Tag: target detection

Abstract: The Functions of Multiple Instances (FUMI) method for learning target pattern and non-target patterns is introduced and extended. The FUMI method differs significantly from traditional supervised learning algorithms because only functions of target patterns are available. Moreover, these functions are likely to involve other non-target patterns. In this paper, data points which are convex […]

Abstract: The Multi-class Convex-FUMI (Multi-class C-FUMI) method is developed and described. The method is capable of learning prototypes for multiple target classes from hyperspectral imagery. Multi-class C-FUMI is a non-traditional supervised learning method based on the Functions of Multiple Instances (FUMI) concept. The FUMI concept differs significantly from traditional supervised by the assumption that only […]

Abstract: The Functions of Multiple Instances (FUMI) method for learning a target prototype from data points that are functions of target and non-target prototypes is introduced. In this paper, a specific case is considered where, given data points which are convex combinations of a target prototype and several non-target prototypes, the Convex-FUMI (C-FUMI) method learns […]

Abstract: In this paper, a study involving the detection of buried objects by fusing airborne Multi-Spectral Imagery (MSI) and ground-based Ground Penetrating Radar (GPR) data is investigated. The benefit of using the airborne sensor to cue the GPR, which will then search the area indicated by the MSI, is investigated and compared to results obtained […]

Abstract: We develop a vegetation mapping method using long-wave hyperspectral imagery and apply it to landmine detection. The novel aspect of the method is that it makes use of emissivity skewness. The main purpose of vegetation detection for mine detection is to minimize false alarms. Vegetation, such as round bushes, may be mistaken as mines […]