Atrous convolutions and ConvLSTM fusion for multi-scale feature learning in EuroSAT satellite imagery
Year: 2026
Type: article
Abstract: Satellite imagery classification plays a crucial role in land cover monitoring, urban planning, and environmental conservation. This paper presents a deep learning architecture designed to improve multi-scale feature learning for satellite image classification on the EuroSAT dataset. The model combines a ResNet50 backbone with atrous convolutions to capture multi-scale contextual information across multiple spectral bands, essential for accurately differentiating complex land-cover classes. The Convolutional Block Attention Module (CBAM) refines the spatial and channel features, focusing on details critical to classification. The Convolutional Long Short-Term Memory (ConvLSTM) module is incorporated as a recurrent spatial refinement stage operating over latent feature representations. Its gated update mechanism enables iterative feature modulation while preserving spatial structure, providing a alternative to purely feed-forward refinement strategies. The model achieves a mean Top-1 accuracy of approximately 97% across multiple random seeds, with a peak accuracy of 98%.
Source: Complex & Intelligent Systems
Institution: Vellore Institute of Technology University
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
OpenAlex ID: W7172291380
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