A Target Detection Method for Line Sampling Hyperspectral Data Based on Difference and Convolution Kernel

Abstract

The invention discloses a linear traveling line spectral data object detection method based on a difference and a convolution kernel, comprising steps of performing high spectral data pre-processing, removing exaggeration wave bands to perform de-equalization processing, choosing a suitable step length to perform traveling line sampling on the data, performing difference operation and convolution operation to obtain a characteristic value measuring the sampling cuboid, choosing a suitable threshold characteristic value to perform processing, wherein the area exceeding the threshold contains an object point, repeating the step 2 and the step 3 until traversing a whole cuboid data set to find the transverse position information of the object point, performing transposition on the hyperspectral data obtained from the step 1 in an image dimension, repeating the step 2 to step 4 until traversing the whole cuboid data set to find the column position information of the object point, and thus positioning the position information of the object point in the image. The method is clear in pricinple, clear in structure, small in calculation, low in time complexity, high in detection recognition and is applicable to the high speed object detection and positioning application of the high spectral image under the uniform background.

Description

technical field

[0001] The invention relates to a hyperspectral image method, in particular to a line sampling hyperspectral detection method based on difference and convolution operations. Background technique

[0002] Hyperspectral detection imaging is a multi-dimensional object spectral information acquisition technology that combines target detection technology with spectral imaging technology. The interspectral resolution of hyperspectral imaging is very high, and the general band width is within 10nm. The target object is imaged with nanometer ultra-high spectral resolution, and dozens or even hundreds of bands are acquired at the same time to form a continuous spectral image. Hyperspectral images can simultaneously acquire two-dimensional spatial information describing the distribution of ground objects and one-dimensional spectral information describing the spectral characteristics of ground objects. The spectral range covers visible light, near-infrared, short-wave ...

Images