IEEE Trans Image Process. 2022;31:99-109. doi: 10.1109/TIP.2021.3127851. Epub 2021 Nov 30.
Remote sensing scene classification (RSSC) is a hotspot and play very important role in the field of remote sensing image interpretation in recent years. With the recent development of the convolutional neural networks, a significant breakthrough has been made in the classification of remote sensing scenes. Many objects form complex and diverse scenes through spatial combination and association, which makes it difficult to classify remote sensing image scenes. The problem of insufficient differentiation of feature representations extracted by Convolutional Neural Networks (CNNs) still exists, which is mainly due to the characteristics of similarity for inter-class images and diversity for intra-class images. In this paper, we propose a remote sensing image scene classification method via Multi-Branch Local Attention Network (MBLANet), where Convolutional Local Attention Module (CLAM) is embedded into all down-sampling blocks and residual blocks of ResNet backbone. CLAM contains two submodules, Convolutional Channel Attention Module (CCAM) and Local Spatial Attention Module (LSAM). The two submodules are placed in parallel to obtain both channel and spatial attentions, which helps to emphasize the main target in the complex background and improve the ability of feature representation. Extensive experiments on three benchmark datasets show that our method is better than state-of-the-art methods.
遥感场景分类（RSSC）是近年来遥感图像解译领域的热点，起着非常重要的作用。随着卷积神经网络的最新发展，在遥感场景分类方面取得了重大突破。许多物体通过空间组合和关联形成复杂多样的场景，这使得遥感图像场景的分类变得困难。卷积神经网络（CNNs）提取的特征表示的区分度不足的问题仍然存在，这主要是由于类间图像的相似性特征和类内图像的多样性特征造成的。在本文中，我们提出了一种基于多分支局部注意网络（MBLANet）的遥感图像场景分类方法，在 ResNet 骨干网络的所有下采样块和残差块中嵌入卷积局部注意模块（CLAM）。CLAM 包含两个子模块，卷积通道注意模块（CCAM）和局部空间注意模块（LSAM）。这两个子模块并行放置，以获得通道和空间注意力，有助于突出复杂背景中的主要目标，并提高特征表示能力。在三个基准数据集上的广泛实验表明，我们的方法优于最先进的方法。
