An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System

Fully Convolutional Networks (FCN) has shown better performance than other classifiers like Random Forest (RF), Support Vector Machine (SVM) and patch-based Deep Convolutional Neural Network (DCNN), for object-based classification using orthoimage only in previous studies; however, for further improving deep learning algorithm performance, multi-view data should be considered for training data enrichment, which has not been investigated for FCN. The present study developed a novel OBIA classification using FCN and multi-view data extracted from small Unmanned Aerial System (UAS) for mapping landcovers. Specifically, this study proposed three methods to automatically generate multi-view training samples from orthoimage training datasets to conduct multi-view object-based classification using FCN, and compared their performances with each other and also with RF, SVM, and DCNN classifiers. The first method does not consider the object surrounding information, while the other two utilized object context information. We demonstrated that all the three versions of FCN multi-view object-based classification outperformed their counterparts utilizing orthoimage data only. Furthermore, the results also showed that when multi-view training samples were prepared with consideration of object surroundings, FCN trained with these samples gave much better accuracy than FCN classification trained without context information. Similar accuracies were achieved from the two methods utilizing object surrounding information, although sample preparation was conducted using two different ways. When comparing FCN with RF, SVM, DCNN implies that FCN generally produced better accuracy than the other classifiers, regardless of using orthoimage or multi-view data