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The uniformity principle of temperature difference fields (TDFs) is applied in this study to improve the constructal optimization for "volume-point" heat conduction based on entransy dissipation rate (EDR) minimization without the premise of an optimal last-order construct, and the constructal optimization algorithm based on EDR minimization is simplified in this paper. The results further prove that the uniformity principle of TDF is consistent with entransy theory. The constructal optimization of "volume-point" heat conduction based on EDR minimization is conducted not only to lower the average temperature but also to obtain a more uniform TDF distribution. Through comparing the optimal results based on EDR minimization without the premise of an optimal last-order construct with those based on maximum temperature difference (MTD) minimization, some criteria and formulas for designing conductivity paths based on EDR minimization and MTD minimization are proposed, and the idea and method of improving constructal optimization via the variational principle are proposed. [ABSTRACT FROM AUTHOR]

Remote sensing image scene classification (RSISC), which aims to classify scene categories for remote sensing imagery, has broad applications in various fields. Recent deep learning (DL) successes have led to a new wave of RSISC applications; however, they lack explainability and trustworthiness. Here, we propose a bidirectional flow decision tree (BFDT) module to create a reliable RS scene classification framework. Our algorithm combines BFDT and Convolutional Neural Networks (CNNs) to make the decision process easily interpretable. First, we extract multilevel feature information from the pretrained CNN model, which provides the basis for constructing the subsequent hierarchical structure. Then the model uses the discriminative nature of scene features at different levels to gradually refine similar subsets and learn the interclass hierarchy. Meanwhile, the last fully connected layer embeds decision rules for the decision tree from the bottom up. Finally, the cascading softmax loss is used to train and learn the depth features based on the hierarchical structure formed by the tree structure that contains rich remote sensing information. We also discovered that superclass results can be obtained well for unseen classes due to its unique tree structure hierarchical property, which results in our model having a good generalization effect. The experimental results align with theoretical predictions using three popular datasets. Our proposed framework provides explainable results, leading to correctable and trustworthy approaches. [ABSTRACT FROM AUTHOR]

Crowd anomaly detection is a practical and challenging problem to computer vision and VideoGIS due to abnormal events' rare and diverse nature. Consequently, traditional methods rely on low-level reconstruction in a single image space, easily affected by unimportant pixels or sudden variations. In addition, real-time detection for crowd anomaly detection is challenging, and localization of anomalies requires other supervision. We present a new detection approach to learn spatiotemporal features with the spatial constraints of a still dynamic image. First, a lightweight spatiotemporal autoencoder has been proposed, capable of real-time image reconstruction. Second, we offer a dynamic network to obtain a compact representation of video frames in motion, reducing false-positive anomaly alerts by spatial constraints. In addition, we adopt the perturbation visual interpretation method for anomaly visualization and localization to improve the credibility of the results. In experiments, our results provide competitive performance across various scenarios. Besides, our approach can process 52.9–63.4 fps in anomaly detection, making it practical for crowd anomaly detection in video surveillance. [ABSTRACT FROM AUTHOR]