We have developed a fully automated system for change detection of high-resolution satellite imagery. Our system, GeoCDX, is sensor-agnostic, resolution-independent and designed to process the very large volumes of data collected by modern high resolution panchromatic and multispectral imaging satellites. GeoCDX performs fully automated coregistration of imagery; extracts high-level features from the satellite imagery; performs change detection processing to pinpoint locations of change; clusters image tiles to group similar regions of change; and presents results in a variety of ways in an easy-to-use web application that facilitates online discovery, analysis, and dissemination of the change detection results. We applied GeoCDX to 4121 image pairs and successfully coregistered over 91% of the pairs covering a total area greater than 370 000 km2; GeoCDX decreased the average coregistration error from 9.6 ± 8.6 m to 1.8 ± 1.2 m. We show that for some pairs, GeoCDX provides up to a 50% increase in users' efficiency compared to manually performing change detection in common GIS software. Moreover, the change detection assessment performed using GeoCDX was on average four times more accurate compared to the manual approach in large part due to the use of our change intensity map that provides visual cues to the user during exploitation. Finally, change detection analysis using GeoCDX resulted in a missed detection rate of less than 2%.

Rapid detection of landmines and explosive hazards is a critical issue for modern military operations. Due to the varied nature of the objects of interest and the complexity of the surroundings, one approach is to utilize the superior recognition capabilities of the human brain in the detection process. We are developing frameworks and algorithms to process image video data from an RGB camera, mounted on a moving vehicle, and to provide cueing capability for a human-in-the-loop detection system. Feedback from the human operator is embedded into the system memory to aid future detection processes (causal). Due to the inherent variation of different objects of interest terms of color and texture appearance, and the inherent variation and complexity of the surroundings, we introduce a classification algorithm that operates on local decision boundaries. Each decision boundary is learned using the support vector machine (SVM) technique. Given test data, the K-nearest neighbor (KNN) method is used to pre-select the nearest training set to localize the scope of SVM training, giving us the local decision boundary.

Previously, we presented a method for comparing soft partitions (i.e. crisp, probabilistic, fuzzy and possibilistic) to a known crisp reference partition. Many of the classical indices that have been used with outputs of crisp clustering algorithms were generalized so that they are applicable for candidate partitions of any type. In particular, focus was placed on generalizations of the Rand index. In this article, we extend our prior work by (1) investigating the behavior of the soft Rand for comparing non-crisp, specifically possibilistic, partitions and (2) we demonstrate how the possibilistic Rand and visual assessment of (cluster) tendency (VAT) algorithm can be used to discover the number of actual clusters and coincident clusters for outputs from the possibilistic c-means (PCM) algorithm.

The Geospatial Change Detection and exploitation (GeoCDX) is a fully automated system for detection and exploitation of change between multitemporal high-resolution satellite and airborne images. Overlapping multitemporal images are first organized into 256 m × 256 m tiles in a global grid reference system. The system quantifies the overall amount of change in a given tile with a tile change score as an aggregation of pixel-level changes. The tiles are initially ranked by these change scores for retrieval, review, and exploitation in a Web-based application. However, the ranking does not account for the wide variety of change types that are typically observed in the top-ranked change tiles. To automatically organize the wide variety of change patterns observed in multitemporal high-resolution imagery, we perform tile clustering using the competitive agglomeration (CA) algorithm stabilized using the fuzzy c-means (FCM) algorithm. Each resulting cluster contains tiles with a visually similar type of change. By visual inspection of these tile clusters, GeoCDX users can quickly find certain types of change without having to sift through a large number of tiles initially organized solely by their tile change score, thereby reducing the time it takes for users to discover and exploit the change pattern(s) of greatest interest to a given application (e.g., urban growth, disaster assessment, facility monitoring, etc.). The tile clusters also provide a high-level overview of the various types of change that occur between the two observations. This overview is compared with a similar yet more limited view offered by a relevance feedback tool that requires a user to select sample tiles for use as samples in the reranking process.

GeoCDX (Geospatial Change Detection and eXploitation) is an integrated system for detecting change between multi-temporal, high-resolution satellite or airborne images. Overlapping images are organized into 256×256 meter tiles in a global grid system. A tile change score measures the amount of change in the tile which is the aggregation of pixel-level change score. The tiles are initially ranked by these change scores. However, this ranking does not account for the wide variety of change types. To learn the change patterns in the data, we apply the fuzzy c-means clustering algorithm to the tiles. Each resulting cluster contains tiles with similar type of change. Users looking for certain types of change can review the tile clusters rather than the more time consuming process of searching through the tile list based on the initial ranking. The clusters also provide users an overview of various types of change found in the scene.

The demand for high-resolution commercial satellite imagery (HR-CSI) has increased significantly over the last 5 years for a wide variety of applications. This demand has driven an increase in volume, frequency of acquisition, and spatial resolution of HR-CSI. In turn, this has spurred the need for more accurate and time-efficient processing tools for analyzing geospatial information to support user-specific applications. One such application is change detection between multi-temporal HR-CSI data. The significant increase in quantity and quality of multi-temporal HR-CSI data makes traditional manual analysis impractical. Thus, there is a need for a fully automated change detection system that not only identifies areas of change, but also allows users to filter, sort through, and analyze areas of change quickly and efficiently. Here we describe a tool - GeoCDX (Geospatial Change Detection and Exploitation) - to meet this need. GeoCDX is an integrated system that performs image ingestion and registration, feature extraction, and change analysis. The change detection results from GeoCDX are web accessible with additional interfaces to Google Earth™ (GE) and Google Maps™ (GM). In the near future GeoCDX will be integrated with its sister system GeoIRIS (Geospatial Information Retrieval and Indexing System). This integration will produce a very powerful HR-CSI analysis package with the change detection capabilities of GeoCDX and the content-based image retrieval system of GeoIRIS.

