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Research on intelligent calculation model and method of precision land use/cover change information driven by high-resolution remote sensing

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：luojc@aircas.ac.cn
  • Introduction：骆剑承，1970年生，男，研究员，研究方向为遥感大数据智能计算与地学分析。E-mail：luojc@aircas.ac.cn
LUO Jiancheng12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：mydonne@163.com
  • Introduction：胡晓东，1982年生，男，助理研究员，研究方向为遥感信息智能提取与自适应计算。E-mail：mydonne@163.com
HU Xiaodong1*,  
  • Affiliation：

    School of Science, Chang’an University, Xi’an 710064, China

WU Tianjun3,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

LIU Wei12,  
  • Affiliation：

    College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

XIA Liegang4,  
  • Affiliation：

    College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

YANG Haiping4,  
  • Affiliation：

    Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

SUN Yingwei5,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

XU Nan12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

ZHANG Xin12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

SHEN Zhanfeng1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

ZHOU Nan12

реферат

Through the earth observation, the geographical phenomena, patterns, and evolutionary processes on the earth’s surface can be fully reflected by the earth observation of remotely sensed imageries. The Land Use/Cover Change (LUCC) products from the High-resolution Remote Sensing (HSRS) data can provide full coverage, quantitative, and fast-updating background information for analyzing the law of spatial distribution of geographic features and their changing mechanisms. The development of HSRS is deeply driving the accuracy of LUCC information. At present, from the implementation effect of major survey projects such as national geographic conditions survey and natural resources survey deployed at the national level, the industrial production of LUCC information can be realized through the combination of human-computer interaction, in-house interpretation, and field survey. The development of high-resolution remote sensing is deeply driving the accuracy of LUCC information. How to improve the intelligent productivity of LUCC products through the innovation of theory and technology is an important bottleneck and key challenge.In the previous research, we put forward the basic concept of geo-parcel/geo-object based on the experience and mode of LUCC production in the departments of land investigation, and determined that the high-precision-level LUCC production is a gradually deepening cognitive process from external visual understanding to internal mechanism analysis. Therefore, this paper takes the geo-parcel as the basic unit of the cognition of land information of earth's surface, and further clarifies the meaning and geographical characteristics of LUCC.Based on the above background, we proposed a new concept of Precise LUCC (P-LUCC) in this paper, which integrates (accurate) quantitative index inversion model on the spatial structure of (fine) geo-parcels. First, this concept is a derivative of our developed spatial-spectrum cognitive theory, which is achieved by coupling HSRS visual features (TU-spatial maps) with multi-source and multi-modal observation mechanism features (PU-spectrum). Moreover, based on the geographical idea of “the unity of five land features”, we further proposed a series of intelligent remote sensing information extraction methods for P-LUCC production. They are organized hierarchically in three kinds of models, i.e. stratified perception model, spatiotemporal synergistically inversion model, and multi-granular decision-making model. In this aspect, we analyzed the cooperative computing mechanism of three types of machine learning models, namely deep learning for visual perception, transfer learning of external knowledge integration, and reinforcement learning via incremental self-organizing. Multi-type learning algorithms based on these mechanism are organized and transformed organically by using the route of “zoning partition-stratified extraction-graded transfer-functional reconstruction”. Thus, we designed a P-LUCC product production line for an information system of HSRS intelligent interpretation.Experiments were performed in the Suzhou High-tech Zone, China, and the accuracy and production efficiency of P-LUCC products were analyzed comprehensively. Through this large regional experimental verification, we show that our proposed technology has obvious advantages in the production accuracy and efficiency of LUCC products. Finally, we also provide some new ideas on thematic application based on P-LUCC information products. In conclusion, this intelligent production mode is worthy of popularization and application in engineering natural resources survey.

ключеви́че слова́

geo-parcel/TUPU;precision LUCC;the unity of five land features;machine learning;intelligent computing;spatial optimization

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