基于DRCNN的PolSAR图像分类综合实验设计

Design of comprehensive experiment for PolSAR image classification based on DRCNN

为了让学生更好地了解和掌握深度学习TensorFlow框架和CNN网络,采用基于不同区域的多尺度卷积神经网络(DRCNN)设计了PolSAR图像分类综合设计实验,旨在实现遥感图像的自动化分类和理解。极化合成孔径雷达(polarimetric synthetic aperture radar,PolSAR)图像能够提供更加丰富的极化信息,更好地刻画地物特征,对国防建设和国家发展具有重要意义。实验利用Python语言,在CNN基础上进行改进研究,设计了多区域的多尺度CNN模型,实现了极化SAR图像的数据处理、特征学习和分类一体化设计。该实验不仅可以帮助学生综合应用图像处理与深度学习知识,理解和利用CNN来进行极化SAR图像分类的基本原理和方法,还能使学生更加深入、熟练地掌握TensorFlow框架,提高学生的科研素质和动手实践能力。

In order to enable students to have a deeper understanding and mastery of the TensorFlow framework and CNN network for deep learning,a comprehensive design experiment for polarimetric SAR image classification is designed using multi-scale convolutional neural networks(DRCNN)based on different regions,aiming to achieve automated classification and understanding of remote sensing images.Polarimetric synthetic aperture radar(PolSAR)images can provide richer polarization information and better characterize ground features,which is of great significance for national defense construction and national development.The experiment utilized Python language to conduct improvement research on the basis of CNN,designed a multi-scale CNN model with multiple regions,and achieved the integrated design of data processing,feature learning,and classification for PolSAR images.This experiment not only helps students comprehensively apply image processing and deep learning knowledge,understand and utilize the basic principles and methods of CNN for PolSAR image classification,but also enables students to more deeply and proficiently master the TensorFlow framework,improve their scientific research quality and hands-on practical ability.