Cloud Detection in Satellite Imagery Using Deep Convolutional Neural Networks (CNN)

The advent of Convolutional Neural Networks (CNNs) has significantly impacted the field of object detection in remote sensing. This study explores the efficacy of CNN-based techniques in various everyday items, including electronic object detection. The dataset used in the research was meticulously assembled, incorporating diverse conditions such as scale, orientation, lighting, and background. The dataset was then subjected to data preprocessing, annotation, and model development, ensuring its quality and fidelity. The CNN model underwent an exhaustive 50 epochs of training, achieving an impressive 95% training accuracy and 96% validation accuracy. The model's precision, recall, and precision were also impressive, with a recall score of 95%. The CNN model's performance was compared with other prominent algorithms, highlighting its transformative potential in remote sensing object detection. The Cloud and Non-Cloud Image Dataset, a curated collection of 1600 high-resolution images from Planet Labs satellite data, serves as a valuable resource for advancing remote sensing research and development. The dataset was enriched through data loading and resizing, normalization, data splitting, augmentation techniques, label encoding, and training-validation split. The development of a sophisticated deep learning model, utilizing convolutional neural networks (CNNs), was central to the project's success. The model's training and validation data showed impressive accuracy, recall, and precision rates, surpassing traditional algorithms. The findings have implications for various applications, such as weather forecasting and environmental monitoring, where accurate cloud identification is crucial. Overall, this study contributes valuable insights and methodologies to the fields of remote sensing and image analysis.