Chongming Island Remote Sensing Image Classification Using Bayesian Minimum Error Rate Algorithm

This program implements Chongming Island remote sensing image classification using the Bayesian Minimum Error Rate algorithm. The system performs pixel-wise classification based on statistical pattern recognition principles, where each pixel is assigned to the land cover category that minimizes the probability of misclassification. The algorithm employs maximum a posteriori (MAP) decision rule by comparing posterior probabilities derived from prior distributions and class-conditional probability density functions (typically modeled using Gaussian distributions). The classification results are visualized through an intuitive pie chart that displays proportional distribution of different land cover types (such as water bodies, vegetation, urban areas, etc.). This quantitative analysis helps users better understand land cover patterns across Chongming Island and supports related research studies. The implementation includes key functions for feature extraction, training data processing, and Bayesian classifier training. The user-friendly interface simplifies operations with interactive parameter adjustments and real-time result visualization. Both professional researchers and general users can efficiently obtain accurate remote sensing classification information, providing valuable references for decision-making and regional planning. The program ensures reliable classification outcomes through rigorous probability modeling and error minimization techniques, making it suitable for environmental monitoring and land use analysis applications.