Design and Implementation of an Object-Based AdaBoost Algorithm Based on Active Learning for Land-Cover Classification in High-Resolution Images

Document Type : Original Article

Authors

1 M.Sc. in Remote Sensing, Dep. of Surveying, K.N. Toosi University of Technology

2 Prof. of Faculty of Geomatics Engineering, Member of the Scientific Center of Spatial Information Technology, K.N. Toosi University of Technology

3 Assistant Prof., Faculty of Civil Engineering, Noshirvani University of Technology, Babol

Abstract

By improvement of the spatial resolution of remote sensing images, more accurate information are provided from the image scene such as texture structures. However, extraction of land cover information from these datas has become a challenging process due to the high spectral diversity and the heterogeneity of surface materials. Visual interpretation is costly and time consuming and automatic interpretation of images does not necessarily lead to high accuracy. Achieving optimal interpretation accuracy requires the design of automatic algorithms that are capable of dealing with the complexity of the image scene. To overcome this problem, object-based image analysis (OBIA) that is sensitive to the image scene morphology, can be particularly effective in an urban area where the density of man-made structures is high. In object-based classification, pixels of a segment are analyzed in combination with each other. So the dimensions of the problem space are reduced, in compared to the pixel-based method, which leads to increasing the computational speed. Meanwhile, due to the different sizes of image segments, supervised object-based classification faces challenges in creating an optimal training set. In this research, AdaBoost algorithm was selected for the object-based classification, to overcome the problem of feature space imbalance, due to the small number of training samples in comparison with the high dimensions of the feature space (including spectral, spatial and geometric features), two strategies were proposed. In the first approach an active learning mechanism was integrated with AdaBoost to produce optimal training data set (OTD) and in another approach based on the feature-to-feature correlation (redundancy) and the feature-to-class correlation (relevance), the candidate feature subset (CFS) was generated to reduce the size of the feature space. To evaluate the proposed method, the developed algorithm was performed on the standard dataset of Vaihingen in Germany and the results were compared with the pixel-based classification. In order to evaluate the signification of the results, the McNemar statistical test was used. The experimental results showed that the proposed object-based approach improved the overall accuracy by 6% and the kappa coefficient by 7% compared to the pixel-based approach. Also, the computational speed of proposed object-based AdaBoost was significantly increased compared to the pixel-based approach. These results indicate the superiority of the proposed approach both in terms of accuracy and processing speed.

Keywords

References

Agrawal, M. & Nagwanshi, K., 2016, High Resolution Image Classification with Edge Detection Based Segmentation and AdaBoost, International Research Journal of Engineering and Technology (IRJET), 3(5), PP. 1844- 1847.
Baatz, M., 2000, Multi Resolution Segmentation: An Optimum Approach for High Quality Multi Scale Image Segmentation, In: Beutrage Zum AGIT-Symposium, Salzburg, Heidelberg, 2000 (PP. 12-23).
Baatz, M., Benz, U., Dehghani, S., Heynen, M., Höltje, A., Hofmann, P., Lingenfelder, I., Mimler, M., Sohlbach, M. & Weber, M., 2004, eCognition Professional User Guide version 4.0. München: Definiens Imaging GmbH.
Bui, Q.-T., Pham Van, M., Hang, N.T.T., Nguyen, Q.-H., Linh, N.X., Hai, P.M., Tuan, T.A. & Van Cu, P. 2019, Hybrid Model to Optimize Object-Based Land Cover Classification by Meta-Heuristic Algorithm: An Example for Supporting Urban Management in Ha Noi, Viet Nam, International Journal of Digital Earth, 12, PP. 1118-1132.
Chen, Y., Dou, P. & Yang, X., 2017, Improving Land Use/Cover Classification with a Multiple Classifier System Using AdaBoost Integration Technique, Remote Sensing, 9, P. 1055.
Cormen, T.H., Leiserson, C.E., Rivest, R.L. & Stein, C., 2009, Introduction to Algorithms, MIT Press.
 
