EARLY-SEASON CROP CLASSIFICATION WITH RADARSAT-2 POLARIMETRIC SYNTHETIC APERTURE RADAR IMAGERY

被引：1
作者：
Tan, Weikai [1 ,2 ]
Sinha, Abhijit [1 ]
Li, Yifeng [1 ]
Ma, Lingfei [2 ]
Li, Jonathan [2 ]
机构：
[1] AUG Signals Ltd, 73 Richmond St W Suite 103, Toronto, ON M5H 4E8, Canada
[2] Univ Waterloo, Dept Geog & Environm Management, 200 Univ Ave W, Waterloo, ON N2L 3G1, Canada
来源：
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2020年
基金：
加拿大自然科学与工程研究理事会;
关键词：
Agriculture; crop classification; synthetic aperture radar; RADARSAT-2;
D O I：
10.1109/IGARSS39084.2020.9324480
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Timely crop classification maps are essential for the agriculture sector to ensure food security and understand the state and trend of crop growth. Though there are several crop monitoring systems in operation, early-season crop classification is still in demand. We developed a robust crop growth estimation technology previously with synthetic aperture radar (SAR) imagery for canola in Canadian Prairies, and we are extending the procedure to enable accurate early-season crop classification. Here we present a dynamic crop classification technique with RADARSAT-2 (RS2) polarimetric SAR (PolSAR) imagery for the classification of canola, corn, soybean and wheat, the four major crop types in Canadian Prairies. The procedure achieved over 90% classification accuracy of the major four crop types in the testing area by the end of July.
引用
收藏
页码：4167 / 4170
页数：4
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