Supervised hyperspectral image (HSI) classification relies on accurate label information. However, it is not always possible to collect perfectly accurate labels for training samples. This motivates the development of classifiers that are sufficiently robust to some reasonable amounts of errors in data labels. Despite the growing importance of this aspect, it has not been sufficiently studied in the literature yet. In this paper, we analyze the effect of erroneous sample labels on probability distributions of the principal components of HSIs, and provide in this way a statistical analysis of the resulting uncertainty in classifiers. Building on the theory of imprecise probabilities, we develop a novel robust dynamic classifier selection (R-DCS) model for data classification with erroneous labels. Particularly, spectral and spatial features are extracted from HSIs to construct two individual classifiers for the dynamic selection, respectively. The proposed R-DCS model is based on the robustness of the classifiers’ predictions: the extent to which a classifier can be altered without changing its prediction. We provide three possible selection strategies for the proposed model with different computational complexities and apply them on three benchmark data sets. Experimental results demonstrate that the proposed model outperforms the individual classifiers it selects from and is more robust to errors in labels compared to widely adopted approaches.

The theory of multi-source information fusion plays a pivotal role in decision-making, especially when handling uncertain or imprecise information. Among the existing frameworks, evidence theory has proven effective for integrating diverse information sources to support informed decision-making. Recently, Random Permutation Set Theory (RPST), an extension of evidence theory, has shown significant practical value due to its ability to leverage the additional information inherent in event permutations. This insight inspires the utilization of permutation data to enhance the decision-making process. When employing RPST for decision-making and fusion, the order in which the fusion is performed can substantially influence the final results. To address this issue, we propose a novel approach that utilizes Fisher Scores to extract additional permutation information to guide decision-making within the RPST framework. Experimental results on the Iris dataset validate the feasibility and effectiveness of the proposed method. Compared to fusion methods employing weighted averaging, our approach, which leverages additional information to determine the fusion order, demonstrates superior accuracy across various training set proportions, achieving an accuracy of 96.26% at an 80% training set proportion. This provides an enhanced strategy for decision-making under uncertainty.

Decision making is still an open issue in the application of Dempster‐Shafer evidence theory. A lot of works have been presented for it. In the transferable belief model (TBM), pignistic probabilities based on the basic probability assignments are used for decision making. In this paper, multiscale probability transformation of basic probability assignment based on the belief function and the plausibility function is proposed, which is a generalization of the pignistic probability transformation. In the multiscale probability function, a factor q based on the Tsallis entropy is used to make the multiscale probabilities diversified. An example showing that the multiscale probability transformation is more reasonable in the decision making is given.