BP Neural Network-Based Remote Sensing Image Classification

This project provides a MATLAB implementation of remote sensing image classification utilizing Backpropagation (BP) Neural Networks. The workflow begins with extracting spectral characteristics from ten distinct land cover types in sample images of Chongming Dongtan. These spectral features serve as input vectors for training the BP neural network, where the network learns to recognize patterns through iterative weight adjustments using gradient descent algorithms. Key implementation aspects include feature normalization, network architecture configuration (hidden layer sizing), and training parameter optimization. Once trained, the network demonstrates classification capability by processing new remote sensing imagery through forward propagation, outputting land cover type probabilities. This codebase offers practical insights into both neural network application and remote sensing analysis techniques. The implementation typically involves: - Data preprocessing: Reading multispectral image data and labeling training samples - Feature engineering: Calculating mean spectral values across different bands for each land cover category - Network configuration: Setting up input/output layer dimensions based on feature dimensions and class numbers - Training phase: Implementing backpropagation with momentum to avoid local minima - Classification: Using the trained network's prediction function on new image pixels