Autoencoder-Based Architecture for Forest Cover Type Classification

The classification of forest cover types is a vital component of environmental management, conservation, and ecological studies. Forests, with their diverse flora and fauna, play a pivotal role in maintaining the planet’s ecological balance. Therefore, precise, and efficient forest type classification based on spectral characteristics is of paramount importance. In this research, an autoencoder-based deep architecture is proposed for forest cover type classification. This research seeks to address the challenges inherent in leveraging spectral data for this purpose. Spectral data is rich with nuanced information that can be challenging to interpret and classify accurately. The Autoencoder-Based Architecture, a deep learning model, demonstrates ability to decipher these intricate spectral features. Autoencoder-based architecture demonstrated a classification accuracy of 98.2