A Review on Back-Propagation Neural Networks in the Application of Remote Sensing Image Classification

ANNs (Artificial neural networks) are used extensively in remote sensing image processing. It has been proven that BPNNs (back-propagation neural networks) have high attainable classification accuracy. However, there is a noticeable variation in the achieved accuracies due to different network designs and implementations. Hence, researchers usually need to conduct several experimental trials before they can finalize the network design. This is a time consuming process which significantly reduces the effectiveness of using BPNNs and the final design may still not be optimal. Therefore, there is a need to see whether there are some common guidelines for effective design and implementation of BPNNs. With this aim in mind, this paper attempts to find and summarize the common guidelines suggested by different authors through literature review and discussion of the findings. To provide readers with background and contextual information, some ANN fundamentals are also introduced.

[1]  Juan J. Flores,et al.  The application of artificial neural networks to the analysis of remotely sensed data , 2008 .

[2]  Daniel Graupe,et al.  Principles of Artificial Neural Networks , 2018, Advanced Series in Circuits and Systems.

[3]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[4]  G. Brier.  VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .

[5]  Jing Zhang,et al.  Application of back propagation neural network in the classification of high resolution remote sensing image: Take remote sensing image of beijing for instance , 2010, 2010 18th International Conference on Geoinformatics.

[6]  Guy M. Nicoletti.  AN ANALYSIS OF NEURAL NETWORKS AS SIMULATORS AND EMULATORS , 2000, Cybern. Syst..

[7]  P. Atkinson,et al.  Introduction Neural networks in remote sensing , 1997 .

[8]  Alsmadi M.Kh.S.,et al.  Back Propagation Algorithm: The Best Algorithm Among the Multi-layer Perceptron Algorithm , 2009 .

[9]  S. O. Prasher,et al.  Application of artificial neural networks in image recognition and classification of crop and weeds , 2000 .

[10]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[11]  Zhaoyang Xu,et al.  Application of BP neural network in remote sensing image classification , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[12]  Michael Egmont-Petersen,et al.  Image processing with neural networks - a review , 2002, Pattern Recognit..

[13]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[14]  Li Chen,et al.  BPN for Land Cover Classification by Using Remotely Sensed Data , 2009, 2009 Fifth International Conference on Natural Computation.

[15]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[16]  Issam Abu-Mahfouz.  A comparative study of three artificial neural networks for the detection and classification of gear faults , 2005, Int. J. Gen. Syst..

[17]  Boran Sekeroglu.  Classification of sonar images using back propagation neural network , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[18]  M. Toshniwal.  An optimized approach to application of neural networks to classification of multispectral, remote sensing data , 2005, Proceedings. 2005 IEEE Networking, Sensing and Control, 2005..