A NEW ARTIFICIAL INTELLIGENCE OPTIMIZATION METHOD FOR PCA BASED UNSUPERVISED CHANGE DETECTION OF REMOTE SENSING IMAGE DATA

In this study, a new artificial intelligence optimization algorithm, Differential Search (DS), was proposed for Principal Component Analysis (PCA) based unsupervised change detection method for optic and SAR image data. The model firstly computes an eigenvector space using previously created k × k blocks. The change detection map is generated by clustering the feature vector as two clusters which are changed and unchanged using Differential Search Algorithm. For clustering, a cost function is used based on minimization of Euclidean distance between cluster centers and pixels. Experimental results of optic and SAR images proved that proposed approach is effective for unsupervised change detection of remote sensing image data.

[1]  Mark A. Friedl,et al.  Distance metric-based forest cover change detection using MODIS time series , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[2]  J. Cihlar,et al.  Change detection with synthetic aperture radar , 1992 .

[3]  Ashish Ghosh,et al.  Fuzzy clustering algorithms for unsupervised change detection in remote sensing images , 2011, Inf. Sci..

[4]  Francesca Bovolo,et al.  Spatio-contextual fuzzy clustering with Markov random field model for change detection in remotely sensed images , 2014 .

[5]  S. Gopal,et al.  Remote sensing of forest change using artificial neural networks , 1996, IEEE Trans. Geosci. Remote. Sens..

[6]  Pinar Civicioglu,et al.  Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm , 2012, Comput. Geosci..

[7]  Turgay Çelik,et al.  Change Detection in Satellite Images Using a Genetic Algorithm Approach , 2010, IEEE Geoscience and Remote Sensing Letters.

[8]  S. Qin,et al.  Selection of the Number of Principal Components: The Variance of the Reconstruction Error Criterion with a Comparison to Other Methods† , 1999 .

[9]  Dervis Karaboga,et al.  A novel clustering approach: Artificial Bee Colony (ABC) algorithm , 2011, Appl. Soft Comput..

[10]  Pinar Civicioglu,et al.  A New Unsupervised Change Detection Approach Based On DWT Image Fusion And Backtracking Search Optimization Algorithm For Optical Remote Sensing Data , 2014 .

[11]  Wenzhong Shi,et al.  Unsupervised change detection using fuzzy c-means and MRF from remotely sensed images , 2013 .

[12]  Turgay Çelik,et al.  Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and $k$-Means Clustering , 2009, IEEE Geoscience and Remote Sensing Letters.

[13]  Jun Chen,et al.  Change Vector Analysis in Posterior Probability Space: A New Method for Land Cover Change Detection , 2011, IEEE Geoscience and Remote Sensing Letters.

[14]  William J. Emery,et al.  An Innovative Neural-Net Method to Detect Temporal Changes in High-Resolution Optical Satellite Imagery , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Francesca Bovolo,et al.  A Context-Sensitive Technique for Unsupervised Change Detection Based on Hopfield-Type Neural Networks , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Lorenzo Bruzzone,et al.  Automatic analysis of the difference image for unsupervised change detection , 2000, IEEE Trans. Geosci. Remote. Sens..

[17]  Ashish Ghosh,et al.  Fuzzy clustering algorithms incorporating local information for change detection in remotely sensed images , 2012, Appl. Soft Comput..

[18]  Turgay Çelik,et al.  Multiscale Change Detection in Multitemporal Satellite Images , 2009, IEEE Geoscience and Remote Sensing Letters.

[19]  Maoguo Gong,et al.  Image change detection based on an improved rough fuzzy c-means clustering algorithm , 2013, International Journal of Machine Learning and Cybernetics.

[20]  Yunlong Zhu,et al.  A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm , 2010 .

[21]  James A. R. Marshall,et al.  Swarm Cognition: an interdisciplinary approach to the study of self-organising biological collectives , 2011, Swarm Intelligence.

[22]  Ashish Ghosh,et al.  Unsupervised Change Detection in Remote-Sensing Images Using Modified Self-Organizing Feature Map Neural Network , 2007, 2007 International Conference on Computing: Theory and Applications (ICCTA'07).

[23]  Maoguo Gong,et al.  Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering , 2012, IEEE Transactions on Image Processing.

[24]  M. Wulder,et al.  Detecting post-fire salvage logging from Landsat change maps and national fire survey data , 2012 .

[25]  Ya-Qiu Jin.  Change detection of enhanced, no-changed and reduced scattering in multi-temporal ERS-2 SARimages using the two-thresholds EM and MRF algorithms , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..