研究目的
To analyze the use of split-brain autoencoders in the context of remote sensing image classification, investigating the importance of training set size, choice of color space, and size of the model to the classification accuracy.
研究成果
The split-brain autoencoder can be successfully applied to remote sensing image classification, achieving near state-of-the-art results with fine-tuning. Larger datasets and deeper models improve classification accuracy, and the method is adaptable to different color spaces.
研究不足
The study is limited by the size of the training dataset and the choice of color space. The potential for improvement with larger datasets and deeper models is acknowledged but not fully explored.
1：Experimental Design and Method Selection:
The study uses split-brain autoencoders for self-supervised learning in remote sensing image classification. The methodology includes training autoencoders on unlabeled datasets and fine-tuning for classification tasks.
2：Sample Selection and Data Sources:
The NWPU-RESISC45 dataset is used for training the autoencoders, and the AID dataset is used for classification tasks.
3：List of Experimental Equipment and Materials:
The study utilizes deep learning models based on AlexNet and VGG16 architectures.
4：Experimental Procedures and Operational Workflow:
The autoencoders are trained using ADAM optimizer with specific learning rates and stopping criteria. Features are extracted using global average pooling and classified using SVM with RBF kernel or fine-tuned CNNs.
5：Data Analysis Methods:
The study evaluates classification accuracy across different training set sizes, color spaces, and model architectures.
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