特征提取策略对高分辨率遥感图像场景分类性能影响的评估  

Evaluation of the effect of feature extraction strategy on the performance of high-resolution remote sensing image scene classification

作　　者：钱晓亮[1] 李佳 程塨[2] 姚西文[2] 赵素娜 陈宜滨 姜利英[1] QIAN Xiaoliang;LI Jia;CHENG Gong;YAO Xiwen;ZHAO Suna;CHEN Yibin;JIANG Liying(College of Electric and Information Engineering,Zhengzhou University of Light Industry,Zhengzhou 450002,China;College of Automation,Northwestern Polytechnical University,Xi＇an 710072,China)

机构地区：[1]郑州轻工业学院电气信息工程学院,郑州450002 [2]西北工业大学自动化学院,西安710072

出　　处：《遥感学报》2018年第5期758-776,共19页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:61501407;61772425;61701415);河南省科技创新杰出人才(编号:184200510015);河南省高校科技创新团队(编号:19IRTSTHN013);郑州工业学院博士基金(编号:2014BSJJ016)~~

摘　　要：高分辨率遥感图像场景分类方法主要涉及两个环节:特征提取以及特征分类,分类器的设计已经相对成熟,当前工作的重点是特征提取策略的研究。为了进一步推动特征提取策略的研究,将特征提取策略对高分辨率遥感图像场景分类性能的影响进行了定性和定量评估。首先,回顾了高分辨率遥感图像场景分类的发展历程;然后,对现有高分辨率遥感图像场景分类方法的特征提取策略进行分类总结,并从理论上将各类特征提取策略对场景分类性能的影响进行定性评估;最后,在3个规模较大的数据集上对多种特征提取策略进行实验对比,将不同特征提取策略对场景分类性能的影响和各数据集的复杂度进行定量评估。Remote sensing image scene classification aims to tag remote sensing images with semantic categories according to the content of the image and is important in disaster monitoring, environmental detection, and urban planning. Scene classification results can provide valuable information about object recognition and image retrieval and can effectively improve the performance of image interpretation. The general process of remote sensing image scene classification mainly consists of feature extraction and scene classification based on image features. Given that the design of classifiers is relatively mature, this work focuses on feature extraction strategy. The influence of various strategies on the performance of scene classification is short of unified evaluation, which limits its development. The effect of various feature extraction strategies on the performance of high-resolution remote sensing image scene classification is evaluated in this study. In the second section of this paper, existing feature extraction strategies are divided into two categories：（1） hand-designed and（2） datadriven feature extraction. Hand-designed features, such as Color Histograms（CH） and Scale Invariant Feature Transform（SIFT）, provide the primary description of images and are presented in the early period. Further abstract description of the images is introduced by coding of hand-designed features, such as Bag of Visual Words（BoVW） and has higher classification accuracy than hand-designed features. However,these feature extraction strategies generally suffer from poor generalization capability due to specific requirements for designing. Furthermore, hand-designed features require significant domain knowledge. By contrast, data-driven features can directly learn powerful features from a large number of sample images and are generally divided into shallow and deep learning features. Shallow learning feature extraction mainly involves Principal Component Analysis（PCA）, Independent Component Analysis（ICA）, a

关 键 词：高分辨率 场景分类 特征提取策略 手工特征 数据驱动特征 深度学习 

分 类 号：TP753[自动化与计算机技术—检测技术与自动化装置]