State Key Laboratory of Remote Sensing Science, Institute of Remote sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China,Hyperspectral Computing Laboratory (Hypercomp), Department of Technology of Computers and Communications, University of Extremadura, Caceres, E-10003, Spain;
State Key Laboratory of Remote Sensing Science, Institute of Remote sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;
Hyperspectral Computing Laboratory (Hypercomp), Department of Technology of Computers and Communications, University of Extremadura, Caceres, E-10003, Spain;
The School of Geography and Planning and Guangdong Key Laboratory for Urbanization and Geo-Simulation, Sun Yat-sen University, Guangzhou, China;
Hyperspectral Computing Laboratory (Hypercomp), Department of Technology of Computers and Communications, University of Extremadura, Caceres, E-10003, Spain;
Hyperspectral Computing Laboratory (Hypercomp), Department of Technology of Computers and Communications, University of Extremadura, Caceres, E-10003, Spain;
Hyperspectral remote sensing; image classification; semi-supervised learning; spectral unmixing;
机译:半监督分类与高光谱数据混合相结合的混合策略
机译:高光谱图像超分辨率与深层学习和光谱解密相结合
机译:基于补丁的主动学习(PTAL)用于高光谱数据的光谱空间分类
机译:一种新的半监督分类策略,结合高光谱数据的主动学习和光谱解密
机译:地面真相数据的开发以及高光谱图像分类和光谱分解的准确性评估。
机译:主动学习加深度学习可以为多通道图像建立成本效益和强大的模型:一个关于高光谱图像分类的案例
机译:一种新的基于光谱分离概念的半监督高光谱图像分类算法