This paper uses an improved deep learning algorithm to judge the rationality of the design of landscape image feature recognition. The preprocessing of the image is proposed to enhance the data. The deficiencies in landscape feature extraction are further addressed based on the new model. Then, the two-stage training method of the model is used to solve the problems of long training time and convergence difficulties in deep learning. Innovative methods for zoning and segmentation training of landscape pattern features are proposed, which makes model training faster and generates more creative landscape patterns. Because of the impact of too many types of landscape elements in landscape images, traditional convolutional neural networks can no longer effectively solve this problem. On this basis, a fully convolutional neural network model is designed to perform semantic segmentation of landscape elements in landscape images. Through the method of deconvolution, the pixel-level semantic segmentation is realized. Compared with the 65% accuracy rate of the convolutional neural network, the fully convolutional neural network has an accuracy rate of 90.3% for the recognition of landscape elements. The method is effective, accurate, and intelligent for the classification of landscape element design, which better improves the accuracy of classification, greatly reduces the cost of landscape element design classification, and ensures that the technical method is feasible. This paper classifies landscape behavior based on this model for full convolutional neural network landscape images and demonstrates the effectiveness of using the model. In terms of landscape image processing, the image evaluation provides a certain basis.

中文翻译：

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基于景观规划设计合理性的深度学习图像特征识别算法

本文使用一种改进的深度学习算法来判断景观特征识别设计的合理性。建议对图像进行预处理以增强数据。基于新模型，可以进一步解决景观特征提取中的不足。然后，该模型的两阶段训练方法被用来解决训练时间长和深度学习收敛困难的问题。提出了对景观格局特征进行分区和分割训练的创新方法，可以使模型训练更快，并产生更多创造性的景观格局。由于景观图像中太多类型的景观元素的影响，传统的卷积神经网络无法再有效地解决这一问题。以这个为基础，设计了一个全卷积神经网络模型来对风景图像中的风景元素进行语义分割。通过反卷积的方法，实现了像素级的语义分割。与卷积神经网络的65％的准确率相比，全卷积神经网络对景观元素的识别的准确率为90.3％。该方法有效，准确，智能地进行了景观要素设计的分类，更好地提高了分类的准确性，大大降低了景观要素设计分类的成本，确保了该技术方法的可行性。本文基于该模型对全卷积神经网络景观图像进行景观行为分类，并证明了使用该模型的有效性。