Point object detection method based on multiple-linear time difference scanning and expansion sampling

Abstract

Provided is a point object detection method based on multiple-linear time difference scanning and expansion sampling. The point object detection method comprises the steps of (1) constructing a double-linear-detector imaging device; (2) performing point object detection through each linear detector in an expansion sampling method; (3) respectively processing Nt groups of image data collected by each the linear detectors to obtain sub-pixel images, performing heterogeneity correction on two sub-pixels processed by the two linear detectors, performing sub pixel matching, and performing calculus of differences to complete background removal; (4) performing threshold filtering on residual images obtained through difference in the step (3), extracting positive and negative point pairs in the residual images through a neighborhood constraint criterion to complete detection and extraction of a moving point object in a scanning process, and forming neighborhood constraint criterion (as specified in the specification) according to imaging time differences of different linear detectors and the target movement speed, wherein delta D represents a distance between a positive area and a negative area, vmin represents the minimum movement speed of the detected object, vmax represents the maximum movement speed of the detected object, and GDS represents ground sampling distances of the linear detectors.

Description

technical field [0001] The invention belongs to the field of target detection and processing, and relates to a point target detection method based on multi-line sequence time-difference scanning extended sampling. Background technique [0002] The research on detection technology of weak and small moving targets in complex background has important applications in civil, aerospace and military. Due to the influence of long imaging distance and complex background, the target is dotted in the image, lacks sufficient texture information and shape information, and the signal-to-noise ratio of the target in the image is very low, which greatly increases the difficulty of weak target detection. At present, general-purpose optical imaging detection systems generally use a single detector for detection and imaging. For the acquired images, filter-based methods, wavelet-based methods, and morphological-based methods are used to perform single-frame background suppression. The suspect...

AI Technical Summary

Problems solved by technology

This detection system is often affected by the complex background, and cannot reliably extract targets on a single frame image. At the same time, this detection system does not make full use of the target's motion information in the process of target extraction, so it often has high false alarms. In order to improve the detection accuracy, complex target detection algorithms are required, which reduces the data processing efficiency

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