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首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Multi-source remotely sensed data fusion for improving land cover classification
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Multi-source remotely sensed data fusion for improving land cover classification

机译：多源遥感数据融合改善土地覆被分类

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摘要

Although many advances have been made in past decades, land cover classification of fine-resolution remotely sensed (RS) data integrating multiple temporal, angular, and spectral features remains limited, and the contribution of different RS features to land cover classification accuracy remains uncertain. We proposed to improve land cover classification accuracy by integrating multi-source RS features through data fusion. We further investigated the effect of different RS features on classification performance. The results of fusing Landsat-8 Operational Land Imager (OLI) data with Moderate Resolution Imaging Spectroradiometer (MODIS), China Environment lA series (HJ-1A), and Advanced Spaceborne Thermal Emission and Reflection (ASTER) digital elevation model (DEM) data, showed that the fused data integrat-ing temporal, spectral, angular, and topographic features achieved better land cover classification accu-racy than the original RS data. Compared with the topographic feature, the temporal and angular features extracted from the fused data played more important roles in classification performance, espe-cially those temporal features containing abundant vegetation growth information, which markedly increased the overall classification accuracy. In addition, the multispectral and hyperspectral fusion suc-cessfully discriminated detailed forest types. Our study provides a straightforward strategy for hierarchi-cal land cover classification by making full use of available RS data. All of these methods and findings could be useful for land cover classification at both regional and global scales. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
机译：尽管在过去的几十年中取得了许多进展，但结合了多个时间，角度和光谱特征的高分辨率遥感（RS）数据的土地覆盖分类仍然受到限制，并且不同的RS特征对土地覆盖分类准确性的贡献仍然不确定。我们提出通过数据融合整合多源遥感特征来提高土地覆被分类的准确性。我们进一步研究了不同RS功能对分类性能的影响。将Landsat-8作战陆地成像仪（OLI）数据与中分辨率成像光谱仪（MODIS），中国环境lA系列（HJ-1A）和高级星载热发射与反射（ASTER）数字高程模型（DEM）数据融合的结果的结果表明，融合了时间，光谱，角度和地形特征的融合数据比原始RS数据具有更好的土地覆被分类准确性。与地形特征相比，从融合数据中提取的时间和角度特征在分类性能中起着更为重要的作用，特别是那些包含丰富的植被生长信息的时间特征，显着提高了整体分类的准确性。此外，多光谱和高光谱融合成功地区分了详细的森林类型。我们的研究通过充分利用可用的RS数据，为分层土地覆被分类提供了一种简单的策略。所有这些方法和发现对于区域和全球范围的土地覆盖分类都可能有用。 （C）2016国际摄影测量与遥感学会（ISPRS）。由Elsevier B.V.发布。保留所有权利。

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  • 作者

    Chen Bin; Huang Bo; Xu Bing;

  • 作者单位

    Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China|Chinese Univ Hong Kong, Dept Geog & Resource Management, Shatin, Hong Kong, Peoples R China;

    Chinese Univ Hong Kong, Dept Geog & Resource Management, Shatin, Hong Kong, Peoples R China;

    Beijing Normal Univ, Coll Global Change & Earth Syst Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China|Tsinghua Univ, Dept Earth Syst Sci, Minist Educ, Key Lab Earth Syst Modeling, Beijing 100084, Peoples R China|Univ Utah, Dept Geog, 260 S Cent Campus Dr, Salt Lake City, UT USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Land cover classification; Remote sensing; Data fusion; Temporal and angular features;

    机译：土地覆被分类;遥感;数据融合;时空和角度特征;

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