Hitherto, image-level classification on remote sensing landslide images has been paid attention to, but the accuracy of traditional deep learning-based methods still have room for improvement. The evidence theory is found efficient to boost the accuracy of neural networks, however, the present study argues three challenges that hinder the lead-in of this theory in deep landslide image classification. Aiming at the three problems, this study makes three improvements. For the interpretability and decision-invariance losses of three previous divergences, we propose a belief Jensen–Renyi divergence with properties proven. To couple the evidence theory with deep remote sensing landslide image classification, a channelwise multiscale visual saliency fusion is developed. We additionally find that the channelwise fusion is capable to reduce false recognition of networks as compared with original RGB images. To avoid decision failures in evidence-theoretic fusion process, we design an interpretability improved three-branched fusion. Experiments on Bijie Landslide dataset corroborate the synergistic benefits of the three improvements, where the proposal is compared with state-of-the-art image classification backbone networks, remote sensing image scene classifiers, evidence fusion algorithms, and versatile evidence-theoretic deep learning classifiers. We also evaluated the new method with two sort of image degradation, as well as an actual scenario in Luding County, China, whose data is publicly available.

The traditional Pignistic transformation is limited in the context of “betting”, which faces information loss and is inconvenient for multi-source information fusion. To tackle this challenge, an Enhanced Pignistic transformation is proposed for the first time. New divergence and information volume measures are tailor-made for the enhanced Pignistic probability, and a novel information fusion algorithm is developed. To further prove the fusion algorithm’s advantages in conflict management, it is applied in a new semi-automatic image segmentation scheme. Two uncertain decision-support techniques named adaptive belief assignment and scalable information extraction are raised, and a fuzzy heuristic refinement algorithm is conducted, fulfilling the gap between evidential decision-making and segmentation refinement. Experimental analysis shows the proposed segmentation algorithm is superior on four metrics each and can enhance the robustness of foreground segmentation, indicating the effectiveness of the proposal in solving the decision inaccuracy of evidential segmentation schemes.

Belief divergence is a significant measure to quantify the discrepancy between evidence, which is beneficial for conflict information management in Dempster–Shafer evidence theory. In this article, three new concepts are given, namely, the belief Bhattacharyya coefficient, adjustment function, and enhancement factor. And based on them, a novel enhanced belief divergence, called EBD, is proposed, which can assess the correlation of subsets and fully reflect the uncertainty of multielement sets. The important properties of the EBD have been studied. In particular, a new EBD-based multisource information fusion method is designed to handle evidence conflict, where the weight of evidence is decided by the EBD between evidence and the information volume of each evidence. Compared with other methods, the proposed method in the applications of target recognition and iris classification can produce more rational and telling outcomes when dealing with conflict information. Finally, an application in risk priority evaluation of the failure modes of rotor blades of an aircraft turbine is provided to validate that the proposed method has the extensive applicability.

Highly conflicting evidence that may lead to the counter-intuitive results is one of the challenges for information fusion in Dempster-Shafer evidence theory. To deal with this issue, evidence conflict is investigated based on belief divergence measuring the discrepancy between evidence. In this paper, the pignistic probability transform belief χ2 divergence, named as BBχ2 divergence, is proposed. By introducing the pignistic probability transform, the proposed BBχ2 divergence can accurately quantify the difference between evidence with the consideration of multi-element sets. Compared with a few belief divergences, the novel divergence has more precision. Based on this advantageous divergence, a new multi-source information fusion method is devised. The proposed method considers both credibility weights and information volume weights to determine the overall weight of each evidence. Eventually, the proposed method is applied in target recognition and fault diagnosis, in which comparative analysis indicates that the proposed method can realize the highest accuracy for managing evidence conflict.