张立强, 李洋, 侯正阳, 李新港, 耿昊, 王跃宾, 李景文, 朱盼盼, 梅杰, 姜颜笑, 李帅朋, 辛奇, 崔颖, 刘素红. 深度学习与遥感数据分析[J]. 武汉大学学报 ( 信息科学版), 2020, 45(12): 1857-1864. DOI: 10.13203/j.whugis20200650
引用本文: 张立强, 李洋, 侯正阳, 李新港, 耿昊, 王跃宾, 李景文, 朱盼盼, 梅杰, 姜颜笑, 李帅朋, 辛奇, 崔颖, 刘素红. 深度学习与遥感数据分析[J]. 武汉大学学报 ( 信息科学版), 2020, 45(12): 1857-1864. DOI: 10.13203/j.whugis20200650
ZHANG Liqiang, LI Yang, HOU Zhengyang, LI Xingang, GENG Hao, WANG Yuebin, LI Jingwen, ZHU Panpan, MEI Jie, JIANG Yanxiao, LI Shuaipeng, XIN Qi, CUI Ying, LIU Suhong. Deep Learning and Remote Sensing Data Analysis[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1857-1864. DOI: 10.13203/j.whugis20200650
Citation: ZHANG Liqiang, LI Yang, HOU Zhengyang, LI Xingang, GENG Hao, WANG Yuebin, LI Jingwen, ZHU Panpan, MEI Jie, JIANG Yanxiao, LI Shuaipeng, XIN Qi, CUI Ying, LIU Suhong. Deep Learning and Remote Sensing Data Analysis[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1857-1864. DOI: 10.13203/j.whugis20200650

深度学习与遥感数据分析

Deep Learning and Remote Sensing Data Analysis

  • 摘要: 深度学习的迅猛发展,为遥感大数据的智能分析提供了重要技术手段。首先主要介绍了遥感数据识别和应用中设计的深度学习模型与方法,提出并实现了面向激光雷达点云、光学遥感图像和高光谱图像等数据地物识别的深度强化学习、多任务学习和亚像素-像素-超像素特征学习网络模型。这类模型的参数基本上由学习得到,调参工作量小,而且充分顾及了地物间的空间和上下文信息以及纹理和光谱特征,泛化能力强。然后描述了联合深度学习和多源遥感数据在精准扶贫评估、青藏高原20 a湿地变化及空间分析和玉米产量估产等方面的研究进展。从中可以看出,为了更好地促进遥感数据向知识的转化,需要面向应用,充分发挥深度学习在遥感大数据处理的优势,发展新的数据处理算法与技术。

     

    Abstract: The rapid development of deep learning provides an important technical means for intelligent analysis of remote sensing big data. Firstly, this paper mainly introduces the deep learning modes in remote sensing data recognition and application, and proposes a deep reinforcement learning, multi-task learning and sub-pixel-pixel-super-pixel feature learning models for object features recognition from LiDAR point clouds, optical remote sensing images and hyperspectral images. The model parameters are basically obtained by learning, and thus the workload of the parameter adjustments is small. The spatial and contextual information, texture and spectral characteristics between ground objects are fully taken into account, so the presented models have good generalization abilities. Then, it describes the progress in terms of the joint deep learning and multi-source remote sensing data in accurate poverty alleviation assessment, wetland change and spatial analysis in Qinghai-Tibet Plateau in the past 20 years, and corn yield estimation. In order to better promote the transformation from remote sensing data to knowledge, it is necessary give full play to the advantages of deep learning in remote sensing big data processing, and develop new data processing algorithms and technologies.

     

/

返回文章
返回