Rhonda D. Phillips

Hybrid image classification and parameter selection using a shared memory parallel algorithm

This work presents a shared memory parallel version of the hybrid classification algorithm IGSCR (iterative guided spectral class rejection) to facilitate the transition from serial to parallel processing. This transition is motivated by a demonstrated need for more computing power driven by the increasing size of remote sensing data sets due to higher resolution sensors, larger study regions, and the like. Parallel IGSCR was developed to produce fast and portable code using Fortran 95, OpenMP, and the Hierarchical Data Format version 5 (HDF5) and accompanying data access library. The intention of this work is to provide an efficient implementation of the established IGSCR classification algorithm. The applicability of the faster parallel IGSCR algorithm is demonstrated by classifying Landsat data covering most of Virginia, USA into forest and non-forest classes with approximately 90% accuracy. Parallel results are given using the SGI Altix 3300 shared memory computer and the SGI Altix 3700 with as many as 64 processors reaching speedups of almost 77. Parallel IGSCR allows an analyst to perform and assess multiple classifications to refine parameters. As an example, parallel IGSCR was used for a factorial analysis consisting of 42 classifications of a 1.2GB image to select the number of initial classes (70) and class purity (70%) used for the remaining two images.

An Adaptive Noise-Filtering Algorithm for AVIRIS Data With Implications for Classification Accuracy

This paper describes a new algorithm used to adaptively filter a remote-sensing data set based on signal-to-noise ratios (SNRs) once the maximum noise fraction has been applied. This algorithm uses Hermite splines to calculate the approximate area underneath the SNR curve as a function of band number, and that area is used to place bands into ldquobinsrdquo with other bands having similar SNRs. A median filter with a variable-sized kernel is then applied to each band, with the same size kernel used for each band in a particular bin. The proposed adaptive filters are applied to a hyperspectral image generated by the airborne visible/infrared imaging spectrometer sensor, and results are given for the identification of three different pine species located within the study area. The adaptive-filtering scheme improves image quality as shown by estimated SNRs. Classification accuracies of three pine species improved by more than 10% in the study area as compared to that achieved by the same discriminant method without adaptive spatial filtering.

Clean: A false alarm reduction method for SAR CCD

Synthetic Aperture Radar Coherent Change Detection (SAR CCD) is a sensitive change detector capable of finding ground surface height changes on the order of a radar wavelength. While this detector is capable of finding small changes such as tire tracks left on the ground, it is fraught with false alarms. This paper introduces a new algorithm, the Clutter Location, Estimation, And Negation (CLEAN) method, to remove multiple classes of false alarms, enhancing the detection of true change in CCD.

Test statistics for synthetic aperture radar coherent change detection

Coherent change detection using paired synthetic aperture radar images is typically performed using a classical estimator of coherence applied under an assumption of complex Gaussian data. The magnitudes of the resultant coherence estimates are plotted as an image and used to gauge changes in the observed scene. Here we investigate the suitability of an alternative coherence estimator that further assumes the variances of the populations underlying each paired sample to be equal. We show experimentally that this alternative estimator outperforms the classical estimator even when the underlying variances are not equal, as long as they are close enough. We demonstrate the suitability of this estimator directly on publicly available synthetic aperture radar data, with a performance improvement observed through increased contrast in the corresponding coherent change detection images.

Continuous Iterative Guided Spectral Class Rejection Classification Algorithm

This paper presents a new semiautomated soft classification method that is a hybrid between supervised and unsupervised classification algorithms for the classification of remote sensing data. Continuous iterative guided spectral class rejection (IGSCR) (CIGSCR) is based on the IGSCR classification method, a crisp classification method that automatically locates spectral classes within information class training data using clustering. This paper outlines the model and algorithm changes necessary to convert IGSCR to use soft clustering to produce soft classification in CIGSCR. This new algorithm addresses specific challenges presented by remote sensing data including large data sets (millions of samples), relatively small training data sets, and difficulty in identifying spectral classes. CIGSCR has many advantages over IGSCR, such as the ability to produce soft classification, less sensitivity to certain input parameters, potential to correctly classify regions that are not amply represented in training data, and a better ability to locate clusters associated with all classes. Furthermore, evidence is presented that the semisupervised clustering in CIGSCR produces more accurate classifications than classification based on clustering without supervision.

