Früher|Nächster

Deep-learning approaches for pixel-level pansharpening

  • role： First author第一作者
  • Affiliation：

    School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China

  • Email：greatyangy@126.com
  • Introduction：杨勇， 研究方向为遥感图像处理、计算机视觉与机器学习。E-mail: greatyangy@126.com
YANG Yong1,  
  • Affiliation：

    School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China

SU Zhao1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Software, Tiangong University, Tianjin 300387, China

  • Email：shuyinghuang2010@126.com
  • Introduction：黄淑英，研究方向为图像处理、人工智能。E-mail: shuyinghuang2010@126.com
HUANG Shuying2*,  
  • Affiliation：

    School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang 330032, China

WAN Weiguo3,  
  • Affiliation：

    School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China

TU Wei1,  
  • Affiliation：

    School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China

LU Hangyuan1

Resümee

Pansharpening is a fundamental problem in the field of remote sensing data processing. It has important research significance and application value in ground object classification and ground surface change detection. In recent years, Deep Learning (DL) has made great progress in natural language processing, computer vision, etc. and has promoted the development of pixel-level pansharpening technology. This work presents a systematic review of the research of DL in pansharpening from two aspects (classical and collaborative approaches) and makes a prospect on this basis. First, the common datasets of pansharpening and the objective evaluation indexes of pansharpening, including reference and non-reference quality evaluation indexes, are provided. Second, the latest research results of DL-based pansharpening are introduced in two different categories from the classical and collaborative methods, and the performance of their algorithms is compared, analyzed, and summarized. The classical methods mainly include AE-based pansharpening, CNN-based pansharpening, DRN-based pansharpening, and GAN-based pansharpening methods. Meanwhile, the collaborative methods mainly include DL+CS-based pansharpening, DL+MRA-based pansharpening, DL+MB-based pansharpening, DL+injection model-based pansharpening, CNN+DRN-based pansharpening, and RNN+CNN-based pansharpening methods. In the comparative analysis of the classical and collaborative methods, the common point is that the DL technology can automatically learn the advantages of complex data features and extract the feature information of the MS or PAN image (i.e., the information that needs to be retained in the HRMS fusion image). The difference is that the structure of the classical mode is more concise, while that of the collaborative mode is more complex because it is the combination of multiple methods or frameworks. In addition, most early DL-based pansharpening methods utilized the powerful data fitting ability of the DL model and seldom paid attention to the field of pansharpening problems. With the gradual deepening of research, such as using DL methods combined with traditional pansharpening methods, this designed fusion model considers spectral and spatial distortions. Accordingly, the DL methods can further enhance the pansharpening effect. Thirdly, the three main application fields of pansharpening are analyzed, such as object classification, target recognition, and surface change detection. Finally, this work discusses the future research direction of DL-based pansharpening in combination with remote sensing knowledge to fully tap the potential of DL to obtain fused images with richer details and more natural spectra. For example, for the evaluation of pansharpening application, the performance of pansharpening in a certain application is related not only to the high quality of fusion image but also to the knowledge of a specific application field. Accordingly, the application-oriented pansharpening evaluation algorithms will be the focus of future study. Furthermore, DL-based pansharpening needs to train a large number of network parameters, resulting in a longer training time for the pansharpening model. The lightweight depth model has a smaller network capacity, lower time complexity, and lower hardware requirements. Therefore, constructing a lightweight pansharpening model is a promising future direction.

Schlüsselwort

pansharpening;deep learning;classical mode;collaborative mode;quality evaluation;remote sensing image fusion

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