遥感图像包含丰富的光谱和空间信息,现已广泛用于农业、林业、灾害评估等众多领域。对遥感图像进行分类是各领域进行后续研究的基础,但因遥感图像数据高维、小样本等特点给分类精度带来了挑战。近年来,在计算机视觉领域取得显著成功的...遥感图像包含丰富的光谱和空间信息,现已广泛用于农业、林业、灾害评估等众多领域。对遥感图像进行分类是各领域进行后续研究的基础,但因遥感图像数据高维、小样本等特点给分类精度带来了挑战。近年来,在计算机视觉领域取得显著成功的深度学习已被广泛应用于遥感图像分类中。本文首先介绍了遥感图像分类的背景及目前存在的问题,然后对遥感图像分类领域中应用较为广泛的深度学习经典模型堆叠自编码器(Stacked Autoencoder, SAE)、深度置信网络(Deep Belief Networks, DBN)、卷积神经网络(Convolutional Neural Networks, CNN)、生成对抗网络(Generative Adversarial Networks, GAN)做了简要概述,接下来介绍了SAE、DBN、CNN和GAN在遥感图像分类领域中的应用发展状况,并对这些经典深度模型作了对比分析,最后对遥感图像分类的未来研究方向进行了展望。Remote sensing images contain rich spectral and spatial information, and have been widely used in many fields such as agriculture, forestry, and disaster assessment. The classification of remote sensing images is the foundation for subsequent research in various fields, but the high dimensionality and limited sample size of remote sensing image data present challenges to classification accuracy. In recent years, deep learning, which has achieved remarkable success in the field of computer vision, has been widely applied in remote sensing image classification. This paper first introduces the background and current problems of remote sensing image classification, and then provides a brief overview of the widely used deep learning classic models Stacked Autoencoder (SAE), Deep Belief Networks (DBN), Convolutional Neural Networks (CNN), and Generative Adversarial Networks (GAN) in the field of remote sensing image classification. Next, it introduces the development status of SAE, DBN, CNN, and GAN in the field of remote sensing image classification, and provides a comparative analysis of these classic deep learning models. Finally, it looks forward to the future research directions of remote sensing image classification.展开更多
文摘遥感图像包含丰富的光谱和空间信息,现已广泛用于农业、林业、灾害评估等众多领域。对遥感图像进行分类是各领域进行后续研究的基础,但因遥感图像数据高维、小样本等特点给分类精度带来了挑战。近年来,在计算机视觉领域取得显著成功的深度学习已被广泛应用于遥感图像分类中。本文首先介绍了遥感图像分类的背景及目前存在的问题,然后对遥感图像分类领域中应用较为广泛的深度学习经典模型堆叠自编码器(Stacked Autoencoder, SAE)、深度置信网络(Deep Belief Networks, DBN)、卷积神经网络(Convolutional Neural Networks, CNN)、生成对抗网络(Generative Adversarial Networks, GAN)做了简要概述,接下来介绍了SAE、DBN、CNN和GAN在遥感图像分类领域中的应用发展状况,并对这些经典深度模型作了对比分析,最后对遥感图像分类的未来研究方向进行了展望。Remote sensing images contain rich spectral and spatial information, and have been widely used in many fields such as agriculture, forestry, and disaster assessment. The classification of remote sensing images is the foundation for subsequent research in various fields, but the high dimensionality and limited sample size of remote sensing image data present challenges to classification accuracy. In recent years, deep learning, which has achieved remarkable success in the field of computer vision, has been widely applied in remote sensing image classification. This paper first introduces the background and current problems of remote sensing image classification, and then provides a brief overview of the widely used deep learning classic models Stacked Autoencoder (SAE), Deep Belief Networks (DBN), Convolutional Neural Networks (CNN), and Generative Adversarial Networks (GAN) in the field of remote sensing image classification. Next, it introduces the development status of SAE, DBN, CNN, and GAN in the field of remote sensing image classification, and provides a comparative analysis of these classic deep learning models. Finally, it looks forward to the future research directions of remote sensing image classification.
