Costas Tsatsoulis

Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices

This paper presents a preliminary study for mapping sea ice patterns (texture) with 100-m ERS-1 synthetic aperture radar (SAR) imagery. The authors used gray-level co-occurrence matrices (GLCM) to quantitatively evaluate textural parameters and representations and to determine which parameter values and representations are best for mapping sea ice texture. They conducted experiments on the quantization levels of the image and the displacement and orientation values of the GLCM by examining the effects textural descriptors such as entropy have in the representation of different sea ice textures. They showed that a complete gray-level representation of the image is not necessary for texture mapping, an eight-level quantization representation is undesirable for textural representation, and the displacement factor in texture measurements is more important than orientation. In addition, they developed three GLCM implementations and evaluated them by a supervised Bayesian classifier on sea ice textural contexts. This experiment concludes that the best GLCM implementation in representing sea ice texture is one that utilizes a range of displacement values such that both microtextures and macrotextures of sea ice can be adequately captured. These findings define the quantization, displacement, and orientation values that are the best for SAR sea ice texture analysis using GLCM.

A comprehensive, automated approach to determining sea ice thickness from SAR data

Documents an approach to sea ice classification through a combination of methods, both algorithmic and heuristic. The resulting system is a comprehensive technique, which uses dynamic local thresholding as a classification basis and then supplements that initial classification using heuristic geophysical knowledge organized in expert systems. The dynamic local thresholding method allows separation of the ice into thickness classes based on local intensity distributions. Because it utilizes the data within each image, it can adapt to varying ice thickness intensities to regional and seasonal changes and is not subject to limitations caused by using predefined parameters. >

ARKTOS: an intelligent system for SAR sea ice image classification

We present an intelligent system for satellite sea ice image analysis named Advanced Reasoning using Knowledge for Typing Of Sea ice (ARKTOS). ARKTOS performs fully automated analysis of synthetic aperture radar (SAR) sea ice images by mimicking the reasoning process of sea ice experts. ARKTOS automatically segments a SAR image of sea ice, generates descriptors for the segments of the image, and then uses expert system rules to classify these sea ice features. ARKTOS also utilizes multisource data fusion to improve classification and performs belief handling using Dempster-Shafer. As a software package, ARKTOS comprises components in image processing, rule-based classification, multisource data fusion, and graphical user interface-based knowledge engineering and modification. As a research project over the past ten years, ARKTOS has undergone phases such as knowledge acquisition, prototyping, refinement, evaluation, deployment, and operationalization at the U.S. National Ice Center. In this paper, we focus on the methodology, evaluations, and classification results of ARKTOS.

A Real-Time Negotiation Model and A Multi-Agent Sensor Network Implementation

This paper describes a negotiation model that incorporates real-time issues for autonomous agents. This model consists of two important ideas: a real-time logical negotiation protocol and a case-based negotiation model. The protocol integrates a real-time Belief-Desire-Intention (BDI) model, a temporal logic model, and communicative acts for negotiation. This protocol explicitly defines the logical and temporal relationships of different knowledge states, facilitating real-time designs such as multi-threaded processing, state profiling and updating, and a set of real-time enabling functional predicates in our implementation. To further support the protocol, we use a case-based reasoning model for negotiation strategy selection. An agent learns from its past experience by deriving a negotiation strategy from the most similar and useful case to its current situation. Guided by the strategy, the agent negotiates with its partners using an argumentation-based negotiation protocol. The model is time and situation aware such that each agent changes its negotiation behavior according to the progress and status of the ongoing negotiation and its current agent profile. We apply the negotiation model to a resource allocation problem and obtain promising results.

Segmentation of satellite imagery of natural scenes using data mining

The authors describe a segmentation technique that integrates traditional image processing algorithms with techniques adapted from knowledge discovery in databases (KDD) and data mining to analyze and segment unstructured satellite images of natural scenes. They have divided their segmentation task into three major steps. First, an initial segmentation is achieved using dynamic local thresholding, producing a set of regions. Then, spectral, spatial, and textural features for each region are generated from the thresholded image. Finally, given these features as attributes, an unsupervised machine learning methodology called conceptual clustering is used to cluster the regions found in the image into N classes-thus, determining the number of classes in the image automatically. They have applied the technique successfully to ERS-1 synthetic aperture radar (SAR). Landsat thematic mapper (TM), and NOAA advanced very high resolution radiometer (AVHRR) data of natural scenes.

