基于增强随机初始化卷积神经网络的无人机柱状仙人掌识别
An Enhanced Randomly Initialized Convolutional Neural Network for Columnar Cactus Recognition in Unmanned Aerial Vehicle Imagery
摘要：
近年来，卷积神经网络（CNN）在遥感图像分类中取得了很好的效果。基于CNN的植物识别是当前深度学习的研究热点之一，因为它在不同的相关领域，特别是环境保护和自然保护领域具有重要的应用价值。自动识别保护区内的植物有助于这些区域的监测过程，并确保其生态系统的可持续性。在这项工作中，我们提出了一个增强的随机初始化卷积神经网络（ERI-CNN）的柱状仙人掌，这是一种特有的植物，存在于墨西哥东南部的特瓦坎Cuicatlán山谷的识别。我们使用了由一组研究人员创建的公共数据集，该数据集由20000多张遥感图像组成。实验结果证实了该模型的有效性相比，在文献中报道的其他模型，如InceptionV 3和修改后的LeNet-5 CNN。我们的ERI-CNN提供了98%的准确率，97%的精确率，97%的召回率，97.5%的f1分数和0.056的损失。
英文摘要：
Recently, Convolutional Neural Networks (CNNs) have made a great performance for remote sensing image classification. Plant recognition using CNNs is one of the active deep learning research topics due to its added-value in different related fields, especially environmental conservation and natural areas preservation. Automatic recognition of plants in protected areas helps in the surveillance process of these zones and ensures the sustainability of their ecosystems. In this work, we propose an Enhanced Randomly Initialized Convolutional Neural Network (ERI-CNN) for the recognition of columnar cactus, which is an endemic plant that exists in the Tehuacán-Cuicatlán Valley in southeastern Mexico. We used a public dataset created by a group of researchers that consists of more than 20000 remote sensing images. The experimental results confirm the effectiveness of the proposed model compared to other models reported in the literature like InceptionV3 and the modified LeNet-5 CNN. Our ERI-CNN provides 98% of accuracy, 97% of precision, 97% of recall, 97.5% as f1-score, and 0.056 loss.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