Finding curves in SAR CCD images

This paper introduces a pattern recognition and computer vision approach to mitigating false alarms in synthetic aperture radar (SAR) coherence change detection (CCD) images. In this paper, we perform an automatic detection of roads in SAR CCD images. The approach is based on a curve tracing algorithm originally proposed by Steger with modifications to better suit the goal of curve detection in SAR CCD images [1]. In our technique, the traditional Stegers method is used to detect curve points, and cubic splines are used to approximate the original curve. To detect roads more accurately, preprocessing and outlier removal techniques are performed along with the curve detection.

Two-Stage Change Detection for Synthetic Aperture Radar

Coherent change detection using paired synthetic aperture radar (SAR) images is often performed using a classical coherence estimator that is invariant to the true variances of the populations underlying each paired sample. While attractive, this estimator is biased and requires a significant number of samples to yield good performance. Increasing sample size often results in decreased image resolution. Thus, we propose the use of Bergers coherence estimate because, with the same number of pixels, the estimator effectively doubles the sample support without sacrificing resolution when the underlying population variances are equal or near equal. A potential drawback of this approach is that it is not invariant since its distribution depends on the pixel pair population variances. While Bergers estimator is inherently sensitive to the inequality of population variances, we propose a method of insulating the detector from this acuity. A two-stage change statistic is introduced to combine a noncoherent intensity change statistic given by the sample variance ratio, followed by the alternative Berger estimator, which assumes equal population variances. The first-stage detector identifies pixel pairs that have nonequal variances as changes caused by the displacement of sizeable object. The pixel pairs that are identified to have equal or near-equal variances in the first stage are used as an input to the second stage. The second-stage test uses the alternative Berger coherence estimator to detect subtle changes such as tire tracks and footprints. We show experimentally that the proposed method yields higher contrast SAR change detection images than the classical coherent change detector (state of the art), the alternative coherent change detector, and the intensity change detector. Experimental results are presented to show the effectiveness and robustness of the proposed algorithm for SAR change detection.

Automatic track tracing in SAR CCD images using search cues

In this paper, we present an algorithm for automatic vehicle track tracing in synthetic aperture radar coherent change detection (SAR CCD) images using search cues. The framework consists of two main steps. The first step uses a rotating matched filter that is modeled to characterize the appearance of vehicle tracks in SAR CCD imagery. For every pixel, the algorithm searches the orientations of the filter that best match the local orientations of the track path using normalized cross correlation. The second step includes track tracing from the estimated orientation image obtained from the previous step. Given a search cue, the tracing algorithm aims to find a parallel track path that maximizes the global length of the curve, and minimizes the differences in the pixel positions and orientations.

A study of fuzzy clustering within the IGSCR framework

The iterative guided spectral class rejection (IGSCR) classification algorithm uses an underlying clustering method and a decision rule to arrive at final classifications for remotely sensed data. Previous versions of IGSCR have used a hard clustering method such as k-means or ISODATA. In an effort to ultimately create a fuzzy version of IGSCR, this work uses an underlying fuzzy clustering algorithm within the IGSCR framework to study the effects of using the fuzzy clustering algorithm. IGSCR with fuzzy k-means was applied to a Landsat ETM+ satellite image to produce a two class classification (forest and nonforest), and results show that although fuzzy k-means did not lead to increased accuracy, the classification results are dramatically different for IGSCR using traditional k-means and fuzzy k-means.

COMBINED INTENSITY AND COHERENT CHANGE DETECTION FOR SYNTHETIC APERTURE RADAR

Coherent change detection using paired synthetic aperture radar images is performed using a classical coherence estimator applied under an assumption of complex Gaussian data. The magnitudes of the resulting coherence estimates are plotted as an image and used to gauge changes in the observed scene. In this paper, a two-stage change statistic that combines non-coherent and coherent change detection algorithms is proposed. In the first stage, a non-coherent intensity change detector is applied to test for changes caused by the displacement of a sizable object using the sample variance ratio test. The sample pairs that failed the first stage are used as an input to the second stage. The second stage test uses an alternative coherence estimator that assumes equal population variances, to detect subtle changes such as tire tracks and footprints. We show experimentally that the proposed method not only has a superior change detection performance over the classical coherent change detector, but also over either the non-coherent intensity change detector or the alternative coherent change detector, alone. Experimental results are presented to show the effectiveness and robustness of the proposed algorithm for SAR change detection.