SqueezeNet with Attention for Remote Sensing Scene Classification

Asmaa S. Alswayed, H. Alhichri, Y. Bazi
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引用次数: 5

Abstract

Scene classification is an important problem in remote sensing (RS) since it is a prerequisite to other more intelligent analysis operations. Given an RS scene, not all of its parts are important for classification. Thus, using an attention mechanism that directs the classification system to focus on the parts that are important and ignore the irrelevant background should enhance the system’s accuracy. In this work we propose a deep CNN architecture based on the pre-trained SqueezeNet CNN. This CNN is composed of nine fire modules (fire 1 to fire 9) each consisting of Squeeze followed by expansion convolution layers. First, we improve the SqueezeNet CNN by introducing several modifications to the architecture. Then we introduce a separate branch to the network that implements an attention mechanism. Each neuron in this activation map of the fire 9 module covers a different receptive field in the original scene. An attention mechanism is applied to these neurons to learn the appropriate weighing scheme for merging the feature vectors corresponding to each neuron. Feature vectors that are assigned a higher weight indicate that the network has given more attention to the receptive field in the scene corresponding to that feature vector. Preliminary results are presenting on five popular scene datasets, namely UC Merced, KSA, AID, Whurs19, and Optimal31 datasets.
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基于关注的挤压网遥感场景分类
场景分类是遥感中的一个重要问题,是其他智能分析操作的前提。给定一个RS场景,并不是它的所有部分都对分类很重要。因此,使用一种注意力机制,引导分类系统关注重要的部分,而忽略无关的背景,应该可以提高系统的准确性。在这项工作中,我们提出了一个基于预训练的SqueezeNet CNN的深度CNN架构。这个CNN由9个模块(fire 1到fire 9)组成,每个模块由Squeeze组成,然后是扩展卷积层。首先,我们通过对结构进行一些修改来改进SqueezeNet CNN。然后,我们在网络中引入了一个独立的分支,该分支实现了注意力机制。第9个模块的激活图中的每个神经元覆盖了原始场景中不同的接受野。将注意机制应用于这些神经元,以学习适当的加权方案来合并每个神经元对应的特征向量。被赋予更高权重的特征向量表明网络更加关注与该特征向量对应的场景中的接受域。初步结果展示了五种流行的场景数据集,即UC Merced, KSA, AID, Whurs19和Optimal31数据集。
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