Particle swarm optimization-based sub-pixel mapping for remote-sensing imagery

Mixed pixels are widely existent in remote-sensing imagery. Although the proportion occupied by each class in mixed pixels can be determined by spectral unmixing, the spatial distribution of classes remains unknown. Sub-pixel mapping (SPM) addresses this problem and a sub-pixel/pixel spatial attraction model (SPSAM) has been introduced to realize SPM. However, this algorithm fails to adequately consider the correlation between sub-pixels. Consequently, the SPM results created by SPSAM are noisy and the accuracy is limited. In this article, a method based on particle swarm optimization is proposed as post-processing on the SPM results obtained with SPSAM. It searches the most likely spatial distribution of classes in each coarse pixel to improve the SPSAM. Experimental results show that the proposed method can provide higher accuracy and reduce the noise in the results created by SPSAM. When compared with the available modified pixel-swapping algorithm, the proposed method often yields higher accuracy results.

[1]  A. Tatema,et al.  Super-resolution land cover pattern prediction using a Hopfield neural network , 2001 .

[2]  Peter M. Atkinson.  Super-resolution target mapping from soft classified remotely sensed imagery , 2001 .

[3]  M. Q. Nguyen,et al.  Superresolution mapping using a Hopfield neural network with lidar data , 2005, IEEE Geoscience and Remote Sensing Letters.

[4]  Pisal Yenradee,et al.  PSO-based algorithm for home care worker scheduling in the UK , 2007, Comput. Ind. Eng..

[5]  F. Ling,et al.  Waterline mapping at the subpixel scale from remote sensing imagery with high‐resolution digital elevation models , 2008 .

[6]  Liangpei Zhang,et al.  A new sub-pixel mapping algorithm based on a BP neural network with an observation model , 2008, Neurocomputing.

[7]  Yong Ge,et al.  Development and Testing of a Subpixel Mapping Algorithm , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Hugh G. Lewis,et al.  Super-resolution mapping of urban scenes from IKONOS imagery using a Hopfield neural network , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[9]  Ashton M. Shortridge,et al.  Assessing Alternatives for Modeling the Spatial Distribution of Multiple Land-cover Classes at Sub-pixel Scales , 2007 .

[10]  Robert De Wulf,et al.  Sub-pixel mapping with neural networks : real-world spatial configurations learned from artificial shapes , 2003 .

[11]  Hugh G. Lewis,et al.  Super-resolution target identification from remotely sensed images using a Hopfield neural network , 2001, IEEE Trans. Geosci. Remote. Sens..

[12]  Ming Li,et al.  A global convergence PSO training algorithm of neural networks , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[13]  Koen C. Mertens,et al.  A sub‐pixel mapping algorithm based on sub‐pixel/pixel spatial attraction models , 2006 .

[14]  J. Qi,et al.  Modification of pixel-swapping algorithm with initialization from a sub-pixel/pixel spatial attraction model , 2009 .

[15]  Robert De Wulf,et al.  Land cover mapping at sub-pixel scales using linear optimization techniques , 2002 .

[16]  L. P. C. Verbeke,et al.  Using genetic algorithms in sub-pixel mapping , 2003 .

[17]  Bernard De Baets,et al.  Direct sub-pixel mapping exploiting spatial dependence , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[18]  Peter M. Atkinson,et al.  Sub‐pixel mapping of rural land cover objects from fine spatial resolution satellite sensor imagery using super‐resolution pixel‐swapping , 2006 .

[19]  Yaozhong Pan,et al.  Multiple-class Land Cover Mapping at the Sub-pixel Scale using an Innovated CA Model , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[20]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[21]  Li Xia,et al.  Swarm intelligence for classification of remote sensing data , 2008 .

[22]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[23]  X.H. Zhi,et al.  A discrete PSO method for generalized TSP problem , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[24]  Zarine P. Kemp,et al.  Innovations in GIS 4 , 1997 .

[25]  R. H. Kassaye,et al.  Suitability of Markov Random Field-Based Method for Super-Resolution Land Cover Mapping , 2013 .

[26]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[27]  Yasuyo Kato Makido.  Land cover mapping at sub-pixel scales , 2006 .

[28]  Pramod K. Varshney,et al.  Super-resolution land cover mapping using a Markov random field based approach , 2005 .

[29]  Russell C. Eberhart,et al.  chapter seven – The Particle Swarm , 2001 .

[30]  P. Atkinson.  Sub-pixel Target Mapping from Soft-classified, Remotely Sensed Imagery , 2005 .

[31]  Alfonso Fernández-Manso,et al.  Spectral unmixing , 2012 .