Abstract
Satellite images act as windows to the world, providing us with detailed snapshots of Earth’s land surface. Utilizing various spectral bands, sensors aboard satellites detect and record information about different surface features, including vegetation, water bodies, urban areas, and barren land. By analyzing the spatial distribution and spectral characteristics of these features, scientists can classify and map land cover types with remarkable precision. The Landsat 8 satellites capture multispectral imagery with an image feature of 30 m for the optical spectrum, along with a thermal band at 100 m resolution. The revisit time for global coverage is approximately 16 days. The Sentinel program, operated by the ESA, includes multiple satellites such as S2 and S1. Sentinel-2 captures imagery at an analysis rate of 10 m for the optical spectrum, with repeatedly visiting in five days. S1 provides SAR data with an image analysis rate of 10–40 m, allowing imaging regardless of weather conditions or daylight. This paper proposes a deep learning model which will process different satellite imagery, i.e., a single DL model which process different satellite images for LULC identification. Here, the satellite images form S1, S2, and L8 for the year 2020 are processed using a single convolutional neural network model (CNN) for classification of LULC. Caatinga Forest Region, Brazil, was taken as the study area which includes three states Rio Grande Do Norte, Paraiba and Pernambuco. Deep neural networks are complex and powerful computational models inspired by the intricate structure and functionality of the human brain. These networks consist of multiple layers of interconnected artificial neurons, each performing its own calculation and contributing to the overall learning and decision-making process. Accuracy and Kappa analysis was done with each satellite images and combination of different satellite images using Google earth engine analysis. The detail study will boost the land cover classification task.
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Babitha, M.S., Diana Andushia, A., Mehathab, A. (2023). A Unified Deep Learning Model for Multi-Satellite Image Classification of Land Use and Land Cover. In: Shakya, S., Tavares, J.M.R.S., Fernández-Caballero, A., Papakostas, G. (eds) Fourth International Conference on Image Processing and Capsule Networks. ICIPCN 2023. Lecture Notes in Networks and Systems, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-99-7093-3_31
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