Multiple Instance Hybrid Estimator for Learning Target Signatures

Signature-based detectors for hyperspectral target detection rely on knowing the specific target signature in advance. However, target signature are often difficult or impossible to obtain. Furthermore, common methods for obtaining target signatures, such as from laboratory measurements or manual selection from an image scene, usually do not capture the discriminative features of target class. In this paper, an approach for estimating a discriminative target signature from imprecise labels is presented. The proposed approach maximizes the response of the hybrid sub-pixel detector within a multiple instance learning framework and estimates a set of discriminative target signatures. After learning target signatures, any signature based detector can then be applied on test data. Both simulated and real hyperspectral target detection experiments are shown to illustrate the effectiveness of the method

Hyperspectral Imagery Target Detection Using Improved Anomaly Detection and Signature Matching Methods

This research extends the field of hyperspectral target detection by developing autonomous anomaly detection and signature matching methodologies that reduce false alarms relative to existing benchmark detectors, and are practical for use in an operational environment. The proposed anomaly detection methodology adapts multivariate outlier detection algorithms for use with hyperspectral datasets containing tens of thousands of non-homogeneous, high-dimensional spectral signatures. In so doing, the limitations of existing, non-robust, anomaly detectors are identified, an autonomous clustering methodology is developed to divide an image into homogeneous background materials, and competing multivariate outlier detection methods are evaluated for their ability to uncover hyperspectral anomalies. To arrive at a final detection algorithm, robust parameter design methods are employed to determine parameter settings that achieve good detection performance over a range of hyperspectral images and targets, thereby removing the burden of these decisions from the user. The final anomaly detection algorithm is tested against existing local and global anomaly detectors, and is shown to achieve superior detection accuracy when applied to a diverse set of hyperspectral images. The proposed signature matching methodology employs image-based atmospheric correction techniques in an automated process to transform a target reflectance signature library into a set of image signatures. This set of signatures is combined with an existing linear filter to form a target detector that is shown to perform as well or better relative to detectors that rely on complicated, information-intensive, atmospheric correction schemes. The performance of the proposed methodology is assessed using a range of target materials in both woodland and desert hyperspectral scenes

Application of Multi-Sensor Fusion Technology in Target Detection and Recognition

Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems

Resolving the terrestrial planet forming regions of HD113766 and HD172555 with MIDI

We present new MIDI interferometric and VISIR spectroscopic observations of HD113766 and HD172555. Additionally we present VISIR 11um and 18um imaging observations of HD113766. These sources represent the youngest (16Myr and 12Myr old respectively) debris disc hosts with emission on <<10AU scales. We find that the disc of HD113766 is partially resolved on baselines of 42-102m, with variations in resolution with baseline length consistent with a Gaussian model for the disc with FWHM of 1.2-1.6AU (9-12mas). This is consistent with the VISIR observations which place an upper limit of 0."14 (17AU) on the emission, with no evidence for extended emission at larger distances. For HD172555 the MIDI observations are consistent with complete resolution of the disc emission on all baselines of lengths 56-93m, putting the dust at a distance of >1AU (>35mas). When combined with limits from TReCS imaging the dust at ~10um is constrained to lie somewhere in the region 1-8AU. Observations at ~18um reveal extended disc emission which could originate from the outer edge of a broad disc, the inner parts of which are also detected but not resolved at 10um, or from a spatially distinct component. These observations provide the most accurate direct measurements of the location of dust at 1-8AU that might originate from the collisions expected during terrestrial planet formation. These observations provide valuable constraints for models of the composition of discs at this epoch and provide a foundation for future studies to examine in more detail the morphology of debris discs.Comment: 22 pages, 19 figures, accepted for publication in MNRA