文件名称:fenlei
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利用深度学习进行遥感图像场景分类
这里我们对NWPU-RESISC45数据集的场景图像进行分类
我们将卷积神经网络应用于图像分类。我们从头开始训练数据集。此外，还应用了预先训练的VGG16 abd ResNet50进行迁移学习。(Scene Classification of Remote Sensing Images Using Deep Learning
Here we classify scene images from NWPU-RESISC45 dataset
We apply convolutional neural network to image classification. We start training data sets from scratch. In addition, a pre-trained VGG16 abd ResNet50 is used for migration learning.)
这里我们对NWPU-RESISC45数据集的场景图像进行分类
我们将卷积神经网络应用于图像分类。我们从头开始训练数据集。此外，还应用了预先训练的VGG16 abd ResNet50进行迁移学习。(Scene Classification of Remote Sensing Images Using Deep Learning
Here we classify scene images from NWPU-RESISC45 dataset
We apply convolutional neural network to image classification. We start training data sets from scratch. In addition, a pre-trained VGG16 abd ResNet50 is used for migration learning.)
相关搜索： 遥感图像 Python 分类
(系统自动生成,下载前可以参看下载内容)
下载文件列表
| 文件名 | 大小 | 更新时间 |
|---|---|---|
| A-System-for-Effecient-Remote-Sensing-Image-Scene-Classification--master\imagenet_utils.py | 1566 | 2017-12-25 |
| A-System-for-Effecient-Remote-Sensing-Image-Scene-Classification--master\README.md | 718 | 2017-12-25 |
| A-System-for-Effecient-Remote-Sensing-Image-Scene-Classification--master\resnet50.py | 12097 | 2017-12-25 |
| A-System-for-Effecient-Remote-Sensing-Image-Scene-Classification--master\resnet50_Transfer_Learning.py | 5620 | 2017-12-25 |
| A-System-for-Effecient-Remote-Sensing-Image-Scene-Classification--master\training_from_scratch.py | 15886 | 2017-12-25 |
| A-System-for-Effecient-Remote-Sensing-Image-Scene-Classification--master\vgg16.py | 8740 | 2017-12-25 |
| A-System-for-Effecient-Remote-Sensing-Image-Scene-Classification--master\vgg16_Transfer_Learning.py | 5349 | 2017-12-25 |
| A-System-for-Effecient-Remote-Sensing-Image-Scene-Classification--master | 0 | 2017-12-25 |
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