Remote Sensing Images (RSI) have been used as a major source of data, particularly with respect to the creation of thematic maps. This process is usually modeled as a supervised learning task where the system needs to learn the patterns of interest provided by the user and assign a class to the rest of the image regions. Thus, it is common to have images obtained from different sensors, which could improve the quality of thematic maps. However, this requires the creation of techniques to properly encode and combine the different properties of the images. So, this paper proposes a boosting-based technique for classification of regions in RSI that manages to encode features extracted from different sources of data, spectral and spatial domains. The approach is evaluated in an urban and a coffee crop recognition scenarios, achieving statistically better results in comparison with the baselines in urban classification and better results at some baselines for the coffee crop recognition.
['Edemir Ferreira', 'Arnaldo de Albuquerque Araújo', 'Jefersson Alex dos Santos']
A Boosting-Based Approach for Remote Sensing Multimodal Image Classification