An Approach for the Customized High-Dimensional Segmentation of Remote Sensing Hyperspectral Images

Abstract: This paper addresses three problems in the field of hyperspectral image segmentation: the fact that the way an image must be segmented is related to what the user requires and the application; the lack and cost of appropriately labeled reference images; and, finally, the information loss problem that arises in many algorithms when high dimensional images are projected onto lower dimensional spaces before starting the segmentation process. To address these issues, the Multi-Gradient based Cellular Automaton (MGCA) structure is proposed to segment multidimensional images without projecting them to lower dimensional spaces. The MGCA structure is coupled with an evolutionary algorithm (ECAS-II) in order to produce the transition rule sets required by MGCA segmenters. These sets are customized to specific segmentation needs as a function of a set of low dimensional training images in which the user expresses his segmentation requirements. Constructing high dimensional image segmenters from low dimensional training sets alleviates the problem of lack of labeled training images. These can be generated online based on a parametrization of the desired segmentation extracted from a set of examples. The strategy has been tested in experiments carried out using synthetic and real hyperspectral images, and it has been compared to state-of-the-art segmentation approaches over benchmark images in the area of remote sensing hyperspectral imaging.Ministerio de Economía y competitividad; TIN2015-63646-C5-1-RMinisterio de Economía y competitividad; RTI2018-101114-B-I00Xunta de Galicia: ED431C 2017/1

Contributions to the analysis and segmentation of remote sensing hyperspectral images

142 p.This PhD Thesis deals with the segmentation of hyperspectral images from the point of view of Lattice Computing. We have introduced the application of Associative Morphological Memories as a tool to detect strong lattice independence, which has been proven equivalent to affine independence. Therefore, sets of strong lattice independent vectors found using our algorithms correspond to the vertices of convex sets that cover most of the data. Unmixing the data relative to these endmembers provides a collection of abundance images which can be assumed either as unsupervised segmentations of the images or as features extracted from the hyperspectral image pixels. Besides, we have applied this feature extraction to propose a content based image retrieval approach based on the image spectral characterization provided by the endmembers. Finally, we extended our ideas to the proposal of Morphological Cellular Automata whose dynamics are guided by the morphological/lattice independence properties of the image pixels. Our works have also explored the applicability of Evolution Strategies to the endmember induction from the hyperspectral image data

Spectral-spatial classification of n-dimensional images in real-time based on segmentation and mathematical morphology on GPUs

The objective of this thesis is to develop efficient schemes for spectral-spatial n-dimensional image classification. By efficient schemes, we mean schemes that produce good classification results in terms of accuracy, as well as schemes that can be executed in real-time on low-cost computing infrastructures, such as the Graphics Processing Units (GPUs) shipped in personal computers. The n-dimensional images include images with two and three dimensions, such as images coming from the medical domain, and also images ranging from ten to hundreds of dimensions, such as the multiand hyperspectral images acquired in remote sensing. In image analysis, classification is a regularly used method for information retrieval in areas such as medical diagnosis, surveillance, manufacturing and remote sensing, among others. In addition, as the hyperspectral images have been widely available in recent years owing to the reduction in the size and cost of the sensors, the number of applications at lab scale, such as food quality control, art forgery detection, disease diagnosis and forensics has also increased. Although there are many spectral-spatial classification schemes, most are computationally inefficient in terms of execution time. In addition, the need for efficient computation on low-cost computing infrastructures is increasing in line with the incorporation of technology into everyday applications. In this thesis we have proposed two spectral-spatial classification schemes: one based on segmentation and other based on wavelets and mathematical morphology. These schemes were designed with the aim of producing good classification results and they perform better than other schemes found in the literature based on segmentation and mathematical morphology in terms of accuracy. Additionally, it was necessary to develop techniques and strategies for efficient GPU computing, for example, a block–asynchronous strategy, resulting in an efficient implementation on GPU of the aforementioned spectral-spatial classification schemes. The optimal GPU parameters were analyzed and different data partitioning and thread block arrangements were studied to exploit the GPU resources. The results show that the GPU is an adequate computing platform for on-board processing of hyperspectral information

A comprehensive approach for the efficient acquisition and processing of hyperspectral images and sequence

