Understanding Heterogeneous EO Datasets: A Framework for Semantic Representations

Earth observation (EO) has become a valuable source of comprehensive, reliable, and persistent information for a wide number of applications. However, dealing with the complexity of land cover is sometimes difficult, as the variety of EO sensors reflects in the multitude of details recorded in several types of image data. Their properties dictate the category and nature of the perceptible land structures. The data heterogeneity hampers proper understanding, preventing the definition of universal procedures for content exploitation. The main shortcomings are due to the different human and sensor perception on objects, as well as to the lack of coincidence between visual elements and similarities obtained by computation. In order to bridge these sensory and semantic gaps, the paper presents a compound framework for EO image information extraction. The proposed approach acts like a common ground between the user's understanding, who is visually shortsighted to the visible domain, and the machines numerical interpretation of a much wider information. A hierarchical data representation is considered. At first, basic elements are automatically computed. Then, users can enforce their judgement on the data processing results until semantic structures are revealed. This procedure completes a user-machine knowledge transfer. The interaction is formalized as a dialogue, where communication is determined by a set of parameters guiding the computational process at each level of representation. The purpose is to maintain the data-driven observable connected to the level of semantics and to human awareness. The proposed concept offers flexibility and interoperability to users, allowing them to generate those results that best fit their application scenario. The experiments performed on different satellite images demonstrate the ability to increase the performances in case of semantic annotation by adjusting a set of parameters to the particularities of the analyzed data

Context based detection of urban land use zones

This dissertation proposes an automated land-use zoning system based on the context of an urban scene. Automated zoning is an important step toward improving object extraction in an urban scene

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

Remote sensing data quality model: from data sources to lifecycle phases

ABSTRACTThe importance of data quality assessment has significantly increased with the boom of information technology and the growing demand for remote sensing (RS) data. The Remote Sensing Data Qu..

Urban scene description for a multi scale classication of high resolution imagery case of Cape Town urban Scene

Includes abstract.Includes bibliographical references.In this paper, a multi level contextual classification approach of the City of Cape Town, South Africa is presented. The methodology developed to identify the different objects using the multi level contextual technique comprised three important phases