The goal of this work is to determine the object correspondence between a sketched map and the scene depicted by the sketch, e.g., as represented by an occupancy grid map (OGM) built by a robot. We describe a novel method based on spatial relations for accomplishing this task. Our method is based on using the histogram of forces as scene descriptors. We generate a correspondence map between two scene descriptors and evaluate its confidence. From this map, we generate a one-to-one object correspondence map for the two scenes such that the object correspondence confidence value is maximized. Challenges include the fact that the two scenes may differ in terms of the shape and size of the objects, their orientation, and the objects might be shifted due to translation. The approach is evaluated using several hand drawn sketches that were collected as a part of a user study. We believe that the ability to perform scene matching will make our sketch interface more robust and easier to use, thereby providing us with a more intuitive way of communicating with the robots

Heterogeneous genetic and epigenetic alterations are commonly found in human non-Hodgkin's lymphomas (NHL). One such epigenetic alteration is aberrant methylation of gene promoter-related CpG islands, where hypermethylation frequently results in transcriptional inactivation of target genes, while a decrease or loss of promoter methylation (hypomethylation) is frequently associated with transcriptional activation. Discovering genes with these relationships in NHL or other types of cancers could lead to a better understanding of the pathobiology of these diseases. The simultaneous analysis of promoter methylation using Differential Methylation Hybridization (DMH) and its associated gene expression using Expressed CpG Island Sequence Tag (ECIST) microarrays generates a large volume of methylation-expression relational data. To analyze this data, we propose a set of algorithms based on fuzzy sets theory, in particular Possibilistic c-Means (PCM) and cluster fuzzy density. For each gene, these algorithms calculate measures of confidence of various methylation-expression relationships in each NHL subclass. Thus, these tools can be used as a means of high volume data exploration to better guide biological confirmation using independent molecular biology methods.

There has been significant amount of study on the use of ground-penetrating radar (GPR) for land-mine detection. This paper presents our analysis of GPR data collected at a U.S. Army test site using a new approach based on frequency subband processing. In this approach, from the radar data that have over 2.5 GHz of bandwidth, we compute separate radar images using the one wide (2 GHz) and four narrow (0.6 GHz) frequency subbands. The results indicate that signals for different frequency subbands are significantly different and give very different performance in land-mine detection. In addition, we also examine a number of features extracted from the GPR data, including magnitude and local-contrast features, ratio between copolarization and cross-polarization signals, and features obtained using polarimetric decomposition. Feature selection procedures are employed to find subsets of features that improve detection performance when combined. Results of land-mine detection, including performance on blind test lanes, are presented

Advances in a scene matching approach based on objects spatial relationships are discussed. Objects spatial relationships are captured using force histograms, which are used as affine-invariant image descriptors. Object mapping across images is performed by finding the best correspondence map between image descriptors. Due to a very large search space, the particle swarm optimization method is used to facilitate more efficient search. The image descriptors map is then translated into an object map identifying the correspondences between objects in the two images.

Previously, we introduced a method for estimating the linguistic direction of motion of an object in a scene by processing a temporal sequence of linguistic descriptions of its position relative to a stationary object. This method relies on the observation of "regular" descriptions generated through a fuzzy logic system based on the histogram of forces. These descriptions may not exist if the distance between the two objects is too close. In this paper, we use fuzzy sets to generate degrees of activation for the estimated directions of motion. Temporal fusion is applied on these values over a fixed time window using weights calculated from linguistic information on the distance between the objects. The new dynamic linguistic descriptions are determined based on the present as well as the past estimates.

This paper examines object pair matching within LADAR (laser radar) imagery. By manipulating the three-dimensional data contained within a LADAR range image, it is possible to create a version of the scene as seen from above. In the transformed view, a fuzzy region represents each object, and the spatial relationship between two objects is represented by a force histogram. Matching two scenes then comes down to comparing force histograms. Each comparison gives a degree of similarity between the two object pairs considered, as well as an assessment of the pose parameters. These values can finally be combined to find a correct scene matching. Experiments on synthetic data as well as on real data demonstrate the applicability of our approach.

Fuzzy set methods have been used to model and manage uncertainty in various aspects of image processing, pattern recognition, and computer vision. High-level computer vision applications hold a great potential for fuzzy set theory because of its links to natural language. Linguistic scene description, a language-based interpretation of regions and their relationships, is one such application that is starting to bear the fruits of fuzzy set theoretic involvement. In this paper, we are expanding on two earlier endeavors. We introduce new families of fuzzy directional relations that rely on the computation of histograms of forces. These families preserve important relative position properties. They provide inputs to a fuzzy rule base that produces logical linguistic descriptions along with assessments as to the validity of the descriptions. Each linguistic output uses hedges from a dictionary of about 30 adverbs and other terms that can be tailored to individual users. Excellent results from several synthetic and real image examples show the applicability of this approach.

One of the longest standing problems in medical image analysis is that of the automated recognition of chromosomes from images of a metaphase spread of a cell. This process of visualizing and categorizing the chromosomes within a cell, called karyotyping, is a key factor in many medical procedures. It is a labor intensive activity, and hence, is a great candidate for automation. However, there are many sources of uncertainty in this problem domain, making complete karyotyping a difficult problem. We describe how fuzzy logic is being inserted into a complete karyotyping system to deal with uncertainty in similar chromosome classes.>