Automatic Image Segmentation by Dynamic Region Merging

This paper addresses the automatic image segmentation problem in a region merging style. With an initially over-segmented image, in which the many regions (or super-pixels) with homogeneous color are detected, image segmentation is performed by iteratively merging the regions according to a statistical test. There are two essential issues in a region merging algorithm: order of merging and the stopping criterion. In the proposed algorithm, these two issues are solved by a novel predicate, which is defined by the sequential probability ratio test (SPRT) and the maximum likelihood criterion. Starting from an over-segmented image, neighboring regions are progressively merged if there is an evidence for merging according to this predicate. We show that the merging order follows the principle of dynamic programming. This formulates image segmentation as an inference problem, where the final segmentation is established based on the observed image. We also prove that the produced segmentation satisfies certain global properties. In addition, a faster algorithm is developed to accelerate the region merging process, which maintains a nearest neighbor graph in each iteration. Experiments on real natural images are conducted to demonstrate the performance of the proposed dynamic region merging algorithm.Comment: 28 pages. This paper is under review in IEEE TI

Active Learning: Any Value for Classification of Remotely Sensed Data?

Active learning, which has a strong impact on processing data prior to the classification phase, is an active research area within the machine learning community, and is now being extended for remote sensing applications. To be effective, classification must rely on the most informative pixels, while the training set should be as compact as possible. Active learning heuristics provide capability to select unlabeled data that are the “most informative” and to obtain the respective labels, contributing to both goals. Characteristics of remotely sensed image data provide both challenges and opportunities to exploit the potential advantages of active learning. We present an overview of active learning methods, then review the latest techniques proposed to cope with the problem of interactive sampling of training pixels for classification of remotely sensed data with support vector machines (SVMs). We discuss remote sensing specific approaches dealing with multisource and spatially and time-varying data, and provide examples for high-dimensional hyperspectral imagery

Mapping land degradation using remote sensing data and an unsupervised clustering algorithm in the eThekwini Metropolitan Area.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Land degradation is a major environmental problem facing South Africa and many other countries around the world. For proper management and adoption of best rehabilitation strategies, a compendious regional-scale assessment approach is needed to attain the full extent of the impairment. The aim of this study was to assess the spatial extent of land degradation with the use of GIS and remote sensing techniques in the eThekwini Metropolitan Area (EMA), KwaZulu-Natal, South Africa. The first objective was to review the status of land degradation in South Africa, as well as tracking of emerging trends in remote sensing and Geographic Information Systems research. Historically, in South Africa, land degradation has been associated with poverty and rurality. While conducting studies was also a challenge, demanding high human and economic resources. Although these studies were accurate and invaluable, most of them were too localized and highly difficult to replicate. The introduction of remote sensing has bought a new dimension with a timely spatial mapping of land degradation at regional scales. As a result, there thus been a sharp increase in remote sensing-based land degradation studies, this is also accompanied by the recent improvements in capabilities of remote sensors and associated GIS platforms. However, there is still a challenge of accessibility, especially for financial constricted regions such as the sub-Sahara of Africa. Most of the cutting-edge remote sensing data such as the hyperspectral and high spatial resolution imagery are highly expensive and therefore inaccessible to those not affording. However, the use of new-age medium resolution sensors is a potential solution. The second objection of this study was to detect and map the spatial distribution of land degradation in the EMA through use of Sentinel-2 derived vegetation indices (VIs) in conjunction with a hierarchical clustering algorithm. Data from Sentinel-2 was used to derive VIs used in this study, these are namely; NDVI, RVI, SAVI; and SARVI. The framework using Ward’s hierarchical clustering performed relatively good to produce 6 clusters that achieved an overall classification accuracy (OA) of 88.81% when mapping land-cover including land degradation. In this regard, land degradation achieved the highest classification accuracy of up to 100%, while water achieved the lowest at 63.33%. Although there was quite a significant difference in accuracies between different land-cover classes, overall, the results were still reasonably good with an error rate of 0.14 and Kappa Coefficient of 0.86. The results from this study, therefore, suggest that Ward’s unsupervised clustering approach is a suitable tool for mapping of complex land-cover classes, particularly land degradation

Probabilistic Image Models and their Massively Parallel Architectures : A Seamless Simulation- and VLSI Design-Framework Approach

Algorithmic robustness in real-world scenarios and real-time processing capabilities are the two essential and at the same time contradictory requirements modern image-processing systems have to fulfill to go significantly beyond state-of-the-art systems. Without suitable image processing and analysis systems at hand, which comply with the before mentioned contradictory requirements, solutions and devices for the application scenarios of the next generation will not become reality. This issue would eventually lead to a serious restraint of innovation for various branches of industry. This thesis presents a coherent approach to the above mentioned problem. The thesis at first describes a massively parallel architecture template and secondly a seamless simulation- and semiconductor-technology-independent design framework for a class of probabilistic image models, which are formulated on a regular Markovian processing grid. The architecture template is composed of different building blocks, which are rigorously derived from Markov Random Field theory with respect to the constraints of \it massively parallel processing \rm and \it technology independence\rm. This systematic derivation procedure leads to many benefits: it decouples the architecture characteristics from constraints of one specific semiconductor technology; it guarantees that the derived massively parallel architecture is in conformity with theory; and it finally guarantees that the derived architecture will be suitable for VLSI implementations. The simulation-framework addresses the unique hardware-relevant simulation needs of MRF based processing architectures. Furthermore the framework ensures a qualified representation for simulation of the image models and their massively parallel architectures by means of their specific simulation modules. This allows for systematic studies with respect to the combination of numerical, architectural, timing and massively parallel processing constraints to disclose novel insights into MRF models and their hardware architectures. The design-framework rests upon a graph theoretical approach, which offers unique capabilities to fulfill the VLSI demands of massively parallel MRF architectures: the semiconductor technology independence guarantees a technology uncommitted architecture for several design steps without restricting the design space too early; the design entry by means of behavioral descriptions allows for a functional representation without determining the architecture at the outset; and the topology-synthesis simplifies and separates the data- and control-path synthesis. Detailed results discussed in the particular chapters together with several additional results collected in the appendix will further substantiate the claims made in this thesis