Enhancing remote sensing satellite image classification through parameter optimization in hybrid CNN-LSTM model

Abstract

Satellite imagery is a vital resource in Earth observation, offering valuable insights for applications ranging from environmental monitoring to disaster response. In the pursuit of accurate and robust classification, we present a study focused on parameter optimization within the framework of hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models. This research seeks to maximize the potential of hybrid CNN-LSTM architecture, which inherently combine spatial feature extraction capabilities of CNNs with LSTM’s ability to model temporal dependencies. We delve into a systematic exploration of hyper-parameters, including the number of CNN layers, LSTM units, learning rates, and dropout rates, fine-tuning them to achieve peak classification performance. These parameters are meticulously adjusted and validated on a diverse and representative satellite image dataset. Beyond hyper-parameter optimization, we address the challenge of class imbalance frequently encountered in satellite image datasets. By incorporating specialized techniques during the optimization process, we mitigate class imbalance effects and bolster the model’s accuracy. The study’s findings not only unveil optimal parameter configurations but also shed light on the intricate interplay between architecture complexity and model performance. The resulting optimized hybrid model demonstrates exceptional classification accuracy, validating its applicability to real-world remote sensing tasks.