Remote Sensing Image Classification Using BP Neural Network

Application of BP Neural Networks in Remote Sensing Image Classification

Remote sensing image classification serves as a critical technique for analyzing land cover types. BP neural networks demonstrate distinct advantages in processing high-dimensional remote sensing data due to their powerful nonlinear mapping capabilities. To address the classification requirements for ten typical land cover types in Dongtan, Chongming Island, the following technical workflow can be implemented:

Data Preprocessing Phase First, spectral features need to be extracted from remote sensing imagery, which may include reflectance values from visible light bands, near-infrared channels, etc. Different land cover types (e.g., water bodies, vegetation, buildings) exhibit unique spectral curves, and these features will form the input dimensions for the neural network. In code implementation, this typically involves using libraries like GDAL or Rasterio for pixel value extraction and NumPy for feature matrix normalization.

Network Architecture Key Points A three-layer BP network structure (input-hidden-output) is adopted. The input layer size corresponds to the spectral feature dimensions, while the output layer contains 10 nodes representing the ten land cover categories. The hidden layer node count is determined through cross-validation. Activation functions like Sigmoid or ReLU are commonly selected to introduce nonlinearity. Code implementation would involve defining the network architecture using frameworks such as TensorFlow/Keras with Dense layers, specifying activation functions in each hidden layer.

Training Optimization Strategies When adjusting weights through backpropagation, dynamic learning rate adaptation (e.g., using Adam optimizer) and batch normalization are essential to prevent convergence issues caused by dimensional differences in spectral data. Early stopping techniques should be employed to avoid overfitting. In practice, this translates to implementing callback functions in Keras for learning rate scheduling and early stopping based on validation loss monitoring.

Classification Implementation Flow The trained network can process spectral feature vectors of pixels to be classified, where the category corresponding to the maximum response value in the output layer becomes the prediction result. The final output is a classification map of the entire image, with accuracy assessment conducted through confusion matrices for specific land cover types like wetlands and tidal flats. Programmatically, this involves using argmax() operations on the output layer and generating classification maps using raster processing libraries.

This method's advantage lies in its ability to autonomously learn complex spectral-land cover relationships, particularly suitable for coastal wetland environments like Dongtan where mixed pixels are common. Future enhancements could incorporate convolutional neural networks to improve spatial feature utilization.