Integrating case-based reasoning and decision theory

Case-based reasoning (CBR) and decision-theoretic techniques can be complementary. Decision theory helps CBR deal with uncertainties in the problem domain, while CBR helps decision theory handle complicated problems with many variables. The goal of integrating CBR and decision theory is to improve the ability of CBR systems to solve problems in domains of incomplete information. Our methodology views the retrieval of old cases in CBR as a decision problem, where each case from the case base provides an alternative solution and a prediction of the possible outcomes for the problem. When case-based problem solving encounters uncertainty, our methodology applies decision theory to evaluate each case in terms of the attributes that are significant for the problem, so that the most desirable case can be selected. We implemented our methodology in a case-based design assistant that helps chemists design pharmaceuticals.

Case-based reasoning and learning in manufacturing with the TOLTEC planner

The research presented concentrates on bringing together case-based reasoning and expert knowledge-based reasoning in a planner called TOLTEC. The experts domain-specific knowledge is modelled as dynamic memory structures and this representation is used to help the planner reason and control its planning process. TOLTEC uses a complex indexing of its cases, so as to allow incremental retrieval. The TOLTEC planner is applied to a highly constrained domain, and it is shown how the final plan is created by adding memory- and situation-selected chunks of subtask expansion to each subtask, until the problem is reduced to primitive (nonexpandable) tasks. It is also shown how the use of dynamic memory structures and dynamic, user-directed backtracking allows the planner to predict and discover failures, recover from them, and modify its knowledge according to them. Finally, it is shown how in a domain of multiple possible solutions for each goal the methodology developed allows the planner to slowly model itself to the preferences of the user. The paper also discusses some of the application domains where TOLTEC has been used, including process planning of cylindrical and prismatic parts, design checking, material selection, and design of communications systems. >

Analysis of SAR Data of the Polar Oceans

Recent advances in the analysis of SAR data of the polar oceans identifying ice floes and computing ice floe distributions in SAR images the role of synthetic aperture radar (SAR) in surface energy flux measurements over sea ice extraction of intermediate scale sea ice deformation parameters from SAR ice motion products fusion of satellite SAR with passive microwave data for sea ice remote sensing wavelet analysis of SAR images in the marginal ice zone mapping the progression of melt onset and freeze-up on Arctic Sea ice using synthetic aperture radar and scatterometry satellite microwave radar observations of Antarctic Sea ice Alaska SAR facility - the US Science Center for Sea Ice SAR data Polar SAR data for operational sea ice mapping the RADARSAT geophysical processor system towards operational monitoring of Arctic sea ice by synthetic aperture radar.

Satisficing coalition formation among agents

In a multiagent system where each agent has only an incomplete view of the world, optimal coalition formation is difficult. Coupling that with real-time and resource constraints often makes the rationalization process infeasible or costly. We propose a coalition formation approach that identifies and builds sub-optimal yet satisficing coalitions among agents to solve a problem detected in the environment. All agents are peers and autonomous. Each is motivated to conserve its own resources while cooperating with other agents to achieve a global task or resource allocation goal. The (initiating) agent-that detects a problem-hastily forms an initial coalition by selecting neighboring agents that it considers to have high potential utilities, based on the capability of each neighbor and its respective inter-agent relationships. The initiating agent next finalizes the coalition via multiple concurrent 1-to-1 negotiations with only neighbors of high potential utility, during which constraints and commitments are exchanged in an argumentation setting.

PANDA: a case-based system to aid novice designers

PANDA, the Pumper Apparatus Novice Design Assistant, is a case-based design system developed to assist firefighters who wish to design their pumper engines. In contrast to other such systems, PANDA addresses the unique needs of a novice, non-specialist who performs design in a highly specialized domain, where the design is decomposable into elements which each fulfill their own identifiable function. In PANDA we study how to create an interactive case-based design assistant that can understand the needs and desires of a non-specialist designer and can translate them into formal specifications; that can provide assistance by using case-based design methodologies; that can deal with non-functional design criteria such as aesthetics and traditional practices; and that can guide the novice designer by discussing alternatives, tradeoffs, and adaptations. Our prototypical system verifies our methodological approach to supporting novice design activities.