A Genetic Algorithm Based on Deep Q-learning in Optimization of Remote Sensing Data Discretization

Feature discretization can improve the processing efficiency of remote sensing big data. However, the distribution of target attribute values is often difficult to ascertain, and there are complex correlations between features, making it extremely difficult to obtain an optimal discretization scheme for remote sensing data. Although feature discretization methods based on evolutionary models can achieve some considerable results, it is difficult to formulate appropriate strategies without prior knowledge as guidance, which makes searching in multidimensional space inefficient and prone to falling into local optima. Therefore, we propose a genetic algorithm based on deep Q-learning (DQLGA) to optimize the discretization scheme of remote sensing data. First, we design a state set in the crossover and mutation stages by balancing the global and local search capabilities of genetic operators to obtain an accurate mapping of states in high-dimensional feature space. Then, we simulate the variation pattern of the optimal discretization scheme by analyzing the distribution of all individuals in the population during the evolution process to construct a reasonable adaptive reward function in reinforcement learning model. Finally, we introduce a pair of deep Q-networks with the same structure by treating the calculation of Q-values as a function fitting problem to fulfil an efficient updating of massive Q-values in state transition. We compare DQLGA with state-of-the-art feature discretization methods on remote sensing data. The experimental results indicate that DQLGA significantly improves the search efficiency of feature discretization methods based on evolutionary models, achieving higher classification accuracy while further reducing the number of breakpoints.