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  1. Model-based Edge Detector for Spectral Imagery Using Sparse Spatio-spectral Masks.

    Abstract not provided.
  2. Shift Estimation Algorithm for Dynamic Sensors with Frame-to-Frame Variation in Their Spectral Response.

    Abstract not provided.
  3. Detection theory for accurate and non-invasive skin cancer diagnosis using dynamic thermal imaging

    Skin cancer is the most common cancer in the United States with over 3.5M annual cases. Presently, visual inspection by a dermatologist has good sensitivity (> 90%) but poor specificity (< 10%), especially for melanoma, which leads to a high number of unnecessary biopsies. Here we use dynamic thermal imaging (DTI) to demonstrate a rapid, accurate and non-invasive imaging system for detection of skin cancer. In DTI, the lesion is cooled down and the thermal recovery is recorded using infrared imaging. The thermal recovery curves of the suspected lesions are then utilized in the context of continuous-time detection theory inmore » order to define an optimal statistical decision rule such that the sensitivity of the algorithm is guaranteed to be at a maximum for every prescribed false-alarm probability. The proposed methodology was tested in a pilot study including 140 human subjects demonstrating a sensitivity in excess of 99% for a prescribed specificity in excess of 99% for detection of skin cancer. To the best of our knowledge, this is the highest reported accuracy for any non-invasive skin cancer diagnosis method.« less
  4. Infrared retina

    Exemplary embodiments provide an infrared (IR) retinal system and method for making and using the IR retinal system. The IR retinal system can include adaptive sensor elements, whose properties including, e.g., spectral response, signal-to-noise ratio, polarization, or amplitude can be tailored at pixel level by changing the applied bias voltage across the detector. "Color" imagery can be obtained from the IR retinal system by using a single focal plane array. The IR sensor elements can be spectrally, spatially and temporally adaptive using quantum-confined transitions in nanoscale quantum dots. The IR sensor elements can be used as building blocks of anmore » infrared retina, similar to cones of human retina, and can be designed to work in the long-wave infrared portion of the electromagnetic spectrum ranging from about 8 .mu.m to about 12 .mu.m as well as the mid-wave portion ranging from about 3 .mu.m to about 5 .mu.m.« less
  5. Joint spatio-spectral based edge detection for multispectral infrared imagery.

    Image segmentation is one of the most important and difficult tasks in digital image processing. It represents a key stage of automated image analysis and interpretation. Segmentation algorithms for gray-scale images utilize basic properties of intensity values such as discontinuity and similarity. However, it is possible to enhance edge-detection capability by means of using spectral information provided by multispectral (MS) or hyperspectral (HS) imagery. In this paper we consider image segmentation algorithms for multispectral images with particular emphasis on detection of multi-color or multispectral edges. More specifically, we report on an algorithm for joint spatio-spectral (JSS) edge detection. By jointmore » we mean simultaneous utilization of spatial and spectral characteristics of a given MS or HS image. The JSS-based edge-detection approach, termed Spectral Ratio Contrast (SRC) edge-detection algorithm, utilizes the novel concept of matching edge signatures. The edge signature represents a combination of spectral ratios calculated using bands that enhance the spectral contrast between the two materials. In conjunction with a spatial mask, the edge signature give rise to a multispectral operator that can be viewed as a three-dimensional extension of the mask. In the extended mask, the third (spectral) dimension of each hyper-pixel can be chosen independently. The SRC is verified using MS and HS imagery from a quantum-dot in a well infrared (IR) focal plane array, and the Airborne Hyperspectral Imager.« less
  6. Multispectral rock-type separation and classification.

    This paper explores the possibility of separating and classifying remotely-sensed multispectral data from rocks and minerals onto seven geological rock-type groups. These groups are extracted from the general categories of metamorphic, igneous and sedimentary rocks. The study is performed under ideal conditions for which the data is generated according to laboratory hyperspectral data for the members, which are, in turn, passed through the Multi-spectral Thermal Imager (MTI) filters yielding 15 bands. The main challenge in separability is the small size of the training data sets, which initially did not permit direct application of Bayesian decision theory. To enable Bayseian classification,more » the original training data is linearly perturbed with the addition minerals, vegetation, soil, water and other valid impurities. As a result, the size of the training data is significantly increased and accurate estimates of the covariance matrices are achieved. In addition, a set of reduced (five) linearly-extracted canonical features that are optimal in providing the most important information about the data is determined. An alternative nonlinear feature-selection method is also employed based on spectral indices comprising a small subset of all possible ratios between bands. By applying three optimization strategies, combinations of two and three ratios are found that provide reliable separability and classification between all seven groups according to the Bhattacharyya distance. To set a benchmark to which the MTI capability in rock classification can be compared, an optimization strategy is performed for the selection of optimal multispectral filters, other than the MTI filters, and an improvement in classification is predicted.« less
  7. Joint spatio-spectral based edge detection for multispectral infrared imagery.

  8. Remote sensing data exploiration for geologic characterization of difficult targets : Laboratory Directed Research and Development project 38703 final report.

    Characterizing the geology, geotechnical aspects, and rock properties of deep underground facility sites can enhance targeting strategies for both nuclear and conventional weapons. This report describes the results of a study to investigate the utility of remote spectral sensing for augmenting the geological and geotechnical information provided by traditional methods. The project primarily considered novel exploitation methods for space-based sensors, which allow clandestine collection of data from denied sites. The investigation focused on developing and applying novel data analysis methods to estimate geologic and geotechnical characteristics in the vicinity of deep underground facilities. Two such methods, one for measuring thermalmore » rock properties and one for classifying rock types, were explored in detail. Several other data exploitation techniques, developed under other projects, were also examined for their potential utility in geologic characterization.« less

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"Hayat, Majeed M."

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