Matthew N. Klaric

Entropy-Balanced Bitmap Tree for Shape-Based Object Retrieval From Large-Scale Satellite Imagery Databases

In this paper, we present a novel indexing structure that was developed to efficiently and accurately perform content-based shape retrieval of objects from a large-scale satellite imagery database. Our geospatial information retrieval and indexing system, GeoIRIS, contains 45 GB of high-resolution satellite imagery. Objects of multiple scales are automatically extracted from satellite imagery and then encoded into a bitmap shape representation. This shape encoding compresses the total size of the shape descriptors to approximately 0.34% of the imagery database size. We have developed the entropy-balanced bitmap (EBB) tree, which exploits the probabilistic nature of bit values in automatically derived shape classes. The efficiency of the shape representation coupled with the EBB tree allows us to index approximately 1.3 million objects for fast content-based retrieval of objects by shape.

GeoCDX: An Automated Change Detection & Exploitation System for High Resolution Satelite Imagery

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 Earthtrade (GE) and Google Mapstrade (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.

Automated object extraction through simplification of the differential morphological profile for high-resolution satellite imagery

This paper presents an approach for automated, multi-scale object extraction from high-resolution satellite imagery. Our algorithm combines techniques from mathematical morphology and principal components analysis (PCA) to identify building footprints in scenes. There are three major components in the algorithm: First, the differential morphological profile (DMP) of the image is constructed using structuring elements (SE) of varying sizes. Several preprocessing techniques are applied to the DMP. Next, the values for an entire profile at a given pixel are combined into a k-dimensional vector representing that pixel, where k is the number of resolution levels. PCA is applied to the set of pixel vectors to reduce the number of dimensions needed to represent the data of the DMP, yet capture a significant portion of their variance. We call the intermediate results of PCA the Eigen-opening image and Eigen-closing image. In the final stage of processing, candidate objects are extracted from the Eigen images. Applying a minimal amount of heuristics we can automatically merge the Eigen images into a set of objects. Additionally, spatial information is used to correlate man-made objects with their shadows from the closing profile, if they exist. The efficiency of this algorithm, coupled with it’s robustness, allow it to be useful as an online object extraction tool for geospatial applications.

Clustering of Detected Changes in High-Resolution Satellite Imagery Using a Stabilized Competitive Agglomeration 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.

Multi-Index Multi-Object Content-Based Retrieval

In many large-scale content-based retrieval (CBR) applications, the input to the search process is a complex query that may be composed of several constituent parts. The proposed approach performs CBR queries by breaking down a complex query into several smaller heterogeneous queries. Object-based queries in an imagery search application can be performed by executing a search over several distinct feature space indexes. For example, CBR indexes may exist for spectral, texture, and shape feature vectors extracted from objects. A query for similar objects can be completed by aggregating the results from these multiple indexes. Complementing this concept, a multi-object search can be used to identify relevant groups of objects which match a given set of query objects. For example, a set of objects identified in satellite imagery could be used as a CBR query in order to identify similar groups of objects. Thus, a query can be performed for each object, and these results can be aggregated into multi-object search results by determining the optimal match of the query objects to those in each resulting group. We introduce the absence penalty method and obligatory object query algorithms for performing multi-index and multi-object CBR searches and provide experimental results that show that the proposed approaches efficiently provide search results with a high degree of precision with minimal error. The experimental results shown demonstrate the efficiency and accuracy of the proposed methods; moreover, through the fusion of multi-index and multi-object search techniques, we are able to construct new sophisticated query mechanisms.

Information mining from human visual reasoning about multi-temporal, high-resolution satellite imagery

Over time experts in image analysis learn to use visual cues to extract large amount of information from an image in a short period of time in comparison to novice viewers. This tacit knowledge of image search strategies develops through many years of experience. Efficient interpretation appears to be a gut instinct of analysts, and this ability is difficult to verbalise or teach to the next generation of analysts. To bridge the gap between experts and novices, we propose a method to attempt to uncover visual strategies in geospatial imagery through eye tracking. Going beyond typical feature-based and classification approaches, our research fuses measures of visual attention with image-based features to derive rules through associative mining. We study how the cognitive processing of an image relates to image content. This research may open the door to a better understanding of human reasoning about the analysis of multi-temporal VHR satellite imagery.

Predicting Relevant Change in High Resolution Satellite Imagery

With the ever increasing volume of remote sensing imagery collected by satellite constellations and aerial platforms, the use of automated techniques for change detection has grown in importance, such that changes in features can be quickly identified. However, the amount of data collected surpasses the capacity of imagery analysts. In order to improve the effectiveness and efficiency of imagery analysts performing data maintenance activities, we propose a method to predict relevant changes in high resolution satellite imagery based on human annotations on selected regions of an image. We study a variety of classifiers in order to determine which is most accurate. Additionally, we experiment with a variety of ways in which a diverse set of training data can be constructed to improve the quality of predictions. The proposed method aids in the analysis of change detection results by using various classifiers to develop a relevant change model that can be used to predict the likelihood of other analyzed areas containing a relevant change or not. These predictions of relevant change are useful to analysts, because they speed the interrogation of automated change detection results by leveraging their observations of areas already analyzed. A comparison of four classifiers shows that the random forest technique slightly outperforms other approaches.