Abstract:

【Objective】 Vegetation detection is an important aspect of urban ecological research. However, because of shadowed areas, occluded areas and color distortion of vegetation in remote sensing images, the current vegetation detection accuracy is low. The purpose of this study is to efficiently and effectively measure the vegetation area in a city based on remote sensing satellite images and deep learning technology to provide algorithms for relevant research, such as vegetation resource statistics. 【Method】 Initially, a deep convolution neural network (CNN) model was used to detect vegetation areas in high-resolution remote sensing images. Subsequently, different optimizers were analyzed and discussed, and the accuracy was compared by setting different convolution kernel sizes. Finally, the number of network layers was studied, the appropriate number of network layers was analyzed, and the present deep convolution neural network was used to detect the vegetation area in the experimental data. 【Result】 Experimental data were obtained from high-resolution remote sensing images of the Zijin Mountain area. The vegetation resources in the area were analyzed using the designed multilayer convolution neural network model, and different optimizers, convolution cores and network layers were compared and studied. The vegetation detection accuracy reached 95.4%, which is significantly higher than that of many current vegetation detection algorithms. 【Conclusion】 It can be concluded that the accuracy of the target detection depends on the structure of the convolutional neural network. By setting the optimizer, convolution kernel and number of network layers, the efficiency and accuracy of target detection can be significantly improved.

Key words: vegetation detection, deep learning, convolutional neural network, image classification