Scene classification of high-resolution remote sensing (RS) images has attracted increasing attentions due to its vital role in a wide range of applications. Convolutional neural networks (CNNs) have recently been applied on many computer vision tasks and have significantly boosted the performance including imagery scene classification, object detection, and so on. However, the classification performance heavily relies on the features that can accurately represent the scene of images, thus, how to fully explore the feature learning ability of CNNs is of crucial importance for scene classification. Another problem in CNNs is that it requires a large number of labeled samples, which is impractical in RS image processing. To address these problems, a novel sparse representation-based framework for small-sample-size RS scene classification with deep feature fusion is proposed. Specially, multilevel features are first extracted from different layers of CNNs to fully exploit the feature learning ability of CNNs. Note that the existing well-trained CNNs, e.g., AlexNet, VGGNet, and ResNet50, are used for feature extraction, in which no labeled samples is required. Then, sparse representation-based classification is designed to fuse the multilevel features, which is especially effective when only a small number of training samples are available. Experimental results over two benchmark datasets, e.g., UC-Merced and WHU-RS19, demonstrated that the proposed method can effectively fuse different levels of features learned in CNNs, and clearly outperform several state-of-the-art methods especially with limited training samples.

中文翻译：

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使用基于稀疏表示的框架和深度特征融合进行遥感场景分类


高分辨率遥感（RS）图像的场景分类因其在广泛应用中的重要作用而受到越来越多的关注。卷积神经网络（CNN）最近已应用于许多计算机视觉任务，并显着提高了图像场景分类、目标检测等性能。然而，分类性能很大程度上依赖于能够准确表示图像场景的特征，因此，如何充分发挥CNN的特征学习能力对于场景分类至关重要。 CNN 的另一个问题是它需要大量标记样本，这在 RS 图像处理中是不切实际的。为了解决这些问题，提出了一种基于稀疏表示的新型框架，用于具有深度特征融合的小样本 RS 场景分类。特别地，首先从CNN的不同层中提取多级特征，以充分利用CNN的特征学习能力。请注意，现有训练有素的 CNN（例如 AlexNet、VGGNet 和 ResNet50）用于特征提取，其中不需要标记样本。然后，设计基于稀疏表示的分类来融合多级特征，这在只有少量训练样本可用时特别有效。在两个基准数据集（例如 UC-Merced 和 WHU-RS19）上的实验结果表明，所提出的方法可以有效地融合 CNN 中学习到的不同级别的特征，并且明显优于几种最先进的方法，尤其是在训练样本有限的情况下。