用迁移学习提升遥感图像分类准确率，MobileNetV2达96%
Remote Sensing Image Classification Using Convolutional Neural Network (CNN) and Transfer Learning Techniques
- 基于CNN和迁移学习提取特征，使用Softmax分类
- 在10400张图像上，MobileNetV2达到96%准确率、0.119测试损失
- 适合需高精度与高效能的遥感图像分析场景
本研究针对输电塔、森林、农田和山脉的航拍图像分类问题，采用卷积神经网络（CNN）提取特征，并通过Softmax进行分类。模型在包含10,400张图像的混合数据集上训练与评估，该数据集结合了自采的谷歌卫星影像与MLRNet数据集。实验设置为10个训练轮次，批次大小为90，使用Adam优化器，学习率为0.001。结果表明，迁移学习模型表现优异，其中MobileNetV2取得96%准确率和0.119测试损失，优于VGG16的90%准确率和0.298测试损失，验证了其在景观分类任务中的有效性。
原文摘要 · Abstract (English)
This study investigates the classification of aerial images depicting transmission towers, forests, farmland, and mountains. To complete the classification job, features are extracted from input photos using a Convolutional Neural Network (CNN) architecture. Then, the images are classified using Softmax. To test the model, we ran it for ten epochs using a batch size of 90, the Adam optimizer, and a learning rate of 0.001. Both training and assessment are conducted using a dataset that blends self-collected pictures from Google satellite imagery with the MLRNet dataset. The comprehensive dataset comprises 10,400 images. Our study shows that transfer learning models and MobileNetV2 in particular, work well for landscape categorization. These models are good options for practical use because they strike a good mix between precision and efficiency; our approach achieves results with an overall accuracy of 87% on the built CNN model. Furthermore, we reach even higher accuracies by utilizing the pretrained VGG16 and MobileNetV2 models as a starting point for transfer learning. Specifically, VGG16 achieves an accuracy of 90% and a test loss of 0.298, while MobileNetV2 outperforms both models with an accuracy of 96% and a test loss of 0.119; the results demonstrate the effectiveness of employing transfer learning with MobileNetV2 for classifying transmission towers, forests, farmland, and mountains.
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