Definiens, A., 2007, Definiens Developer 7 Reference Book, Definiens AG, München, PP. 21-24.
Dou, P., Chen, Y. & Yue, H., 2018, Remote-Sensing Imagery Classification Using Multiple Classification Algorithm-Based AdaBoost, International Journal of Remote Sensing, 39, PP. 619-639.
Dronova, I., Gong, P. & Wang, L., 2011, Object-Based Analysis and Change Detection of Major Wetland Cover Types and Their Classification Uncertainty during the Low Water Period at Poyang Lake, China, Remote Sensing of Environment, 115, PP. 3220-3236.
Duffy, N. & Helmbold, D.P., 2000, Potential Boosters?, In: Advances in Neural Informa-tion Processing Systems (PP. 258-264).
Fan, J., Zeng, G., Body, M. & Hacid, M.-S., 2005, Seeded Region Growing: An Extensive and Comparative Study, Pattern Recognition Letters, 26, PP. 1139-1156.
Foody, G.M., 2004, Thematic Map Comparison, Photogrammetric Engineering & Remote Sensing, 70, PP. 627-633.
Freund, Y. & Schapire, R.E., 1997, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences, 55, PP. 119-139.
Georganos, S., Grippa, T., Vanhuysse, S., Lennert, M., Shimoni, M., Kalogirou, S. & Wolff, E., 2018, Less Is More: Optimizing Classification Performance through Feature Selection in a Very-High-Resolution Remote Sensing Object-Based Urban Application, GIScience & Remote Sensing, 55, PP. 221-242.
Hamidi, M., Ebadi, H. & Kiani, A., 2018, Building Detection in Urban Areas from High-Resolution Remote Sensing Images Using the Developed AdaBoost Method and High-Level (Quasi-Deep) Features, JGST., 8(2), PP. 35-52.
Haralick, R.M., Shanmugam, K. & Dinstein, I., 1973, Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, SMC-3, PP. 610-621.
Heo, J. & Yang, J.Y., 2014, AdaBoost Based Bankruptcy Forecasting of Korean Construction Companies, Applied Soft Computing, 24, PP. 494-499.
Huang, X. & Zhang, L., 2012, An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery, IEEE Transactions on Geoscience and Remote Sensing, 51, PP. 257-272.
ISPRS, 2013, Web Site of the ISPRS Test Project on Urban Classification and 3D Building Reconstruction, Available: http://www2.isprs.org/commissions/comm3/wg4/tests.html.
Jia, Y., 2015, Object-Based Land Cover Classification with Orthophoto and Lidar Data, In: Environmental Science, Mathematics.
Kégl, B., 2013, The Return of AdaBoost. MH: Multi-Class Hamming Trees, LAL/LRI, University of Paris-Sud, CNRS, 91898 Orsay, France.
Kiani, A., Ahmadi, F.F. & Ebadi, H., 2019, Developing an Interpretation System for High-Resolution Remotely Sensed Images Based on Hybrid Decision-Making Process in a Multi-scale Manner, Journal of the Indian Society of Remote Sensing, PP. 1-18.
Kiani, A., Farnood Ahmadi, F. & Ebadi, H., 2021, Correction of Training Process in Object-Based Image Interpretation via Knowledge Based System Capabilities, Multimedia Tools and Applications, 80, PP. 24901-24924.
Laliberte, A.S., Browning, D. & Rango, A., 2012, A Comparison of Three Feature Selection Methods for Object-Based Classification of Sub-Decimeter Resolution UltraCam-L Imagery, International Journal of Applied Earth Observation and Geoinformation, 15, PP. 70-78.
Li, X., Wang, L. & Sung, E., 2004, Improving Adaboost for Classification on Small Training Sample Sets with Active Learning, Korea, Available:
   https://www.researchgate.net/publication/228849380_Improving_adaboost_for_classification_on_small_training_sample_sets_with_active_learning.
Ma, L., Li, M., Ma, X., Cheng, L., Du, P. & Liu, Y., 2017, A Review of Supervised Object-Based Land-Cover Image Classification, ISPRS Journal of Photogrammetry and Remote Sensing, 130, PP. 277-293.
Mason, L., Baxter, J., Bartlett, P.L. & Frean, M.R., 2000, Boosting Algorithms as Gradient Descent, In: Advances in Neural Information Processing Systems (PP. 512-518).
Maxwell, A.E., Warner, T.A. & Fang, F., 2018, Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review, International Journal of Remote Sensing, 39, PP. 2784-2817.
Momeni, R., Aplin, P. & Boyd, D.S., 2016, Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach, Remote Sensing, 8, P. 88.
Pérez-Ortiz, M., Peña, J.M., Gutiérrez, P.A., Torres-Sánchez, J., Hervás-Martínez, C. & López-Granados, F., 2016, Selecting Patterns and Features for between-and within-Crop-Row Weed Mapping Using UAV-Imagery, Expert Systems with Applications, 47, PP. 85-94.
Prasvita, D.S. & Arymurthy, A.M., 2017, Classification of LiDAR Images Fused with Aerial Optical Images Using Ensemble Classifier AdaBoost. MH and Post-processing BFS, International Journal of Technology And Business, 1, PP. 10-16.
Schapire, R.E. & Singer, Y., 1999, Improved Boosting Algorithms Using Confidence-Rated Predictions, Machine Learning, 37, PP. 297-336.
Tamimi, E., Ebadi, H. & Kiani, A., 2017, Evaluation of Different Metaheuristic Optimization Algorithms in Feature Selection and Parameter Determination in SVM Classification, Arabian Journal of Geosciences, 10, P. 478.
Tokarczyk, P., Wegner, J.D., Walk, S. & Schindler, K., 2015, Features, Color Spaces, and Boosting: New Insights on Semantic Classification of Remote Sensing Images, IEEE Transactions on Geoscience and Remote Sensing, 53, PP. 280-295.
Viola, P. & Jones, M.J., 2004, Robust Real-Time Face Detection, International Journal of Computer Vision, 57, PP. 137-154.
 

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