Multimodal Classification of Remote Sensing Images

Remote Sensing Images (RSIs) 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 classification problem 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. Associated with the nature of RSIs, there are several challenges that can be highlighted: (1) they are georeferenced images, i.e., a geographic coordinate is associated with each pixel; (2) the data commonly captures specific frequencies across the electromagnetic spectrum instead of the visible spectrum, which requires the development of specific algorithms to describe patterns; (3) the detail level of each data may vary, resulting in images with different spatial and pixel resolution, but covering the same area; (4) due to the high pixel resolution images, efficient processing algorithms are desirable. Thus, it is very common to have images obtained from different sensors, which could improve the quality of thematic maps generated. However, this requires the creation of techniques to properly encode and combine the different properties of the images. Therefore, this M.Sc. dissertation proposes two techniques for classification of regions in RSIs that manages to encode features extracted from different sources of data, spectral and spatial domains. The major objective is the development of approches able to exploit the diversity of these different types of features to improve the accuracy in the creation of thematic maps. Keywords-Multimodal Classification; Remote Sensing; Data Fusion.