Improving remote sensing crop classification by argumentation-based conflict resolution in ensemble learning

The acquisition of data through remote sensing has become of great importance in precision agriculture, as it covers large geographical areas faster and cheaper than ground inspections. The challenge is to develop technical solutions that can benefit from both huge amounts of raw data extracted from satellite images, but also from the robust amount of knowledge refined during centuries of agricultural practice. Aiming to accurately classify crops from satellite images, we developed a hybrid intelligent system that can exploit both agricultural expert knowledge and machine learning algorithms. As the crop raw data is characterized by heterogeneity, we drive our attention to ensemble learners, while expert knowledge is encapsulated within a rule-based system. Vote-based methods for solving conflicts between ensemble's base learners have difficulties in classifying exceptional cases correctly and also to give the rationale behind their decision. The conceptual research question is on conflict resolution in ensemble learning. To deal with debatable cases in ensemble learning and to increase transparency in such debatable decisions, our hypothesis is that argumentation could be more effective than voting-based methods. The main contribution is that voting system in ensemble learning is substituted by an argumentation-base conflict resolutor. Prospective decisions of base classifiers are presented to an argumentative system based on defeasible logic that performs dialectical reasoning on pros and cons against a classification decision. The system computes a recommendation considering both the rules extracted from base learners and the available expert knowledge. The investigated case study deals with crop classification into four classes: corn, soybean, cotton, and rice. The test site used for the experiment is an area of 20 square kilometers in the New Madrid County, southeast of the Missouri State, USA. The results show that our approach increases classification accuracy compared to the voting-based method for conflict resolution in an ensemble learner comprising of three base classifiers: a decision tree, a neural network, and a support vector machine algorithm. We also argue that combining ensemble learning and argumentation fits the decision patterns of human agents, who first collect various opinions and then perform dialectical reasoning on these opinions. We think that the people who can benefit from the conceptual instrumentation presented in this work are decision makers in domains characterized by high data availability, robust expert knowledge, and a need for justifying the rationale behind decisions.