Fractal Neural Network Approach for Analyzing Satellite Images

Satellites play a critical role in modern technology by providing images for various applications, such as detecting infrastructure and assessing environmental impacts. The author’s work investigates the application of Fractal Neural Networks (FractalNet) for automating the detection of specific objects in satellite images. The study aims to improve processing speed and accuracy compared to traditional Convolutional Neural Networks (CNNs). The research involves developing and comparing FractalNet with CNNs, focusing on their effectiveness in image classification. The architecture of FractalNet, characterized by recursive structures and deep layers, is evaluated against CNNs like VGG16 and ResNet50. Data collection included manually gathering high-resolution satellite images of specific objects from Google Earth. The neural network models were trained and tested with varying hyperparameters, including learning rates and batch sizes. FractalNet demonstrated superior performance over CNNs, particularly in deep network configurations. The results improved significantly with data augmentation and optimized hyperparameters, achieving a test accuracy of up to 93.26% with a 32-layer model. Fractal neural networks offer a promising approach for automating satellite image analysis, providing better accuracy and robustness compared to traditional CNNs architectures.