Neural Network Source Code for Remote Sensing Image Classification Applications
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Resource Overview
Neural network source code implementable for remote sensing image classification, featuring methods including Backpropagation (BP) and Kohonen algorithms with detailed architecture specifications.
Detailed Documentation
The neural network source code supports multiple methodologies applicable to remote sensing image classification, including Backpropagation (BP) algorithms for multilayer perceptron training and Kohonen self-organizing maps for unsupervised clustering. Implementation typically involves defining network layers with activation functions (e.g., sigmoid/tanh for BP) and distance-based neuron competition for Kohonen. Additionally, developers can extend functionality by integrating convolutional neural networks (CNNs) for spatial feature extraction using convolutional layers and pooling operations, or recurrent neural networks (RNNs) with LSTM/GRU units for temporal sequence analysis in multi-temporal imagery. These advanced architectures enhance classification accuracy through automated feature learning and context preservation.
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