Damaged buildings recognition of post-earthquake high-resolution remote sensing images based on feature space and decision tree optimization

Abstract

Multi-Agent system has broad application in real world, whose security performance, however, is barely considered. Reinforcement learning is one of the most important methods to resolve Multi-Agent problems. At present, certain progress has been made in applying Multi-Agent reinforcement learning to robot system, man-machine match, and automatic, etc. However, in the above area, an agent may fall into unsafe states where the agent may find it difficult to bypass obstacles, to receive information from other agents and so on. Ensuring the safety of Multi-Agent system is of great importance in the above areas where an agent may fall into dangerous states that are irreversible, causing great damage. To solve the safety problem, in this paper we introduce a Multi-Agent Cooperation Q-Learning Algorithm based on Constrained Markov Game. In this method, safety constraints are added to the set of actions, and each agent, when interacting with the environment to search for optimal values, should be restricted by the safety rules, so as to obtain an optimal policy that satisfies the security requirements. Since traditional Multi-Agent reinforcement learning algorithm is no more suitable for the proposed model in this paper, a new solution is introduced for calculating the global optimum state-action function that satisfies the safety constraints. We take advantage of the Lagrange multiplier method to determine the optimal action that can be performed in the current state based on the premise of linearizing constraint functions, under conditions that the state-action function and the constraint function are both differentiable, which not only improves the efficiency and accuracy of the algorithm, but also guarantees to obtain the global optimal solution. The experiments verify the effectiveness of the algorithm.

Relevant Features Independence of Heuristic Selection and Important Features of Decision Tree in the Medical Data Classification

“…The above results confirm that the feature selection using the feature's importance of the tree model which is done by researchers in [34,36,37] is not a good choice. The subset of important features in Table V are the input features when building the decision tree based on the approaches in [34,36,37]. The subset of relevant and independent features in Table VI has confirmed that both subset features have very different elements.…”

“…The extraction of useful service quality features by using a hybrid model of the Information System and the decision tree to enhance customer satisfaction and loyalty was conducted by Romalt and Kumar [36]. The feature importance of the Decision tree to acquire symbolized sets of damaged buildings as the relevant features only using post-earthquake information was conducted by Wang et al [37]. Finding the feature relevant by identifying and ranking the scores of feature importance generated by three techniques namely impurity-based, permutation-based, and Shap values for building a model to predict breast cancer was done by Mathew [38].…”

Land Cover Classification by Gaofen Satellite Images Based on CART Algorithm in Yuli County, Xinjiang, China

“…A decision tree (DT) is one of the most intuitively effective classifiers applied in the automatic identification of remote-sensing images. CART is one of the DT classification algorithms, and it is widely used in machine learning and artificial intelligence [14][15][16]. CART is an intelligent algorithm that uses recursive segmentation technology to build a prediction model, which analyzes the relationship between multiple attributes and decisions to generate easy-to-understand rules for prediction [15][16][17].…”

Earthquake-Damaged Buildings Detection in Very High-Resolution Remote Sensing Images Based on Object Context and Boundary Enhanced Loss

“…Timely and accurately acquiring of earthquake damage information of buildings based on remote sensing images is of great significance for post-earthquake emergency response and post-disaster reconstruction [1,2]. Different from common urban scenes, post-earthquake remote sensing images contain rather complex structures and spatial arrangement and earthquake-damaged buildings with diversified patterns are mingled with undamaged buildings, which greatly challenges the abstract representation and feature modeling of earthquake-damaged buildings [3,4]. The automatic detection technology for earthquake-damaged buildings using very high-resolution (VHR) remote sensing images has become a research hotspot in computer vision.…”