Publications

Shyam Kuttikkad, Reuven Meth and Rama Chellappa, Registration and Exploitation of Multi-pass Airborne Synthetic Aperture Radar Images, Technical Report, Center for Automation Research, U. of Maryland, 1997.

Rama Chellappa, Qinfen Zheng, Shyam Kuttikkad, Chandra Shekhar and Philippe Burlina, Site Model Construction for the Exploitation of E-O and SAR Images, To appear in a book on Progress in IU, ed: O. Firschein and T. Strat, Morgan Kauffman Ltd.

Chandra Shekhar, Shyam Kuttikkad, Rama Chellappa and M. Thonnat, Knowledge-based integration of IU algorithms, Intl. Conference on Pattern Recognition, Vienna, Austria, 1996.

Shyam Kuttikkad, Rama Chellappa, and Les Novak, Building 2-D wide area site models from single- and multi-pass single polarization SAR data, SPIE Aerosense '96, Orlando, FL, March 1996.

Shyam Kuttikkad and Rama Chellappa, Building wide area 2-D site models from high resolution fully polarimetric Synthetic Aperture Radar images, IEEE Intl. Symposium on Computer Vision, Coral Gables, FL, pp. 84-85, November 1995.

Shyam Kuttikkad and Rama Chellappa, Non-Gaussian CFAR techniques for target detection in high resolution SAR images, IEEE Intl. Conf. in Image Processing, Austin, TX, pp. 910-914, November 1995.

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"Registration and Exploitation of Multi-pass Airborne Synthetic Aperture Radar Images" -Shyam Kuttikkad, Reuven Meth and Rama Chellappa

High resolution multi-pass, airborne Synthetic Aperture Radar (SAR) imagery is useful for surveillance and remote sensing applications. We present a technique for registering multiple high resolution SAR images, acquired from Unmanned Air Vehicles (UAVs) and other airborne platforms. A global affine transformation derived from the sensor acquisition parameters is used to automatically register the images, followed by a refinement to correct for translational errors. The registered SAR images are used for improving the accuracy of segmentation maps, and estimating heights of objects and target orientation angles.

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"Site Model Construction for the Exploitation of E-O and SAR Images" -Rama Chellappa, Qinfen Zheng, Shyam Kuttikkad, Chandra Shekhar and Philippe Burlina

Algorithms for building site models from electro-optical (E-O) and Synthetic Aperture Radar (SAR) imagery are presented in this paper. First, a detailed procedure for building a 3-D site model from several E-O reconnaissance images is presented. Issues such as initial site model setup, image-to-site-model registration, site model construction, and automatic control point correspondence are addressed. Next, an algorithm for building a wide-area 2-D site model from high-resolution SAR imagery is presented. A three-stage algorithm involving detection of bright pixels in a non-homogeneous background, statistical segmentation of the SAR image into homogeneous regions, and detection of man-made structures, is used for this purpose.

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"Knowledge-based integration of IU algorithms" -Chandra Shekhar, Shyam Kuttikkad, Rama Chellappa and M. Thonnat

This paper deals with the integration of image understanding (IU) programs using a knowledge-based approach. The basic concepts of program integration are discussed, and a simple problem-solving model for program integration is outlined. Two types of reasoning, planning and execution control, are identified. A system developed using this model, called OCAPI (Optimizing, Controlling and Automating the Processing of Images), is introduced. OCAPI is in an AI environment in which the reasoning used by the IU specialist is formally represented using frames and production rules. An example of an application developed using OCAPI is presented, and the advantages and shortcomings of this approach are discussed.

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"Building 2-D wide area site models from single- and multi-pass single polarization SAR data" -Shyam Kuttikkad, Rama Chellappa, and Les Novak

Wide area site models are useful for delineating regions of interest and assisting in tasks like monitoring and change detection. They are also useful in registering a newly acquired image to an existing one of the same site, or to a map. This paper presents an algorithm for building a two-dimensional (2-D) wide area site model from high resolution, single polarization Synthetic Aperture Radar (SAR) data. A three stage algorithm - involving detection of bright pixels, statistical segmentation of the data into homogeneous regions, and labeling/validation of segmentation results - is used for this task. Constant False Alarm Rate (CFAR) detectors are used for detecting bright pixels. Under assumptions of a suitable model for the statistical distribution of single polarization intensity or complex data, maximum likelihood (ML) labeling is used for initial segmentation. Knowledge of the acquisition parameters and other geometric cues are used to refine the initial segmentation and to extract man-made objects like buildings, and their shadows, as well as roads, from these images. When data from multiple passes of the same site is available, site models yield feature points which can be used to register the different images. In case complete information regarding the radar location, heading, and depression angle are available, the multiple views can be registered prior to site model construction, leading to improved performance. Site models are also useful for SAR data compression, where possible targets, man-made objects, and their neighborhoods are compressed losslessly and the background regions are compressed using lossy schemes.

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"Building Wide Area 2-D Site Models From High Resolution Fully Polarimetric Synthetic Aperture Radar Images" -Shyam Kuttikkad and Rama Chellappa

Wide area site models are useful for delineating regions of interest and assisting in tasks like monitoring and change detection. They are also useful in registering a newly acquired image to an existing one of the same site, or to a map. This paper presents a complete algorithm for building an approximate 2-D wide-area site model from high resolution, polarimetric Synthetic Aperture Radar (SAR) data. A three stage algorithm - involving detection of possible targets, statistical segmentation of the data into homogeneous regions, and validation of segmentation results - is used for this task. Constant False Alarm Rate (CFAR) detectors are used for target detection, while maximum likelihood labeling is used for initial segmentation. Knowledge of the sensor heading and other geometric cues are used to refine the initial segmentation and to extract man-made objects like buildings, and their shadows, as well as roads, from these images.

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"Non-Gaussian CFAR techniques for target detection in high resolution SAR images" - Shyam Kuttikkad and Rama Chellappa

Constant False Alarm Rate (CFAR) processing of Synthetic Aperture Radar (SAR) images facilitates target detection in spatially varying background clutter. The traditional Rayleigh distribution does not appear to be a good choice for modeling the natural terrain backscatter in high resolution SAR. We use the Weibull and K distributions to model clutter since they seem to fit observed data better and also include the Rayleigh distribution as a special case. The Cell Averaged CFAR technique works well in situations where a single, small target is present in locally homogeneous clutter. The Order Statistic CFAR is more useful for larger targets and in multiple target situations. Comparisons are made between the various CFAR techniques by applying them to real, high-resolution SAR images, obtained from the MIT Lincoln Laboratory.

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