A Multiclass Approach for Land-Cover Mapping by Using Multiple Data Sensors

Abstract

An usual way to acquire information about monitored objects or areas in earth surface is by using remote sensing images. These images can be obtained by different types of sensors (e.g., active and passive) and according to the sensor, distinct properties can be observed from the specified data. Typically, these sensors are specialized to encode one or few properties from the object (e.g. spectral and spatial properties), which makes necessary the use of diverse and different sensors to obtain complementary information. Given the amount of information collected, it is essential to use a suitable technique to combine the different features. In this work, we propose a new late fusion technique, a majority voting scheme, which is able to exploit the diversity of different types of features, extracted from different sensors. The new approach is evaluated in an urban classification scenario, achieving statistically better results in comparison with the proposed baselines.