With the development of earth observation techniques

a large number of high-resolution remote sensing images can now be acquired by using different types of sensors. Handling these "big" remote sensing data with diverse characteristics is difficult when traditional remote sensing techniques are used. New challenges

such as high-dimensional datasets（high spatial and hyperspectral features）

complex structures（nonlinear and overlapped distribution）

and optimization problems（high computational complexity）

have also emerged. To address these problems

evolutionary computing-based techniques based on biological systems have been widely used for remote sensing image processing. Such techniques possess the following advantages:（1） powerful global optimization capability

acquiring the optimal or nearly optimal solution of objective functions;（2） self-organizing and self-learning capability

learning from original remote sensing data autonomously; and（3） capability of handling multi-objective problem

optimizing the multiple objective function simultaneously because of its population-based characteristics. Evolutionary computing has achieved preliminary success in the field of remote sensing data processing.In this paper

the applications of evolutionary computing to the fields of remote sensing image processing are reviewed

along with feature representation and feature selection

classification and clustering

and sub-pixel-level processing techniques

such as endmember extraction

hyperspectral unmixing

and sub-pixel mapping. Compared with the traditional methods of remote sensing image processing

these new methods are thought to be intelligent and accurate because of their powerful global optimization. For example

their constraints on the characteristics of objective functions

such as their derivatives

are few. They can also generate few assumptions about remote sensing data because of their self-organizing and self-learning capabilities. They can consider a large number of objective functions because of their capability of handling multi-objective problems. In summary

these new methods exhibit intelligent characteristics and high accuracy in remote sensing image processing. Finally

several crucial issues and research directions in the use of evolutionary computing are highlighted:（1）multi-objective optimization for regularization-based ill-posed problems in remote sensing processing

such as hyperspectral unmixing;（2）the discussion on the efficiency of evolutionary computing-based remote sensing processing methods

such as the memetic algorithm

which is a hybrid of evolutionary computing and machine learning

and the speeding up techniques of evolutionary computing-based remote sensing processing methods.