基于多级别跨层双线性融合的光学遥感图像场景分类  

Optical Remote Sensing Image Scene Classification Based on Multi-level Cross-layer Bilinear Fusion

作　　者：余甜微 郑恩让[1] 沈钧戈 王凯 YU Tianwei;ZHENG Enrang;SHEN Junge;WANG Kai(School of Electrical and Control Engineering,Shaanxi University of Science and Technology,Xi′an 710021,China;Unmanned System Research Institute,Northwestern Polytechnical University,Xi′an 710072,China;Henan Key Laboratory of Underwater Intelligent Equipment,Zhengzhou 450000,China)

机构地区：[1]陕西科技大学电气与控制工程学院,西安710021 [2]西北工业大学无人系统技术研究院,西安710072 [3]河南省水下智能装备重点实验室,郑州450000

出　　处：《光子学报》2022年第2期250-263,共14页Acta Photonica Sinica

基　　金：国家自然科学基金(No.61603233)。

摘　　要：针对光学遥感场景图像存在由空间模式复杂、类间相似度大和同类多样性高导致的模型分类准确度受限的问题,提出一种基于多级别跨层双线性融合的光学遥感场景分类算法。首先从ResNet50模型中提取多层次特征信息,将膨胀卷积的扩张率设置为不同数值来提取多个空间尺度下的上下文特征,通过串行融合多尺度特征丰富特征信息的场景语义。为了充分利用低层、高层、全局上下文特征信息的互补优势,提出多级别注意力特征融合模块,有效增强模型的特征提取能力。最后采用跨层双线性融合方法对多级别特征进行分层融合,融合后的特征用于分类。通过在三个公开的遥感数据集UCM、AID和PatternNet上进行广泛试验,验证了所提方法的可行性,与其它先进的场景分类方法相比,该方法实现了更加优异的分类性能。Remote sensing,a kind of detection technology,provides non-contact surface observation through sensor platform.With the rapid development of unmanned aerial vehicle,remote sensing and satellites technology,quantitative remote sensing images with higher resolution can be generated.Compared with medium and low-resolution remote sensing images,these high-resolution remote sensing images contain richer ground objects and spatial details,which can express the spatial structure and texture features of ground object more clearly,providing good conditions and foundation for remote sensing image interpretation and analysis.Therefore,high-resolution remote sensing images have become an important data source for fine earth observation.The scene classification of high-resolution remote sensing image refers to the analysis of extracted remote sensing image information,dividing the scene image of interest to different categories,such as forest,river,railway,etc.,and is widely applied in environmental monitoring,urban planning,military object detection,global climate change research and other fields.Unlike general natural images,the geometry structure and space pattern of remote sensing images are highly complex,and there are also problems such as complex background and many types,which is a great challenge for effectively describing remote sensing image content.In addition,as a result of the complexity and diversity of remote sensing image scenes,different scenes may contain almost the same ground object targets,or the same scene may contain different ground object targets.At this regard,how to design discriminative feature representation to describe the image directly affects the quality of scene classification.In the past few decades,many approaches have been proposed,and most of these methods can be divided into two main categories.The traditional scene classification methods,such as Scale Invariant Feature Transform(SIFT),Histogram of Oriented Gradients(HOG)and Color Histogram(CH),mainly use hand-crafted feature but highly

关 键 词：遥感 场景分类 膨胀卷积 多级别注意力 跨层双线性融合 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]