Engineering topics

Tilton, James C.

Publications and source records attributed to Tilton, James C..

79 records · Page 5

NASA NTRS1991

A tool for interactive exploration of a hierarchical segmentation

Hierarchical segmentation is discussed as a form of region growing in which the sequence of merges is controlled by a 'best merge first' principle, and a record of the region merging sequence is often retained for later analysis. In the author's massively parallel implementation of hierarchical segmentation, which he calls iterative parallel region growing (IPRG), a set of directional edge maps are used to store the region merging sequence information. An iteractive tool is described that allows an analyst to fully explore a hierarchical segmentation, potentially producing an image segmentation that is a combination of segmentations produced at several different iterations of the IPRG algorithm. A potential method for automatically producing such segmentations is also discussed.

Tilton, James C.↗

NASA NTRS1991

Applications of the massively parallel machine, the MasPar MP-1, to Earth sciences

The computational workload of upcoming NASA science missions, especially the ground data processing for the Earth Observing System, is projected to be quite large (in the 50 to 100 gigaFLOPS range) and corespondingly very expensive to perform using conventional supercomputer systems. High performance, general purpose massively parallel computer systems such as the MasPar MP-1 are being investigated by NASA as a more cost effective alternative. Massively parallel systems are targeted for accelerated development and maturation by NASA's upcoming five-year High Performance Computing and Communications Program. A summary of the broad range of applications currently running on the MP-1 at NASA/Goddard are presented in this paper along with descriptions of the parallel algorithmic techniques employed in five applications that have bearing on Earth sciences.

Fischer, James R.↗

NASA NTRS1989

Data compression experiments with LANDSAT thematic mapper and Nimbus-7 coastal zone color scanner data

A case study is presented where an image segmentation based compression technique is applied to LANDSAT Thematic Mapper (TM) and Nimbus-7 Coastal Zone Color Scanner (CZCS) data. The compression technique, called Spatially Constrained Clustering (SCC), can be regarded as an adaptive vector quantization approach. The SCC can be applied to either single or multiple spectral bands of image data. The segmented image resulting from SCC is encoded in small rectangular blocks, with the codebook varying from block to block. Lossless compression potential (LDP) of sample TM and CZCS images are evaluated. For the TM test image, the LCP is 2.79. For the CZCS test image the LCP is 1.89, even though when only a cloud-free section of the image is considered the LCP increases to 3.48. Examples of compressed images are shown at several compression ratios ranging from 4 to 15. In the case of TM data, the compressed data are classified using the Bayes' classifier. The results show an improvement in the similarity between the classification results and ground truth when compressed data are used, thus showing that compression is, in fact, a useful first step in the analysis.

Tilton, James C.↗

NASA NTRS1989

Image segmentation by iterative parallel region growing and splitting

The spatially constrained clustering (SCC) iterative parallel region-growing technique is applied to image analysis. The SCC algorithm is implemented on the massively parallel processor at NASA Goddard. Most previous region-growing approaches have the drawback that the segmentation produced depends on the order in which portions of the image are processed. The ideal solution to this problem (merging only the single most similar pair of spatially adjacent regions in the image in each iteration) becomes impractical except for very small images, even on a massively parallel computer. The SCC algorithm overcomes these problems by performing, in parallel, the best merge within each of a set of local, possibly overlapping, subimages. A region-splitting stage is also incorporated into the algorithm, but experiments show that region splitting generally does not improve segmentation results. The SCC algorithm has been tested on various imagery data, and test results for a Landsat TM image are summarized.

Tilton, James C.↗

NASA NTRS1988

LANDSAT-4 and LANDSAT-5 Multispectral Scanner Coherent Noise Characterization and Removal

A technique is described for characterizing the coherent noise found in LANDSAT-4 and LANDSAT-5 MSS data and a companion technique for filtering out the coherent noise. The techniques are demonstrated on LANDSAT-4 and LANDSAT-5 MSS data sets, and explanations of the noise pattern are suggested in Appendix C. A cookbook procedure for characterizing and filtering the coherent noise using special NASA/Goddard IDIMS functions is included. Also presented are analysis results from the retrofitted LANDSAT-5 MSS sensor, which shows that the coherent noise has been substantially reduced.

Tilton, James C.↗

NASA NTRS1988

Image segmentation by iterative parallel region growing with application to data compression and image analysis

Image segmentation can be a key step in data compression and image analysis. However, the segmentation results produced by most previous approaches to region growing are suspect because they depend on the order in which portions of the image are processed. An iterative parallel segmentation algorithm avoids this problem by performing globally best merges first. Such a segmentation approach, and two implementations of the approach on NASA's Massively Parallel Processor (MPP) are described. Application of the segmentation approach to data compression and image analysis is then described, and results of such application are given for a LANDSAT Thematic Mapper image.

Tilton, James C.↗

NASA NTRS1987

Contextual classification on the massively parallel processor

Classifiers are often used to produce land cover maps from multispectral Earth observation imagery. Conventionally, these classifiers have been designed to exploit the spectral information contained in the imagery. Very few classifiers exploit the spatial information content of the imagery, and the few that do rarely exploit spatial information content in conjunction with spectral and/or temporal information. A contextual classifier that exploits spatial and spectral information in combination through a general statistical approach was studied. Early test results obtained from an implementation of the classifier on a VAX-11/780 minicomputer were encouraging, but they are of limited meaning because they were produced from small data sets. An implementation of the contextual classifier is presented on the Massively Parallel Processor (MPP) at Goddard that for the first time makes feasible the testing of the classifier on large data sets.

Tilton, James C.↗