CROP CLASSIFICATION USING MULTITEMPORAL LANDSAT 8 IMAGES

被引：0

作者：

Song, Jingduo ^{[1
]}

Xing, Minfeng ^{[1
,2
]}

Ma, Yichuan ^{[3
]}

Wang, Long ^{[1
]}

Luo, Kaiwei ^{[1
]}

Quan, Xingwen ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Resources & Environm, Chengdu 611731, Sichuan, Peoples R China

[2] Univ Elect Sci & Technol China, Ctr Informat & Geosci, Chengdu 611731, Peoples R China

[3] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China

来源：

2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) | 2019年

基金：

中国国家自然科学基金;

关键词：

Time Series; Cloud Processing; Crop Classification; COVER CLASSIFICATION; CLOUD;

D O I：

10.1109/igarss.2019.8899274

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

The objective of this study is to investigate the potential of multitemporal remote sensing images for crop classification. Multi-temporal Landsat 8 OLI/TIRS C1 Level-1 images were acquired. The surface reflectance of visible and near infrared bands was used to represent the characteristics of crops. A time series model of surface reflectance was constructed for crop classification. Cloud cover is critical for the accuracy of classification. In order to remove the influence of clouds, the cloud pixels were neglected by setting a constant. Pearson correlation coefficient was used in the time series model of surface reflectance to classify the crop type. Finally, the overall accuracy reaches 78.26% and Kappa reaches 71.33%. Therefore, the method has the operational potential for crop classification even in the special area with cloudy or foggy weather.

引用

页码：2407 / 2410

页数：4

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