基于eCongnition软件的土地利用分类研究

doi:10.12190/j.issn.2096-1197.2020.04.21

北方农业学报 ›› 2020, Vol. 48 ›› Issue (4): 126-134.doi: 10.12190/j.issn.2096-1197.2020.04.21

• 农业气象·农业信息技术 • 上一篇

基于eCongnition软件的土地利用分类研究

乌云德吉^1,2, 陈瑞卿², 敦惠霞¹

1.内蒙古自治区农牧业科学院农牧业经济与信息研究所,内蒙古呼和浩特 010031;
2.中国农业科学院农业资源与农业区划研究所/农业农村部农业遥感重点实验室,北京 100081

收稿日期:2020-07-28 出版日期:2020-08-20 发布日期:2020-10-23
通讯作者: 敦惠霞（1962—）,女,编审,学士,主要从事农业信息管理及期刊出版工作。
作者简介:乌云德吉（1987—）,女,助理研究员,博士研究生,研究方向为农情遥感监测。
基金资助:
内蒙古自治区农牧业科学院青年创新基金项目（2017QNJJN10）; 内蒙古农牧业创新基金项目（2018CXJJ09）

Study on the classification of land utilization based on the eCongnition software

Wuyundeji^1,2, CHEN Ruiqing², DUN Huixia¹

1. Institute of Agricultural and Animal Husbandry Economics and Information,Inner Mongolia Academy of Agricultural and Animal Husbandry Sciences,Hohhot 010031,China;
2. Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences/Key Laboratory of Agri-information,Ministry of Agriculture and Rural Affairs,Beijing 100081,China

Received:2020-07-28 Online:2020-08-20 Published:2020-10-23

摘要/Abstract

摘要： 【目的】利用遥感制图对海南省屯昌县土地利用情况进行分析评价。【方法】文章采用海南地区GF-2 多光谱4 m和全色1 m影像,经过正射校正、辐射定标、大气校正和融合处理,在 eCongnition软件平台上,利用面向对象分类方法,通过构建NDVI、NDWI、SAVI 3种植被指数特征值,结合多尺度分割算法,建立规则集,逐层逐步提取了该地区林地、水体、不透水面和其他植被4种类型的地物。【结果】林地和水体提取结果较优,NDVI对林地和其他植被2个地类识别的贡献率较大。 NDWI指数的引入能够较好地解决浅水识别困难的问题,取得了很好的水体分类效果;通过SAVI临界阈值的设定,不透水面和道路能够较好地分类,但是部分裸土地与不透水面容易混淆。分类结果为不透水面12.392 km²、水体2.534 km²、林地8.519 km²、其他植被7.690 km²。【结论】在eCongnition 软件平台的多尺度分割算法和多种指数结合逐层提取土地利用覆盖分类方法具有高效且便于操作等特点,适合在区域尺度上推广。

关键词: 土地利用分类, 面向对象分类方法, eCongnition, 植被指数, 不透水面

Abstract: 【Objective】 To analyze and evaluate the land use in Tunchang County of Hainan Province by using remote sensing mapping.【Methods】 The GF-2 multi-spectral 4-meter and panchromatic 1-meter images in Hainan were used to extract the land cover types.After the data preprocessing,the vegetation index NDVI,NDWI and SAVI wer constructed on eCongnition software platform as well as the rule set by using object-based classification approach,combining with multi-scale segmentation algorithm.And then four types of land objects,i.e.woodland,water body,impervious surface and other vegetation were gradually extracted from the image.【Results】 The results of extracting forest land and water body were better,and NDVI contributes more for the recognition of forest land and other vegetation.The introduction of NDWI index could solve the difficult problem of shallow water identification in urban areas and surrounding areas,and had achieved good classification results.Through setting SAVI threshold,urban impervious surface and road could be classified better,but some bare land and impervious surface were easy to be confused.The classification results were 12.392 km² of impervious surface,2.534 km² of water,8.519 km² of woodland and 7.690 km² of other vegetation respectively【Conclusion】 The multi-scale segmentation algorithm and multi-index extraction method based on eCongnition software had the characteristics of high efficiency and easy operation,and were suitable for applicating on regional scale.

Key words: Land use classification, Object-based classification approach, eCongnition, Vegetation index, Impervious surface

中图分类号:

TP701

乌云德吉, 陈瑞卿, 敦惠霞. 基于eCongnition软件的土地利用分类研究[J]. 北方农业学报, 2020, 48(4): 126-134.

Wuyundeji, CHEN Ruiqing, DUN Huixia. Study on the classification of land utilization based on the eCongnition software[J]. Journal of Northern Agriculture, 2020, 48(4): 126-134.

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基于eCongnition软件的土地利用分类研究

Study on the classification of land utilization based on the eCongnition software

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