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This work focuses on incorporating pairwise constraints into a spectral clustering algorithm. A new constrained spectral clustering method is proposed, as well as an active constraint acquisition technique and a heuristic for parameter selection. We demonstrate that our constrained spectral clustering method, CSC, works well when the data exhibits what we term local proximity structure.
This paper describes an algorithm for hierarchical image segmentation (referred to as HSEG) and its recursive formulation (referred to as RHSEG). The HSEG algorithm is a hybrid of region growing and constrained spectral clustering that produces a hierarchical set of image segmentations based on detected convergence points. In the main, HSEG employs the hierarchical stepwise optimization (HS WO) approach to region growing, which seeks to produce segmentations that are more optimized than those produced by more classic approaches to region growing. In addition, HSEG optionally interjects between HSWO region growing iterations merges between spatially non-adjacent regions (i.e., spectrally based merging or clustering) constrained by a threshold derived from the previous HSWO region growing iteration. While the addition of constrained spectral clustering improves the segmentation results, especially for larger images, it also significantly increases HSEG's computational requirements. To counteract this, a computationally efficient recursive, divide-and-conquer, implementation of HSEG (RHSEG) has been devised and is described herein. Included in this description is special code that is required to avoid processing artifacts caused by RHSEG s recursive subdivision of the image data. Implementations for single processor and for multiple processor computer systems are described. Results with Landsat TM data are included comparing HSEG with classic region growing. Finally, an application to image information mining and knowledge discovery is discussed.
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High-sensitivity IRAS coadded survey data, coupled with new high-sensitivity near-IR observations, are used to investigate the nature of embedded objects over an 4.3-sq-pc area comprising the central star-forming cloud of the Ophiuchi molecular complex; the area encompasses the central cloud of the Rho Ophiuchi complex and includes the core region. Seventy-eight members of the embedded cluster were identified; spectral energy distributions were constructed for 53 objects and were compared with theoretical models to gain insight into their evolutionary status. Bolometric luminosities could be estimated for nearly all of the association members, leading to a revised luminosity function for this dust-embedded cluster.
This work focuses on the active selection of pairwise constraints for spectral clustering. We develop and analyze a technique for Active Constrained Clustering by Examining Spectral eigenvectorS (ACCESS) derived from a similarity matrix.
Spectral classification has been carried out for B-type stars in Ori OBl Ib, NGC 2362, IC 5146 and NGC 2264. Six stars with weak helium lines for their UBV colors were found in Ori OBl Ib, one in NGC 2362 which might be helium variable, and none in IC 5146. The star Walker No 109 (B6V) in NGC 2264 is a helium variable which has developed an outstanding absorption feature between 4144 and 4156 A.
The hierarchical image segmentation algorithm (referred to as HSEG) is a hybrid of hierarchical step-wise optimization (HSWO) and constrained spectral clustering that produces a hierarchical set of image segmentations. HSWO is an iterative approach to region grooving segmentation in which the optimal image segmentation is found at N(sub R) regions, given a segmentation at N(sub R+1) regions. HSEG's addition of constrained spectral clustering makes it a computationally intensive algorithm, for all but, the smallest of images. To counteract this, a computationally efficient recursive approximation of HSEG (called RHSEG) has been devised. Further improvements in processing speed are obtained through a parallel implementation of RHSEG. This chapter describes this parallel implementation and demonstrates its computational efficiency on a Landsat Thematic Mapper test scene.
Comparisons were made between subscenes from the first TM scene acquired of the Washington, D.C. area and a MSS scene acquired approximately one year earlier. Three types of analyses were conducted to compare TM and MSS data: a water body analysis, a principal components analysis and a spectral clustering analysis. The water body analysis compared the capability of the TM to the MSS for detecting small uniform targets. Of the 59 ponds located on aerial photographs 34 (58%) were detected by the TM with six commission errors (15%) and 13 (22%) were detected by the MSS with three commission errors (19%). The smallest water body detected by the TM was 16 meters; the smallest detected by the MSS was 40 meters. For the principal components analysis, means and covariance matrices were calculated for each subscene, and principal components images generated and characterized. In the spectral clustering comparison each scene was independently clustered and the clusters were assigned to informational classes. The preliminary comparison indicated that TM data provides enhancements over MSS in terms of (1) small target detection and (2) data dimensionality (even with 4-band data). The extra dimension, partially resultant from TM band 1, appears useful for built-up/non-built-up area separation.
The development of a computer program is reported for extracting features from remotely sensed data presented in digital image form. This computer program requires no human supervision or prejudgment and operates unassisted on the raw digital data. A condensed general background is included on remote sensing of earth features and a short synopsis on some of the most commonly used types of feature extraction techniques. Results obtained from the unsupervised feature extraction computer program along with a description and listing of the computer program are presented.
Some preliminary indications were provided as to the relative merits of actual TM data versus MSS data for land cover mapping related applications. Three analyses were designed which had sensitivity to the differences in spectral, spatial and radiometric parameters between the TM and MSS. In the water body analysis, a primarily spatially related test, the detectability of small uniform targets was examined. The principal components analysis, an examination of the inherent dimensionality of the data, was more spectrally and radiometrically related. The spectral clustering analysis, also heavily spectrally and radiometrically influenced, provided information on the types of targets separable on TM versus MSS data. These analyses were to be conducted with simultaneously collected LANDSAT-4 complete TM (7 band) and MSS (4 band) data. In actuality, 4-band TM data, and archived LANDSAT-2 MSS data of the same area were used.
