybrid detectors which fuse spectroscopy with statistical hypothesis testing. Our results show that the hybrid detectors provide improved performance in three different ways: insensitivity to the number of background signatures, improved detection performance, and consistent performance across multiple images leading to improved receiver operating characteristic curves. Lastly, we present a novel adaptive threshold estimate via extreme value theory. The method can be used on any detector type - not just those that are constant false alarm rate (CFAR) detectors. Even on CFAR detectors our proposed method can estimate thresholds that are better than theoretical predictions due to the inherent mismatch between the CFAR model assumptions and real data. Additionally, our method works in the presence of target detections while still estimating an accurate threshold for a desired false alarm rate

Advanced Pre-Processing and Change-Detection Techniques for the Analysis of Multitemporal VHR Remote Sensing Images

Remote sensing images regularly acquired by satellite over the same geographical areas (multitemporal images) provide very important information on the land cover dynamic. In the last years the ever increasing availability of multitemporal very high geometrical resolution (VHR) remote sensing images (which have sub-metric resolution) resulted in new potentially relevant applications related to environmental monitoring and land cover control and management. The most of these applications are associated with the analysis of dynamic phenomena (both anthropic and non anthropic) that occur at different scales and result in changes on the Earth surface. In this context, in order to adequately exploit the huge amount of data acquired by remote sensing satellites, it is mandatory to develop unsupervised and automatic techniques for an efficient and effective analysis of such kind of multitemporal data. In the literature several techniques have been developed for the automatic analysis of multitemporal medium/high resolution data. However these techniques do not result effective when dealing with VHR images. The main reasons consist in their inability both to exploit the high geometrical detail content of VHR data and to model the multiscale nature of the scene (and therefore of possible changes). In this framework it is important to develop unsupervised change-detection(CD) methods able to automatically manage the large amount of information of VHR data, without the need of any prior information on the area under investigation. Even if these methods usually identify only the presence/absence of changes without giving information about the kind of change occurred, they are considered the most interesting from an operational perspective, as in the most of the applications no multitemporal ground truth information is available. Considering the above mentioned limitations, in this thesis we study the main problems related to multitemporal VHR images with particular attention to registration noise (i.e. the noise related to a non-perfect alignment of the multitemporal images under investigation). Then, on the basis of the results of the conducted analysis, we develop robust unsupervised and automatic change-detection methods. In particular, the following specific issues are addressed in this work: 1. Analysis of the effects of registration noise in multitemporal VHR images and definition of a method for the estimation of the distribution of such kind of noise useful for defining: a. Change-detection techniques robust to registration noise (RN); the proposed techniques are able to significantly reduce the false alarm rate due to RN that is raised by the standard CD techniques when dealing with VHR images. b. Effective registration methods; the proposed strategies are based on a multiscale analysis of the scene which allows one to extract accurate control points for the registration of VHR images. 2. Detection and discrimination of multiple changes in multitemporal images; this techniques allow one to overcome the limitation of the existing unsupervised techniques, as they are able to identify and separate different kinds of change without any prior information on the study areas. 3. Pre-processing techniques for optimizing change detection on VHR images; in particular, in this context we evaluate the impact of: a. Image transformation techniques on the results of the CD process; b. Different strategies of image pansharpening applied to the original multitemporal images on the results of the CD process. For each of the above mentioned topic an analysis of the state of the art is carried out, the limitations of existing methods are pointed out and the proposed solutions to the addressed problems are described in details. Finally, experimental results conducted on both simulated and real data are reported in order to show and confirm the validity of all the proposed methods