Classification of Ecological Garden Scenes Using Deep Learning in Remote Sensing Images

Abstract

Ecological garden is one of the important links of urban ecosystem. Accurate and effective analysis of temporal and spatial changes of garden vegetation is conducive to the sustainable development of urban resources and environment. However, the elements of garden vegetation are close to each other, and the current analysis methods can’t support the remote sensing image analysis in the ecological garden scene. To solve this problem, this paper introduces a new method for recognition and classification based on deep learning networks. Firstly, radiometric correction and geometric correction are used to preprocess the data collected by unmanned aerial vehicle (UAV); the ecological garden remote sensing image analysis network is constructed based on three-dimensional convolutional neural network (3D-CNN), which adopts double convolution pool structure, To address the issue of pixel misclassification due to similar features in landscape vegetation classification, a deep learning network can be used for high-dimensional feature extraction. On this basis, the multiple label conditional random field (MLCRF) algorithm can be applied for global optimization, effectively solving the misclassification problem caused by similar features of vegetation. This method not only improves classification accuracy but also is suitable for large-scale image classification tasks in complex scenes. The simulation results show that the overall recognition accuracy of the proposed 3D-CNN-MLCRF identification method is 97.83% and the Kappa coefficient is 97.18%, which can accurately and effectively realize the task of ecological garden scene classification.