The continuously improving resolution of spatial

temporal

spectral

and radiometric for remote sensing data also increases the data type. For example

the amount of remote sensing data acquired from the aerospace

aviation

space

and other remote sensing platforms is increasing dramatically. Therefore

remote sensing data have obvious big data characteristics. This study analyzes the key technologies and issues in applying remote sensing big data and provides valuable reference for researchers. Based on numerous references

the characteristics of remote sensing big data are clarified. The processing systems for remote sensing big data are introduced from the perspectives of GPU hardware acceleration

clustering

grid

cloud computing

cloud grid

and complex high performance. The key technologies of remote sensing big data

including distributed clustered storage technology

are discussed. The existing problems are discussed through uncertainties

information fusion

machine learning

and analysis platform of remote sensing big data. The development trends are also discussed

including modeling a variety of uncertainties of remote sensing big data and machine learning methods for remote sensing big data. This study reviews the characteristics of remote sensing big data

the typical processing system

and the core technology. Key issues and future trends in this area in the practical application of academic research are also summarized. Big data technologies bring not only opportunities but also challenges for remote sensing data mining and knowledge acquisition. Several significant breakthroughs

such as machine learning for big data

unified analysis framework

and high-level information fusion

can promote further development for remote sensing knowledge mining.