Research On Dimensionality Reduction Method Of Hyperspectral Image For 3D Reconstruction

Hyperspectral images can provide tens or even hundreds of high spectral resolution band images,which cover the visible to infrared region,and contain rich information about the spatial spectral distribution associated with the physical,chemical,and geometric properties of the material of the object.It has been widely used in environmental monitoring,atmospheric and ocean remote sensing,military field and planetary exploration.In recent years,with the development of hyperspectral data processing technology and 3D reconstruction technology,many researchers combine object 3D geometric information with spectral information to generate hyperspectral 3D models.Hyperspectral 3D models are meaningful for 3D applications requiring fine spectral analysis.It can be used for mineral classification and quantitative analysis,cultural relics research,plant quantitative analysis,etc.,and has broad application prospects.At present,the mainstream method for 3D reconstruction based on hyperspectral imagery is band-by-band 3D reconstruction,and then the 3D point cloud of all bands is fused to generate a hyperspectral 3D model.However,the amount of data in hyperspectral images is large,which causes computational burden,and the high correlation of adjacent spectra causes redundancy of data.Computation of 3D models by band will result in computational waste and is not necessary.Then data dimensionality reduction becomes very important.The existing hyperspectral data dimensionality reduction technology is mainly applied to remote sensing image classification and other applications.The dimensionality reduction research for 3D reconstruction is rare,and the applicability of existing methods to hyperspectral 3D reconstruction is also no related studies.This paper focuses on the method of dimensionality reduction of hyperspectral imagery for 3D reconstruction.The main work is as follows:(1)Study the algorithm principle of the existing hyperspectral image band selection method.The existing hyperspectral image band selection method is to select the band subset from the original band,and the band subset represents the main information of the original image,and the redundancy.Lower.Three kinds of band selection methods are studied,and then three representative methods are verified by experiments: subspace division method,graph representation method,density clustering based method,and three methods for hyperspectral imagery.The applicability of 3D reconstruction.(2)Visual word bag model is a common method in computer vision image processing.For the first time,the visual word bag model is applied to the hyperspectral image dimensionality reduction problem.The visual word is constructed according to the feature point description subset of all band images of hyperspectral image.The bag model calculates the similarity between the bands according to the visual word bag model,and based on this,the band selection is performed.According to the experiment,the band selection method based on the visual tape model is stable and effective.(3)According to the characteristics of three-dimensional reconstruction of hyperspectral images,hyperspectral images contain multiple band images.Different bands can reflect different features of the target.The feature point descriptor is the description of the image features.The feature point descriptor is the basis of image matching and 3D reconstruction.Compared with the traditional dimensionality reduction method,the image dimensionality reduction for the 3D reconstruction requires the band.Set high representation and low redundancy at the feature point description level.Based on this,two band selection methods for 3D reconstruction are proposed in this paper.The band selection method based on SIFT feature and the band selection method based on ORB feature are verified by experiments.The feature representation of the two methods is better than the commonly used method.It is also able to reflect more complete and detailed spectral information,and the method based on ORB features under the same conditions is superior to the method based on SFIT features.(4)According to the purpose of 3D reconstruction,this paper proposes an evaluation method based on feature representation,and uses the descriptive mean difference measure of the feature subset of the band subset to measure the representativeness of the band subset.The spectral representative evaluation method is proposed.The spectral range is divided into several intervals.By discussing the band redundancy and representativeness in each sub-interval,the integrity and fineness of the original spectral information expressed by the band set are evaluated.