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An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification

机译：一种高效轻量级的卷积神经网络用于遥感影像场景分类

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摘要

Classifying remote sensing images is vital for interpreting image content. Presently, remote sensing image scene classification methods using convolutional neural networks have drawbacks, including excessive parameters and heavy calculation costs. More efficient and lightweight CNNs have fewer parameters and calculations, but their classification performance is generally weaker. We propose a more efficient and lightweight convolutional neural network method to improve classification accuracy with a small training dataset. Inspired by fine-grained visual recognition, this study introduces a bilinear convolutional neural network model for scene classification. First, the lightweight convolutional neural network, MobileNetv2, is used to extract deep and abstract image features. Each feature is then transformed into two features with two different convolutional layers. The transformed features are subjected to Hadamard product operation to obtain an enhanced bilinear feature. Finally, the bilinear feature after pooling and normalization is used for classification. Experiments are performed on three widely used datasets: UC Merced, AID, and NWPU-RESISC45. Compared with other state-of-art methods, the proposed method has fewer parameters and calculations, while achieving higher accuracy. By including feature fusion with bilinear pooling, performance and accuracy for remote scene classification can greatly improve. This could be applied to any remote sensing image classification task.
机译：对遥感影像进行分类对于解释影像内容至关重要。当前，使用卷积神经网络的遥感图像场景分类方法存在缺陷，包括参数过多和计算成本高。效率更高，重量更轻的CNN具有更少的参数和计算，但其分类性能通常较弱。我们提出了一种更有效，更轻量级的卷积神经网络方法，以通过小的训练数据集提高分类精度。受细粒度视觉识别的启发，本研究引入了用于场景分类的双线性卷积神经网络模型。首先，轻量级的卷积神经网络MobileNetv2用于提取深层和抽象图像特征。然后将每个特征转换为具有两个不同卷积层的两个特征。对变换后的特征进行Hadamard乘积运算，以获得增强的双线性特征。最后，将合并和归一化后的双线性特征用于分类。在三个广泛使用的数据集上进行了实验：UC Merced，AID和NWPU-RESISC45。与其他最新方法相比，该方法具有更少的参数和计算量，同时实现了更高的精度。通过将特征融合与双线性池一起使用，可以大大提高远程场景分类的性能和准确性。这可以应用于任何遥感图像分类任务。

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