Classification Of Forest Stand Types Based On Multi-Source And Multi-Temporal Remote Sensing Data

As the most important terrestrial ecosystem,forests have played a huge role in ecological balance,economic construction and water conservation.Therefore,the management and monitoring of forest resources is of great significance.The traditional forest resources survey is expensive,difficult and low in efficiency,while remote sensing technology has greatly improved the efficiency of forest resources survey and provided new opportunities for forest type identification.However,remote sensing classification is affected by factors such as data sources,feature features,and classification methods.Therefore,it is necessary to explore data source selection,feature extraction and optimization,and classification models.This paper takes Mengjiagang Forest Farm in Heilongjiang Province as the main research area,and uses multi-temporal Sentinel-1 SAR,Sentinel-2 and GF-1 images as remote sensing data sources,integrates digital elevation model,CCD image,forest management inventory data and field survey data,constructs the classification system in the forest farm,and extracts multi temporal spectral features,vegetation index,radar features,texture features and terrain factors combined with the phenological characteristics of forest types.Then analyze and optimize the features that are conducive to distinguishing various categories,construct a variety of classification feature combinations,and explore the classification effect of forest stand types under multi-source and multi temporal data and the influence of optimization variables under different feature optimization algorithms on classification accuracy.At the same time,further explore the classification effect and applicability of deep learning methods(U-Net,SegNet,DeepLab V3+)in forest stand types.The main research contents and conclusions are as follows:(1)Classification of stand types from multi-source remote sensing images.By classifying all the features of the multi-temporal Sentinel-1/2 images and GF-1 images separately,using the random forest method to conduct classification experiments,it is found that the time accuracy of synthesizing all features of sentinel-1/2 image is the highest,which is 82.88%;When itegrating all features of GF-1 image,the highest accuracy is 80.83%.The user accuracy and producer accuracy of each category reach the maximum,and the misclassification and misclassification in the confusion matrix are reduced.It can be seen that the Sentinel-1/2 image has higher accuracy,which is 2.05%higher than that of the GF-1 image,indicating that the rich spectral information is more conducive to image classification.Combining all the features of Sentinel-1 SAR image,Sentinel-2 image and GF-1 image,the classification accuracy reaches 83.33%,which is higher than that of using a single data source.Therefore,integrating multi-source image data is beneficial to improve the classification accuracy.(2)In view of the possible redundant variables in multi feature images,four feature optimization algorithms of VSURF,Boruta,RFE and varselRF are used for variable screening and random forest method classification for all the features of sentinel-1/2 image,all the features of GF-1 image and all the features of sentinel-1/2 and GF-1 image.The results show that varSelRF method has the best effect on optimizing variables,which can reduce redundant variables and irrelevant variables and improve the accuracy of the model.Through variable optimization,the classification accuracy of the latter three schemes is improved by 0.32%,0.42%and 0.92%respectively,indicating that feature optimization can avoid variable redundancy and improve the efficiency of the model.(3)Classification of image forest stand types with different spatial resolutions based on U-Net model.The maximum likelihood method,support vector machine,decision tree,random forest and U-Net model are used to classify the two schemes of Sentinel-2 image spectral feature+DEM and GF-1 image spectral feature+DEM respectively.The results show that the accuracy of the U-Net model in the two schemes is significantly better than other classification methods;the support vector machine is the second,and the decision tree Kyoto is the lowest.At the same time,the accuracy of the U-Net model based on Sentinel-2 image is 4.5%higher than that of GF-1 image,indicating that the U-Net model can learn the rich multiband features of the image,thereby improving the accuracy.(4)Multi-source image forest stand types classification based on deep learning method.By combining the spectral characteristics of multi temporal sentinel-2 image and GF-1 image and DEM data,three deep learning methods U-Net,SegNet and DeepLab V3+model are used to classify forest stand types,and compared with maximum likelihood method and random forest.The results show that the three deep learning methods have higher classification accuracy than the traditional machine learning.Among them,the accuracy of U-Net model is the highest,with an overall accuracy of 86.08%and a kappa coefficient of 0.8163,followed by DeepLab V3+and SegNet models.Among the traditional machine learning methods,the accuracy of maximum likelihood method is 80.55%,which is 3.37%higher than that of random forest classification.The deep learning model can automatically learn and mine the deep feature information of the image,reduce the salt and pepper noise,effectively improve the classification accuracy,and provide a reference for the subsequent research on the classification of forest stand types.