A fully learnable context-driven object-based model for mapping land cover using multi-view data from unmanned aircraft systems

• Published in: Remote sensing of environment 2018-10, Vol.216, p.328-344
• Main Authors: Liu, Tao, Abd-Elrahman, Amr, Zare, Alina, Dewitt, Bon A., Flory, Luke, Smith, Scot E.
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Classification; Classifiers; Conditional random fields; CRF; DCNN; GMM; Image classification; Information processing; Information systems; Land cover; Land use; Mapping; Mathematical models; Model accuracy; Model testing; Multi-view; Neural networks; OBIA; Parameters; Support vector machines; SVM; Unmanned aerial vehicles; Unmanned aircraft; Wetland; Wetlands
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2018.06.031

Context information is rarely used in the object-based landcover classification. Previous models that attempted to utilize this information usually required the user to input empirical values for critical model parameters, leading to less optimal performance. Multi-view image information is useful for improving classification accuracy, but the methods to assimilate multi-view information to make it usable for context driven models have not been explored in the literature. Here we propose a novel method to exploit the multi-view information for generating class membership probability. Moreover, we develop a new conditional random field model to integrate multi-view information and context information to further improve landcover classification accuracy. This model does not require the user to manually input parameters because all parameters in the Conditional Random Field (CRF) model are fully learned from the training dataset using the gradient descent approach. Using multi-view data extracted from small Unmanned Aerial Systems (UASs), we experimented with Gaussian Mixed Model (GMM), Random Forest (RF), Support Vector Machine (SVM) and Deep Convolutional Neural Networks (DCNN) classifiers to test model performance. The results showed that our model improved average overall accuracies from 58.3% to 74.7% for the GMM classifier, 75.8% to 87.3% for the RF classifier, 75.0% to 84.4% for the SVM classifier and 80.3% to 86.3% for the DCNN classifier. Although the degree of improvement may depend on the specific classifier respectively, the proposed model can significantly improve classification accuracy irrespective of classifier type. •A CRF model utilizing context information was developed for land cover mapping.•A method utilizing multi-view data in CRF model was developed.•The new model is fully learnable with substantially improved classification accuracy.•Four classifiers (GMM, SVM, RF, DCNN) were compared for the first time in CRF model.•Belief propagation was applied and assessed with alpha-expansion for the first time.