Early season crop classification using multi-frequency polarimetric synthetic aperture radar with machine learning methods

及时准确的作物分类对加拿大的作物管理和粮食安全至关重要。目前，加拿大的作物分类地图只在每个生长季节结束时提供，因此，早季作物分类将有助于加拿大更好地管理农业活动。大多数农作物具有相似的光谱特征，在早期生长季节频繁的阴天是光学卫星图像的挑战。当前合成孔径雷达（SAR）图像的应用受到较长重复周期的限制，因此利用多频率、多角度SAR图像对早季作物进行准确分类是必不可少的。本项目的目的是推广A.U.G.作物生育期估测技术。Signals Ltd.（AUG）利用Sentinel-1、TerraSAR-X和模拟雷达卫星星座使命极化合成孔径雷达（PolSAR）图像以及最先进的机器学习算法实现早季作物分类。该项目的成果将有助于AUG将作物监测技术商业化，为加拿大农业提供及时准确的信息。

Timely and accurate crop classification is essential for Canada's crop management and food security. Currently, Canadian crop classification maps are only available at the end of each growing season, therefore early season crop classification will help Canada better manage agricultural activities. Most crops have similar spectral signatures and frequent cloudy days in early growing seasons are challenges for optical satellite images. The application of current synthetic aperture radar (SAR) images is limited by longer repeating cycles so the utilization of multi-frequency and multi-angle SAR images are essential for achieving accurate early season crop classification. The objective of this project is to extend the crop growth stage estimation technology of A.U.G. Signals Ltd. (AUG) to achieve early season crop classification using Sentinel-1, TerraSAR-X and simulated RADARSAT constellation mission (RCM) polarimetric SAR (PolSAR) images with state-of-the-art machine learning algorithms. The outcome of the project will help AUG commercialize crop monitoring technologies to provide timely and accurate information for the Canadian agriculture industry.