Learning-Based Fuzzy Fusion of Multiple Classifiers for Object-Oriented Classification of High Resolution Images

Abstract

In remote-sensing, multi-classifier systems (MCS) have found its use for efficient pixel level image classification. Current challenge faced by the RS community is, classification of very high resolution (VHR) satellite/aerial images. Despite the abundance of data, certain inherent difficulties affect the performance of existing pixel-based models. Hence, the trend for classification of VHR imagery has shifted to object-oriented image analysis (OOIA) which work at object level. We propose a shift of paradigm to object-oriented MCS (OOMCS) for efficient classification of VHR imagery. Our system uses the modern computer vision concept of superpixels for the segmentation stage in OOIA. To this end, we construct a learning-based decision fusion method for integrating the decisions from the MCS at superpixel level for the classification task. Upon detailed experimentation, we show that our method exceeds in performance with respect to a variety of traditional OOIA decision systems. Our method has also empirically outperformed under conditions of two typical artefacts, namely unbalanced samples and high intra-class variance.