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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Transfer Learning With Fully Pretrained Deep Convolution Networks for Land-Use Classification
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Transfer Learning With Fully Pretrained Deep Convolution Networks for Land-Use Classification

机译：完全预先训练的深度卷积网络用于土地利用分类的转移学习

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摘要

In recent years, transfer learning with pretrained convolutional networks (CNets) has been successfully applied to land-use classification with high spatial resolution (HSR) imagery. The commonly used transfer CNets partially use the feature descriptor part of the pretained CNets, and replace the classifier part of the pretrained CNets in the old task with a new one. This causes the separation and asynchrony between the feature descriptor part and the classifier part of the transferred CNets during the learning process, which reduces the effectiveness of the training process. To overcome this weakness, a transfer learning method with fully pretrained CNets is proposed in this letter for the land-use classification of HSR images. In the proposed method, a multilayer perceptron (MLP) classifier is quickly pretrained using the high-level features extracted by the feature descriptor of the pretrained CNets. Fully pretrained CNets can be generated by concatenating the feature descriptor of the pretrained CNets and the pretained MLP. Because both the feature descriptor and the classifier are pretrained, the separation and asynchrony between the two parts can be avoided during the training process. The final transferred CNets are then obtained by fine-tuning the fully pretrained CNets with the random cropping and mirroring strategy. The experiments show that the proposed method can accelerate the convergence of the training process with no loss of accuracy in land-use classification, and its performance is comparable to other latest methods.
机译：近年来，具有预训练卷积网络（CNets）的转移学习已成功应用于具有高空间分辨率（HSR）图像的土地利用分类。常用的传输CNets部分使用保留的CNets的特征描述符部分，并用新任务替换旧任务中预训练的CNets的分类器部分。在学习过程中，这导致了传输的CNets的特征描述符部分和分类器部分之间的分离和异步，从而降低了训练过程的有效性。为了克服这一弱点，本文提出了一种具有完全预训练的CNets的转移学习方法，用于HSR图像的土地利用分类。在所提出的方法中，使用预训练的CNets的特征描述符提取的高级特征来快速预训练多层感知器（MLP）分类器。可以通过将预训练的CNets的特征描述符和保留的MLP串联来生成完全预训练的CNets。由于特征描述符和分类器都经过预训练，因此在训练过程中可以避免两个部分之间的分离和异步。然后，通过使用随机裁剪和镜像策略对经过完全预训练的CNets进行微调，可以获得最终传输的CNets。实验表明，该方法可以加快训练过程的收敛速度，并且不会影响土地利用分类的准确性，其性能可与其他最新方法相媲美。

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