Research On Land Cover Segmentation Algorithms Based On UAV High-resolution Remote Sensing Imagery

UAV remote sensing has the characteristics of high spatial resolution,strong real-time performance and convenient payloads replacement.It can effectively make up for the problems of insufficient spatial resolution,long acquisition cycle and limited fixed payloads of satellite remote sensing.Land cover segmentation algorithm of high-resolution remote sensing imagery can play an important role in precision agriculture,urban planning,land use and other fields,and greatly save labor cost.It has become one of the hot topics in the field of remote sensing in recent years.After summarizing the research on land cover segmentation in the fields of remote sensing and artificial intelligence at home and abroad,this paper further studies the problems of salt and pepper noise,insufficient perception and few samples existing in the process of land cover segmentation of UAV high-resolution remote sensing imagery.The main research work and innovative achievements of this paper are as follows:(1)Research on object-based machine learning land cover segmentation algorithms.This study uses the images collected by UAV equipped with multispectral camera in the experimental farmland in Dongliao district as the data source to segment the four types of land covers(hemp,corn,rose,river)in this area.The experiment compares the effect of object-based machine learning algorithms and traditional machine learning algorithms under the influence of salt and pepper noise in high-resolution imagery.The experimental results show that the object-based machine learning algorithm effectively weakens the influence of salt and pepper effect in land cover segmentation.Among them,the overall accuracy of object-based random forest machine learning algorithm can reach 92.24%,and the kappa coefficient can reach89.04%.(2)Research on deep learning land cover segmentation algorithms based on dimension interactive attention mechanism.In this study,the images collected by UAV equipped with visible light camera in Jilin University campus are used as the data source to segment the three types of land covers(vegetation,buildings,the others)in this area.The DIA-U-Net deep learning model combining dimension interactive attention module and U-Net semantic segmentation model is proposed,and the feature perception ability of U-Net under various attention modules is compared.The experimental results show that the feature perception of the deep learning land cover segmentation algorithm DIA-U-Net based on dimensional interactive attention mechanism is more sufficient,which effectively improves the overall effect of land cover segmentation.Among them,the overall accuracy of the deeper DIA-U-Net deep learning algorithm can reach 91.37%,and the kappa coefficient can reach 86.50%.(3)Research on object-based active learning land cover segmentation algorithms based on dimension interactive attention mechanism.This study uses the public UAV hyperspectral dataset named WHU-Hi as the data source.The dataset contains 9,22 and 16 types of land covers in Longkou,Honghu and Hanchuan areas of Hubei Province,and the training samples account for 0.1%-0.15% of the total labeled samples.The experiment adopts an object-based active learning algorithm based on dimension interactive attention mechanism to solve the problem of few samples.The object-based method is introduced into the sample selection strategy part to select the most representative samples.The dimension interactive attention module is introduced into the active learning model part to improve the feature perception ability of the model.The experimental results show that the object-based active learning algorithm based on dimension interactive attention mechanism effectively solves the problem of few samples.Among them,the OA,AA and kappa of active learning algorithm with multiple rounds can reach more than 98%.In this paper,the UAV high-resolution remote sensing imagery is used as the data source.The object-based machine learning algorithm,the deep learning algorithm based on dimensional interactive attention mechanism and the object-based active learning algorithm based on dimensional interactive attention mechanism are adopted to gradually solve the problems of salt and pepper noise,insufficient perception and few samples in multispectral,visible and hyperspectral images.Finally,a comprehensive and complete land cover segmentation algorithm system is formed,which provides the reference and idea for the related research of land cover segmentation of UAV high-resolution remote sensing imagery in the future.