基于视觉注意机制的大范围水体信息遥感智能提取  

Intelligent extraction of remote sensing information on large-scale water based on visual attention mechanism

作　　者：汪权方[1,2] 张梦茹 张雨 汪倩倩 陈龙跃 杨宇琪 WANG Quanfang;ZHANG Mengru;ZHANG Yu;WANG Qianqian;CHEN Longyue;YANG Yuqi(Faculty of Resources and Environmental Science,Hubei University,Wuhan Hubei 430062,China;Hubei Provincial Engineering Research Center for Remote Sensing Technology in Agriculture(Hubei University),Wuhan Hubei 430062,China)

机构地区：[1]湖北大学资源环境学院,武汉430062 [2]湖北省农业遥感应用工程技术研究中心(湖北大学),武汉430062

出　　处：《计算机应用》2020年第4期1038-1044,共7页journal of Computer Applications

基　　金：湖北省技术创新重大项目(2018ABA078)。

摘　　要：建立契合遥感数据内在特征的智能信息分析模型与方法,是解决遥感大数据时代信息智能提取的关键所在。从普适性的大范围水体信息遥感智能采集的需求出发,构建一种基于视觉选择性注意机制与AdaBoost算法的水体信息遥感智能提取方法。首先通过对遥感多特征指数的RGB配色方案的优化设计,实现水体信息图像特征的增强和可视化表达。然后在HSV颜色空间中,利用色差距离图像的关键节点信息构造分类特征集,并采用AdaBoost算法构建水体识别分类器,据此从图像色彩聚类结果中自动识别出水体所属类别,实现水体信息的智能提取。对比实验结果表明,该方法的水体信息提取结果在漏分率(LR)和复合分类精度(CCA)上都有明显提高;同时,该方法能有效减少对高质量训练样本的依赖性,对于丰水期泥沙含量较高水体以及洪灾导致的淹没区等临时性水域也具有较好的识别性能。In order to solve the intelligence extraction of information in the era of remote sensing big data,it is important to build the model and method of intelligent information analysis fitting the intrinsic characteristics of remote sensing data.To meet the demand of universal remote sensing intelligent acquisition of large-scale water information,an intelligent extraction method of remote sensing water information based on visual selective attention mechanism and AdaBoost algorithm was proposed.Firstly,by the optimization design of RGB color scheme of remote sensing multi-feature index,the enhancement and visual representation of the water information image features were realized.Then,in HSV color space,the key node information of the chromatic aberration distance image was used to construct the classification feature set,and AdaBoost algorithm was used to construct the water recognition classifier.On this basis,the category that the water belongs to was automatically recognized from the image color clustering result,so as to realize the intelligent extraction of water information.Experimental results show that the proposed method has the water information extraction results improved on Leak Rate(LR)and Composite Classification Accuracy(CCA).At the same time,the proposed method not only effectively reduces the dependence on high quality training samples,but also has good performance on the recognition of temporary water areas such as water with high sediment concentration at wet season and submerged area caused by flooding.

关 键 词：视觉注意机制 ADABOOST算法 水体 色差 遥感信息智能提取 

分 类 号：TP75[自动化与计算机技术—检测技术与自动化装置] TP391.41[自动化与计算机技术—控制科学与工程]