ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

Tree species information is of great significance to forestry resource monitoring and management, timely and accurate control of tree species and growth status is the basis for protection forest project construction and benefit evaluation. In order to study the effect of using UAV hyperspectral images to classify protection forest tree species, the advantages of UAV hyperspectral images high-resolution, multi-band, and short-period were used, taking the “Three North” protection forest which on the northern edge of the 150th Regiment of the Eighth Division of Xinjiang Production and Construction Corps as the research area, typical areas were selected and Matrice600 hexarotor drones equipped with Rikola hyperspectral imaging senstor was used to obtain hyperspectral images. Firstly, spectral features, textural features, vegetation indices (VIs) and characteristics of mathematical statics were extracted from the UAV hyperspectral image, the support vector machine-recusive feature elimination (SVM-RFE) algorithm were used selection bands. Through the random forest algorithm to evaluate the importance of all features and combination with the overall classification accuracies was employed for feature reduction, and then four classification schemes of hyperspectral image full band, the best combination of original band, all feature variables, and feature variables based on random forest (RF) feature reduction were constructed. The classification results showed that the original band combination selected by the SVM-RFE algorithm based on crossvalidation proposed can better restore the original spectral features;when considering the four features (spectral features, textural features, hyperspectral Vis and mathematical statistics features) and after feature reduction, the three classifiers used, random forest (RF), maximum likehood classification (MLC) and support vector machine (SVM), the overall classification accuracies of RF was the highest, which were 95.93%, respectively. These results also suggested that vegetation indices were effective for discriminating tree species with similar spectral signatures. The overall results provided evidence for the effectiveness and potential of UAV hyperspectral data for protection forest tree species identification.