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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Remote Sensing Scene Classification Using Convolutional Features and Deep Forest Classifier
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Remote Sensing Scene Classification Using Convolutional Features and Deep Forest Classifier

机译：基于卷积特征和深林分类器的遥感场景分类

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摘要

High-resolution remote sensing scene classification (HR-RSSC) plays an increasingly important role since it aims to enhance the scene semantic understanding. Recently, convolutional neural networks (CNNs) proved their effectiveness in learning powerful feature representations for various visual recognition tasks. However, in the RS domain, the performance of CNN is still limited due to the lack of sufficient labeled data. In this letter, we propose an HR-RSSC method based on CNN transfer learning (TL) for feature extraction (FE) and deep forest (DF) for classification. In fact, we extract deep features from the last convolutional layer in order to avoid the use of the fully connected layers (FCLs) which need many parameters to tune. Moreover, we train a DF model that is based on ensemble learning that can achieve better performances than single classifiers and is easy to train with few parameters. We evaluate the proposed method on two RS image. Compared to full-training, fine-tuning, and state-of-the-art CNN TL methods, the results demonstrate the effectiveness of the DF model for HR-RSSC based on CNN TL in terms of overall accuracy and training time.
机译：高分辨率遥感场景分类（HR-RSSC）起着越来越重要的作用，因为它旨在增强场景的语义理解。最近，卷积神经网络（CNN）证明了其在学习各种视觉识别任务的强大特征表示方面的有效性。但是，在RS域中，由于缺少足够的标记数据，CNN的性能仍然受到限制。在这封信中，我们提出了一种基于CNN传递学习（TL）进行特征提取（FE）和深林（DF）进行分类的HR-RSSC方法。实际上，我们从最后一个卷积层中提取深度特征，以避免使用需要许多参数进行调整的完全连接层（FCL）。此外，我们训练了基于集合学习的DF模型，该模型可以比单个分类器实现更好的性能，并且易于训练且参数很少。我们在两个RS图像上评估提出的方法。与全面训练，微调和最新的CNN TL方法相比，结果证明了基于CNN TL的HR-RSSC DF模型在整体准确性和训练时间方面的有效性。

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