Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications
This addresses the challenge of accurate label acquisition in remote sensing for tasks like target detection and crop yield prediction, but it is incremental as it adapts existing multiple instance learning to fusion methods.
The paper tackled the problem of classifier and regression fusion with ambiguous or imprecise labels in remote sensing by proposing novel models based on multiple instance learning, showing effective performance in experiments on synthetic data and applications like target detection and crop yield prediction.
In classifier (or regression) fusion the aim is to combine the outputs of several algorithms to boost overall performance. Standard supervised fusion algorithms often require accurate and precise training labels. However, accurate labels may be difficult to obtain in many remote sensing applications. This paper proposes novel classification and regression fusion models that can be trained given ambiguosly and imprecisely labeled training data in which training labels are associated with sets of data points (i.e., "bags") instead of individual data points (i.e., "instances") following a multiple instance learning framework. Experiments were conducted based on the proposed algorithms on both synthetic data and applications such as target detection and crop yield prediction given remote sensing data. The proposed algorithms show effective classification and regression performance.