The NASA Analogy Software Costing Tool Suite (ASCoT) consists of a cluster-based analogy estimator for estimating software development effort, a K-Nearest Neighbors (KNN) analogy estimator for estimating effort and delivered lines of code, a simple regression-based cost estimating relationship (CER) model that estimates cost in dollars, and a probabilistic version of COCOMO II. In this paper we document the analogy algorithms as well as summarize the results of the performance of the KNN and the principle components (PCA) cluster analogy models. KNN performance is assessed by varying the number of inputs and number of neighbors. Four different clustering methods: K-means, Spectral Clustering, Hierarchical Clustering, and Principle Components Analysis (PCA), and their respective evaluation criterion are described in detail. The comparative performance of all four estimation models is assessed using magnitude of relative error (MRE) measurements.
A hierarchical set of image segmentations is a set of several image segmentations of the same image at different levels of detail in which the segmentations at coarser levels of detail can be produced from simple merges of regions at finer levels of detail. In [1], Tilton, et a1 describes an approach for producing hierarchical segmentations (called HSEG) and gave a progress report on exploiting these hierarchical segmentations for image information mining. The HSEG algorithm is a hybrid of region growing and constrained spectral clustering that produces a hierarchical set of image segmentations based on detected convergence points. In the main, HSEG employs the hierarchical stepwise optimization (HSWO) approach to region growing, which was described as early as 1989 by Beaulieu and Goldberg. The HSWO approach seeks to produce segmentations that are more optimized than those produced by more classic approaches to region growing (e.g. Horowitz and T. Pavlidis, [3]). In addition, HSEG optionally interjects between HSWO region growing iterations, merges between spatially non-adjacent regions (i.e., spectrally based merging or clustering) constrained by a threshold derived from the previous HSWO region growing iteration. While the addition of constrained spectral clustering improves the utility of the segmentation results, especially for larger images, it also significantly increases HSEG s computational requirements. To counteract this, a computationally efficient recursive, divide-and-conquer, implementation of HSEG (RHSEG) was devised, which includes special code to avoid processing artifacts caused by RHSEG s recursive subdivision of the image data. The recursive nature of RHSEG makes for a straightforward parallel implementation. This paper describes the HSEG algorithm, its recursive formulation (referred to as RHSEG), and the implementation of RHSEG using massively parallel GNU-LINUX software. Results with Landsat TM data are included comparing RHSEG with classic region growing.
Absolute spectral energy distributions were measured in galaxies of three distant clusters. Redshifts and information on evolutionary effects are inferred by a comparison of the results with the integrated light energy distribution for nearby giant elliptical galaxies. Evidence for evolution over the last 3 to 6 billion years could not be established for these galaxies.
The Arches cluster is a young, densely packed massive star cluster in our Galaxy that shows a high level of star formation activity. The nature of the extended non-thermal X-ray emission around the cluster remains unclear. The observed bright Fe K(alpha) line emission at 6.4 keV from material that is neutral or in a low ionization state can be produced either by X-ray photoionization or by cosmic-ray particle bombardment or both. In this paper, we report on the first detection of the extended emission around the Arches cluster above 10 keV with the NuSTAR mission, and present results on its morphology and spectrum. The spatial distribution of the hard X-ray emission is found to be consistent with the broad region around the cluster where the 6.4 keV line is observed. The interpretation of the hard X-ray emission within the context of the X-ray reflection model puts a strong constraint on the luminosity of the possible illuminating hard X-ray source. The properties of the observed emission are also in broad agreement with the low-energy cosmic-ray proton excitation scenario. Key words: cosmic rays - Galaxy: center - ISM: general - X-rays: individual (Arches cluster)
A manual interpretation of ERTS-1 MSS system corrected imagery has been performed on a study area within the Houston Area Test Site to classify land use using the Level 1 categories proposed by the Department of the Interior. The two types of imagery used included: (1) black and white transparencies of each band enlarged to a scale of approximately 1:250,000 and (2) color transparencies composited from the computer compatible tapes using the film recorder on a multispectral data analysis station. The results of this interpretation have been compared with the 1970 land use inventory of HATS which was compiled using color ektachrome imagery from high altitude aircraft (scale 1:120,000). Urban data from the same scene was also analyzed using a computer-aided (clustering) technique. The resulting clusters, representing areas of similar content, were compared with existing land use patterns in Houston. A technique was developed to correlate the spectral clusters to specific urban features on aircraft imagery by the location of specific, high contrast objects in particular resolution elements. It was concluded that ERTS-1 data could be used to develop Level 1 and many Level 2 land use categories for regional inventories and perhaps to some degree on a local level.
Estimators and analysts are increasingly being tasked to develop better models and reliable cost estimates in support of program planning and execution. While there has been extensive work on improving parametric methods for cost estimation, there is very little focus on the use of cost models based on analogy and clustering algorithms. In this paper we summarize the results of our research in developing an analogy method for estimating NASA spacecraft flight software using spectral clustering on system characteristics (symbolic nonnumerical data) and evaluate its performance by comparing it to a number of the most commonly used estimation methods. The strengths and weaknesses of each method based on their performance are also discussed. The paper concludes with an overview of the analogy estimation tool (ASCoT) developed for use within NASA that implements the recommended analogy algorithm.
Skylab S-192 multispectral scanner data, in 12 bands covering wavelengths from 0.41 to 2.3 microns, have been investigated to identify and classify geologic units of the lunar surface. Seventeen spectral cluster classes have been identified, seven in the highlands, seven in the maria, and three of which occur in both or in border regions. This finding may be roughly indicative of the relative heterogeneity of these regions. It implies that there is as much heterogeneity in the highlands as in the maria. This work extends the spectral and aerial coverage of similar studies of the lunar surface and provides useful data for comparison for most of the lunar near side.
The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.