Programa Oficial de Doctorado en Computación. 5009P01[Abstract] Despite the scientific and technological developments achieved during the last two decades in the hyperspectral field, some methodological, operational and conceptual issues have restricted the progress, promotion and popular dissemination of this technology. These shortcomings include the specialized knowledge required for the acquisition of hyperspectral images, the shortage of publicly accessible hyperspectral image repositories with reliable ground truth images or the lack of methodologies that allow for the adaptation of algorithms to particular user or application processing needs. The work presented here has the objective of contributing to the hyperspectral field with procedures for the automatic acquisition of hyperspectral scenes, including the hardware adaptation of our own imagers and the development of methods for the calibration and correction of the hyperspectral datacubes, the creation of a publicly available hyperspectral repository of well categorized and labeled images and the design and implementation of novel computational intelligence based processing techniques that solve typical issues related to the segmentation and denoising of hyperspectral images as well as sequences of them taking into account their temporal evolution.[Resumen] A pesar de los desarrollos tecnológicos y científicos logrados en el campo hiperespectral durante las dos últimas décadas, alg\mas limitaciones de tipo metodológico, operacional y conceptual han restringido el progreso, difusión y popularización de esta tecnología, entre ellas, el conocimiento especializado requerido en la adquisición de imágenes hiperespectrales, la carencia de repositorios de imágenes hiperespectrales con etiquetados fiables y de acceso público o la falta de metodologías que posibiliten la adaptación de algoritmos a usuarios o necesidades de procesamiento concretas. Este trabajo doctoral tiene el objetivo de contribuir al campo hiperespectral con procedimientos para la adquisición automática de escenas hiperespectrales, incluyendo la adaptación hardware de cámaras hiperespectrales propias y el desarrollo de métodos para la calibración y corrección de cubos de datos hiperespectrales; la creación de un repositorio hiperespectral de acceso público con imágenes categorizadas y con verdades de terreno fiables; y el diseño e implementación de técnicas de procesamiento basadas en inteligencia computacional para la resolución de problemas típicamente relacionados con las tareas de segmentación y eliminación de ruido en imágenes estáticas y secuencias de imágenes hiperespectrales teniendo en consideración su evolución temporal.[Resumo] A pesar dos desenvolvementos tecnolóxicos e científicos logrados no campo hiperespectral durante as dúas últimas décadas, algunhas lirrútacións de tipo metodolóxico¡ operacional e conceptual restrinxiron o progreso) difusión e popularización desta tecnoloxía, entre elas, o coñecemento especializado requirido na adquisición de imaxes hiperespectrales¡ a carencia de repositorios de irnaxes hiperespectrales con etiquetaxes fiables e de acceso público ou a falta de metodoloxías que posibiliten a adaptación de algoritmos a usuarios ou necesidades de procesamento concretas. Este traballo doutoral ten o obxectívo de contribuir ao campo hiperespectral con procedementos para a adquisición automática de eicenas hiperespectrais, incluíndo a adaptación hardware de cámaras hiperespectrales propias e o desenvolvemento de métodos para a calibración e corrección de cubos de datos hiperespectrais; a creación dun repositorio hiperespectral de acceso público con imaxes categorizadas e con verdades de terreo fiables; e o deseño e implementación de técnicas de procesamento baseadas en intelixencia computacional para a resolución de problemas tipicamente relacionado~ coas tarefas de segmentación e eliminación de ruído en imaxes estáticas e secuencias de imaxes hiperespectrai~ tendo en consideración a súa evolución temporal

Two and three dimensional segmentation of multimodal imagery

The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes

Urban land use change analysis and modelling: a case study of Setubal-Sesimbra, Portugal

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesIn this paper urban land use change analysis and modeling of the Concelhos of Setúbal and Sesimbra, Portugal is accomplished using multitemporal and multispectral satellite images acquired in the years 2000 and 2006 and other vector datasets. The LULC maps are first obtained using an object-oriented image classification approach with the Nearest Neighbour algorithm in Definiens. Classification is assessed using the overall accuracy and Kappa measure of agreement. These measures of accuracies are above minimum standard accepted levels. The land use dynamics, both for pattern and quantities are also studied using a post classification change detection technique together with the following selected spatial/landscape metrics: class area, number of patches, edge density, largest patch index, Euclidian mean nearest neighbor distance, area weighted mean patch fractal dimension and contagion. Urban sprawl has also been measured using Shannon Entropy approach to describe the dispersion of land development or sprawl. Results indicated that the study area has undergone a tremendous change in urban growth and pattern during the study period. A Cellular Automata Markov (CA_Markov) modeling approach has also been applied to predict urban land use change between 1990 and 2010 with two scenarios: MMU 1ha and MMU 25ha. The suitability maps (change drivers) are calibrated with the LULC maps of 1990 and 2000 using MCE and a contiguity filter. The maps of 1990 and 2000 are also used for the transition probability matrix. Then, the land use maps of 2006 are simulated to compare the result of the “prediction” with the actual land use map in that year so that further prediction can be carried out for the year 2010. This is evaluated based on the Kappa measure of agreement (Kno, Klocation and Kquanity) and produced a satisfactory level of accuracy. After calibrating the model and assessing its validity, a “real” prediction for the year 2010 is carried out. Analysis of the prediction revealed that the rate of urban growth tends to continue and would threaten large areas that are currently reserved for forest cover, farming lands and natural parks. Finally, the modeling output provides a building block for successive urban planning, for exploring how an

A novel unsupervised Levy flight particle swarm optimization (ULPSO) method for multispectral remote-sensing image classification

The rapid development of earth observation technology has produced large quantities of remote-sensing data. Unsupervised classification (i.e. clustering) of remote-sensing images, an important means to acquire land-use/cover information, has become increasingly in demand due to its simplicity and ease of application. Traditional methods, such as k-means, struggle to solve this NP-hard (Non-deterministic Polynomial hard) image classification problem. Particle swarm optimization (PSO), always achieving better result than k-means, has recently been applied to unsupervised image classification. However, PSO was also found to be easily trapped on local optima. This article proposes a novel unsupervised Levy flight particle swarm optimization (ULPSO) method for image classification with balanced exploitation and exploration capabilities. It benefits from a new searching strategy: the worst particle in the swarm is targeted and its position is updated with Levy flight at each iteration. The effectiveness of the proposed method was tested with three types of remote-sensing imagery (Landsat Thematic Mapper (TM), Flightline C1 (FLC), and QuickBird) that are distinct in terms of spatial and spectral resolution and landscape. Our results showed that ULPSO is able to achieve significantly better and more stable classification results than k-means and the other two intelligent methods based on genetic algorithm (GA) and particle swarm optimization (PSO) over all of the experiments. ULPSO is, therefore, recommended as an effective alternative for unsupervised remote-sensing image classification