鲁恒,付萧,刘超,李龙国,李乃稳,庄文化.基于低空遥感与迁移学习的土地利用信息快速制图方法[J].农业机械学报,2016,47(11):262-269.
Lu Heng,Fu Xiao,Liu Chao,Li Longguo,Li Naiwen,Zhuang Wenhua.Landuse Information Quick Mapping Based on Low Altitude Remote Sensing Technology and Transfer Learning[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(11):262-269.
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基于低空遥感与迁移学习的土地利用信息快速制图方法   [下载全文]
Landuse Information Quick Mapping Based on Low Altitude Remote Sensing Technology and Transfer Learning   [Download Pdf][in English]
投稿时间:2016-07-15  
DOI:10.6041/j.issn.1000-1298.2016.11.036
中文关键词:  低空遥感技术  土地利用信息  分类制图  不变对象获取  知识迁移学习  先验知识
基金项目:国家自然科学基金青年基金项目(51209153、41301021)、数字制图与国土信息应用工程国家测绘地理信息局重点实验室开放基金项目(DM2014SC02)和国土资源部地学空间信息技术重点实验室开放基金项目(KLGSIT2015-04)
作者单位
鲁恒 四川大学 
付萧 西南交通大学
汉诺威大学 
刘超 四川大学 
李龙国 四川大学 
李乃稳 四川大学 
庄文化 四川大学 
中文摘要:为解决样本的手工获取和常规的目视解译难以适应目前农业土地资源信息自动化提取的需求问题,引入时空数据挖掘技术,运用关联知识迁移学习机制,提出了一种基于知识迁移学习的高分辨遥感影像土地利用信息分类制图方法(KTLC)。首先,运用改进的均值漂移算法对新的待分类制图影像进行分割获得影像对象,然后,将分割后对象的矢量边界与前时相土地利用矢量专题图进行配准、嵌套,通过叠加分析获取当前影像中的不变对象,并通过光谱、空间信息阈值筛选完成不变对象的提纯,进而将历史专题图中的地物类别知识迁移到新影像对象上,建立新的特征与地物类别映射关系,最后,运用决策树构建分类规则完成当前影像的快速分类制图,并将所提方法与利用易康(eCognition)软件进行分类(EC)的结果进行对比。研究结果表明,对于2组实验影像,KTLC方法分类总体精度分别为88.61%、88.30%,EC方法分类的总体精度分别为89.87%、84.84%,2种方法分类制图精度相当,但在效率方面,KTLC方法优于EC方法。
Lu Heng  Fu Xiao  Liu Chao  Li Longguo  Li Naiwen  Zhuang Wenhua
Sichuan University,Southwest Jiaotong University; University of Hannover,Sichuan University,Sichuan University,Sichuan University and Sichuan University
Key Words:low altitude remote sensing technology  landuse information  classification mapping  invariant objects acquisition  knowledge transfer learning  prior knowledge
Abstract:Obtaining surface spatio temporal data rapidly, automatically and accurately is an important issue in agriculture informationization and intellectualization. Samples obtained by manual and conventional manual visual interpretation are difficult to adapt the demands of current agricultural land resources information automatic extraction. At the same time, low altitude remote sensing technology as a kind of emerging technology for earth observation in recent years, with its flexibility, high efficiency, low cost, was widely used in the investigation of all kinds of resources. If only extraction information from single phase image, regardless of the historical image data set information extraction has been completed, it will cause information waste and repeated work. Based on this, spatio temporal data mining technology was introduced, and related knowledge transfer learning mechanism was used, a novel landuse information classification method based on knowledge transfer learning (KTLC) was proposed. Firstly, new image was segmented by improved mean shift algorithm to obtain image objects. Secondly, the vector boundary of the objects and former historical landuse thematic map were matched and nested, invariant objects were obtained through overlay analysis, and purification of invariant object was finished by spectral and spatial information threshold filtering. The historical features category knowledge of thematic map was transferred to the new image objects. Finally, current images classification mapping was completed based on decision tree, and landuse classification mapping results were completed by the KTLC and eCognition for landuse information mapping classification (EC). The experimental results showed that KTLC could obtain accuracies equivalent to EC, and also outperforms EC in terms of efficiency.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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