Remote sensing scene classification is still a challenging task. Extracting features effectively from restricted existing labeled data is key to scene classification. Convolutional neural networks (CNNs) are an effective method of constructing discriminating feature representation. However, CNNs usually utilize the feature map from the last layer and ignore additional layers with valuable feature information. In addition, the direct integration of multiple layers brings only a small improvement due to feature redundancy and destruction. To explore the potential information from additional layers and improve the effect of feature fusion, we propose multilayer feature fusion accesses with weight adjustment based on a CNN. We construct access to deliver additional features to one layer to achieve feature fusion and set weight factors to adjust the fusion degree to reduce feature redundancy and destruction. We perform experiments on two common data sets, which indicate improved accuracies and advantages of the extraction capability of our method.

中文翻译：

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基于卷积神经网络的具有权重调整的多层特征融合用于遥感场景分类

遥感场景分类仍然是一项具有挑战性的任务。从受限制的现有标记数据中有效提取特征是场景分类的关键。卷积神经网络 (CNN) 是一种构建判别特征表示的有效方法。然而，CNN 通常利用最后一层的特征图，而忽略具有有价值特征信息的附加层。此外，由于特征冗余和破坏，多层的直接集成只会带来很小的改进。为了探索来自附加层的潜在信息并提高特征融合的效果，我们提出了基于 CNN 的权重调整多层特征融合访问。我们构造访问以向一层提供附加特征以实现特征融合并设置权重因子以调整融合度以减少特征冗余和破坏。我们对两个常见数据集进行了实验，这表明我们方法的提取能力提高了准确性和优势。