A Trusted Generative-Discriminative Joint Feature Learning Framework for Remote Sensing Image Classification

Remote sensing image (RSI) classification is a popular research topic that aims to assign semantic labels to images acquired from aerial or maritime platforms. Existing deep feature learning methods for this task can be divided into two paradigms: generative and discriminative. The former methods are good at capturing every local detail of images, while the later approaches focus on the most salient area. The significant differences between the two types of methods, both in terms of their underlying mechanisms and practical implementation, motivate us to integrate information acquired by both paradigms by exploiting their complementary strengths. However, this idea faces a challenge that local information in the extracted features, especially those from generative methods, may not be reliable for RSI classification. The reason for this challenge is that, due to the characteristics of the ground observation perspective, some RSIs, while semantically different, exhibit a significant degree of similarity in local details. This phenomenon leads to insufficient discriminability of local features to separate multiple RSI categories, which implies that the classification results overly focused on local information may be unreliable. To address this issue, in this article, we propose a novel framework that integrates generative and discriminative feature learning methods with evidential learning for RSI classification. Our framework uses the Dirichlet distribution to model the predicted probabilities to be integrated, thereby collecting evidence about their reliability. This enables us to integrate multiple features at an evidence level and make reliable decisions, overcoming the unreliabilities of generative-discriminative joint feature learning induced by RSI characteristics. We evaluate the proposed framework on several satellite and shipborne RSI classification datasets. The experimental results show that our method outperforms the state-of-the-art baselines in terms of accuracy and robustness.