Multiscale Classification of Remote Sensing Images

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....

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 50; no. 10; pp. 3764 - 3775
Main Authors: dos Santos, Jefersson Alex, Gosselin, P., Philipp-Foliguet, S., da S Torres, R., Falao, A. X.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01-10-2012
Institute of Electrical and Electronics Engineers
Subjects:
Applied geophysics
Boosting
Computer Science
Earth sciences
Earth, ocean, space
Exact sciences and technology
Feature extraction
Histograms
Image color analysis
image descriptors
Image segmentation
Internal geophysics
Machine Learning
Multimedia
multiscale classification
multiscale segmentation
remote sensing image (RSI)
Statistics
Support vector machines
support vector machines (SVM)
Training
Vectors
experimental studies
multiscale classification
support vector machines (SVM)
maps
remote sensing
Boosting
global
baseline
classification
inventory
quality
remote sensing image (RSI)
multiscale segmentation
segmentation
image descriptors
land cover
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Summary: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.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2012.2186582