BiLSTM Classifier Model for Land Cover Change Detection and Classification

Abstract

Objective: This research proposes a change detection in the satellite images and land cover analysis using a Bidirectional Long Short-Term Memory (BiLSTM) model and vegetation index-based feature maps for future map generation. Change detection complements land cover analysis by identifying and quantifying alterations in land cover over time.   Theoretical framework: Land cover analysis and land cover change detection plays crucial role in understanding and monitoring the Earth's surface.Despite its importance, land cover analysis and change detection face several challenges. One major challenge is obtaining accurate and up-to-date information on land cover and change detection  across large areas can be complex and costly. Inconsistent data sources and limited access to historical records can hinder the accuracy and reliability of change detection analyses.These challenges requires a combination of technological advancements, improved data availability, refined algorithms, robust validation approaches, and interdisciplinary collaborations.   Method: BiLSTM method is used for implementation which is  powerful for land cover classification, as it allowing the integration of spatial and temporal information and capturing complex patterns in satellite imagery data. BiLSTM, tend to be more complex but they often offer higher accuracy and the ability to learn intricate patterns and representations.   Results and conclusion: The six vegetation index-based feature maps are considered.Therefore, the resulting accuracy is also determined using the Flamingo-Hyena optimization (FHO), and the experimental outcomes disclosed that the proposed model is superior to the existing model in terms of accuracy with 0.95%, MSE with 0.05%, precision with 0.94%, Recall with 0.94%, and F1 measure with 0.94% respectively. vegetation index-based feature maps are essential for land cover analysis and change detection as they provide valuable information on vegetation dynamics, ecological processes, land management, and climate change impacts.   Research implications: This process land cover analysis and change detection helps to detect deforestation, urban expansion, agricultural expansion, and other land cover  changes. By harnessing these techniques, policy makers can address emerging issues such as deforestation, loss of biodiversity, and encroachment on natural habitats.