Comparison of four machine learning methods for object-oriented change detection in high-resolution satellite imagery

High resolution image change detection is one of the key technologies of remote sensing application, which is of great significance for resource survey, environmental monitoring, fine agriculture, military mapping and battlefield environment detection. In this paper, for high-resolution satellite imagery, Random Forest (RF), Support Vector Machine (SVM), Deep belief network (DBN), and Adaboost models were established to verify the possibility of different machine learning applications in change detection. In order to compare detection accuracy of four machine learning Method, we applied these four machine learning methods for two high-resolution images. The results shows that SVM has higher overall accuracy at small samples compared to RF, Adaboost, and DBN for binary and from-to change detection. With the increase in the number of samples, RF has higher overall accuracy compared to Adaboost, SVM and DBN.

[1]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .

[2]  Nantheera Anantrasirichai,et al.  SVM-based texture classification in Optical Coherence Tomography , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[3]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[4]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[5]  Steven E. Franklin,et al.  A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery , 2012 .

[6]  D. Lu,et al.  Change detection techniques , 2004 .

[7]  Thomas Blaschke,et al.  Object-oriented image analysis and scale-space: Theory and methods for modeling and evaluating multi-scale landscape structure , 2001 .

[8]  Geoffrey J. Hay,et al.  Object-based change detection , 2012 .

[9]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[10]  Jordi Cristóbal,et al.  Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[11]  Dongmei Chen,et al.  Change detection from remotely sensed images: From pixel-based to object-based approaches , 2013 .

[12]  Chong Liu,et al.  The Integrated Use of DMSP-OLS Nighttime Light and MODIS Data for Monitoring Large-Scale Impervious Surface Dynamics: A Case Study in the Yangtze River Delta , 2014, Remote. Sens..