Remote Sensing Recognition and Classification of Forest Vegetation Based on Image Feature Depth Learning

Abstract

In order to study the remote sensing recognition and classification of forest vegetation based on image feature depth learning, this paper presents a deep learning method using classical algorithms such as the maximum class method and maximum entropy method, as well as the FRFCM algorithm and convolutional neural network. In this method, SVM is used to train, classify, and recognize the color information of a high-resolution remote sensing image, remove the nongreen background of the classified image, and finally convert it to HSI space for morphological opening and closing reconstruction, so as to obtain the final extraction target. Then, a visual interface is designed to facilitate operation, which can compare the forest vegetation extraction results and operation processing time under different algorithms, so as to realize the rapid and accurate monitoring of karst forest vegetation change with remote sensing big data. The algorithm research shows that the overall accuracy of multifeature ant colony intelligent classification based on vegetation zoning is 88.85%, Kappa = 0.86, which is better than the traditional remote sensing image classification method, and provides an effective method for land use land cover remote sensing information extraction in large-scale complex terrain areas. In this way, the error extraction and missing extraction can be reduced in the extraction results of forest vegetation area in remote sensing images, and the experimental extraction results will be further close to the optimal segmentation effect.