Multiscale classification of remote sensing images

A huge effort has been applied in image classification to create high-quality thematic maps and to establish precise inventories about land cover use. The peculiarities of remote sensing images (RSIs) combined with the traditional image classification challenges made RSI classification a hard task. Our aim is to propose a kind of boost-classifier adapted to multiscale segmentation. We use the paradigm of boosting, whose principle is to combine weak classifiers to build an efficient global one. Each weak classifier is trained for one level of the segmentation and one region descriptor. We have proposed and tested weak classifiers based on linear support vector machines (SVM) and region distances provided by descriptors. The experiments were performed on a large image of coffee plantations. We have shown in this paper that our approach based on boosting can detect the scale and set of features best suited to a particular training set. We have also shown that hierarchical multiscale analysis is able to reduce training time and to produce a stronger classifier. We compare the proposed methods with a baseline based on SVM with radial basis function kernel. The results show that the proposed methods outperform the baseline.


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Related papers:

J. A. dos Santos, P-H. Gosselin, S. Phillip-Foliguet, R. da S. Torres, A. X. Falcão. Multiscale Classification of Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, v. 50, p. 3764-3775, 2012
J. A. dos Santos, P-H. Gosselin, S. Phillip-Foliguet, R. da S. Torres, and A. X. Falcão. Interactive Multiscale Classification of High-Resolution Remote Sensing Images. IEEE Journal of Selected Topics on Earth Observations and Remote Sensing, v. 6, p. 2020-2034, 2013
J.A. dos Santos, F.A. Faria, R. Torres, A. Rocha, P.H. Gosselin, S. Philipp-Foliguet, A.X. Falcao Descriptor Correlation Analysis for Remote Sensing Image Multi-Scale Classification. In International Conference on Pattern Recognition, Tsukuba Science City, Japan, November 2012
Almeida, J. A. dos Santos, B. Alberton, R. da S. Torres, and L. P. C. Morellato. Applying Machine Learning based on Multiscale Classifiers to Detect Remote Phenology Patterns in Cerrado Savanna Trees. Ecological Informatics, 2014.