Research On Remote Sensing Image Stitching Algorithm Based On Improved SIFT Algorithm

UAV as a new low-altitude remote sensing platform is rapid development.It has been widely used in many regions such as agriculture,land surveying,disaster prevention and so on.Especially in agriculture,with the UAV has characteristic of fast imaging,low cost of flight and maintenance,high image resolution.It has been widely used in precision agriculture,disease and pest prediction,crop yield estimation,crop nutrient analysis and so on,and has a relatively good application results.Meanwhile,as our country has vast territory and large crop area,UAV still has a very large space for development in agriculture.However,due to the limited resources and flight height,the images acquired by airborne equipment often can not cover the whole task area.Therefore,in order to obtain the image of the whole task area,it is necessary to mosaic the gathered image.Because of the quality of the stitching determine the accuracy of Agricultural information analysis.Therefore,it is of great practical significance to study the field crop remote sensing image splicing technology.There are three methods of remote sensing image matching: Image matching based on gray information,transform domain and features.Scale-invariant feature transform algorithm is a feature-based matching algorithm,and is now widely used.It remains invariant to image scale rotation,is also has a high degree of robustness.However,when the algorithm deals with field crop images,there are problems such as insufficient number of feature points,concentration of feature points,and poor matching results.This article focuses on image matching based on SIFT algorithm.The research background of remote sensing image matching and the research status at home and abroad are introduced in detail.Then describes the basics of image processing and the key process and classification of remote sensing image matching.Briefly introduce several common matching algorithms;Secondly,the main ideas of scale space theory and standard SIFT algorithm are introduced,and the algorithm flow chart is constructed.The standard SIFT algorithm implementation principle is introduced in detail: scale space construction and extreme point detection,feature point extraction,feature descriptor generation and feature point matching.Meanwhile aiming at the problem that the standard SIFT algorithm extracted too small feature points and poor stitching effect from field crops remote sensing images.The paper put forward the following improvement:For the problem of crops remote sensing image details serious loss and too few feature points are extracted,the sharpening filter is used to enhance the details of the image.Meanwhile,in order to prevent the image noise from sharpening,the process of image sharpening is reasonably arranged in the preprocessing stage.In order to solve the problem of too concentrated distribution of feature points in field crop remote sensing image,a method based on image scale was proposed to adaptive modification of sampling step length.Which can uniform feature point distribution in two aspects of scale and space.Finally,the standard SIFT algorithm and the optimized SIFT algorithm were compared and verified in feature point extraction,feature point matching,and rotation robustness,respectively used the rice remote sensing image set obtained in 2016.Experimental results show that the optimized algorithm improves the number and quality of feature point extracted when dealing with low contrast field crops,optimized algorithm achieves an ideal image registration effect.