Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications

March 13, 2018 in Journal Papers

Abstract:

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.

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“ArXiv Code Code

Citation:

X. Du and A. Zare, "Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, pp. 2741-2753, May 2019.
@Article{Du2018Multiple
Title = {Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications},
Author = {Du, Xiaoxiao and Zare, Alina},
Journal = {IEEE Transactions on Geoscience and Remote Sensing},
Year = {2019},
Publisher={IEEE},
Pages = {2741-2753},
Month = {May},
doi = {10.1109/TGRS.2018.2876687},
}