Texture information has shown a significant contribution to patternrecognition in hyperspectral image (HSI) analysis. In this paper, a multi-component based the volumetric directional pattern (MC-VDP) is proposed for...
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ISBN:
(数字)9781510618107
ISBN:
(纸本)9781510618107
Texture information has shown a significant contribution to patternrecognition in hyperspectral image (HSI) analysis. In this paper, a multi-component based the volumetric directional pattern (MC-VDP) is proposed for HSI classification. The original VDP operator extracts a three-dimensional texture feature from three consecutive bands by applying eight directional Kirsch filters to the raw intensity values. However, the local sign and local magnitude components, that are generated by a local difference sign-magnitude transform, are not incorporated before Kirsch masking. In this work, we first compute the local sign and local magnitude components followed by VDP operator and then combine them with the original VDP feature to form MC-VDP. By analyzing the local sign and local magnitude components, two volumetric texture features are obtained, namely VDP-Sign (VDP-S) and VDP-Magnitude (VDP-M). Thus MC-VDP operator is constituted of VDP-S, VDP-M, and the original VDP features. In details, VDP-S and VDP-M preserve additional discriminant information to describe the volumetric local structures in HSI, and they can be readily fused since their scheme are constructed in the same fashion. From experimental results, it is observed that a fusion of VDP-S, VDP-M, and the original VDP coded maps provides more discriminant information and thus better classification accuracy compared to the other popular spatial feature extraction methods.
Wide Area Motion Imagery (WAMI) systems used on surveillance aircraft may suffer from system calibration errors associated with frequent re-installation. These geo-coding errors corrupt the quality of mapping of the t...
详细信息
ISBN:
(数字)9781510618107
ISBN:
(纸本)9781510618107
Wide Area Motion Imagery (WAMI) systems used on surveillance aircraft may suffer from system calibration errors associated with frequent re-installation. These geo-coding errors corrupt the quality of mapping of the tracked objects, 'movers', from the image frame into world reference frame. In this study, an automated system for calibration of the imagery captured with six-camera WAMI array has been developed. The automatic calibration was achieved by a system of several multi-scale feature classifiers adaptively applied to an image captured by the camera array dependent on the feature availability and classifier accuracy. The feature extraction and association modules were designed to be operating independently on a frame from any given camera. The choice of the module was performed automatically using a decision tree designed as a part of the system architecture. Calculation of the per-frame corrections to mitigate the localisation error of the movers was performed by associating the features detected in each individual camera and features extracted from available satellite imagery used as a datum. The effects of the distance to the feature and the choice of the feature extraction module on the mover localisation accuracy have been evaluated on 300 frames (6 images each) captured with the WAMI array. Significant reduction in the magnitude of the geo-coding error (from 15.77-36.54 m to 5.42-8.55 m on average) was achieved and can be seen in improved alignment of the features projected into the frame as well as the reliable mapping of the mover trajectories across frames. Unlike similar systems, focusing on post-processing, the WAMI calibration system presented in the paper was designed for continuous parameter estimation in real-time.
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