Leveraging deep learning for change detection in bi-temporal remote sensing imagery
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Deforestation in the Brazilian Amazon poses significant threats to global cli- mate stability, biodiversity, and local communities. This dissertation presents ad- vanced deep learning approaches to improve deforestation detection using bi-temporal Sentinel-2 satellite imagery. We developed a specialized dataset capturing deforesta- tion events between 2020 and 2021 in key conservation units of the Amazon.We first adapted transformer-based change detection models to the deforestation context, leveraging attention mechanisms to analyze spatial and temporal patterns. While these models showed high accuracy, limitations remained in effectively captur- ing subtle environmental changes. To address this, we introduce DeforestNet, a novel deep learning framework that integrates advanced semantic segmentation encoders within a siamese architecture. DeforestNet employs cross-temporal interaction mechanisms and temporal fusion strategies to enhance the discrimination of true deforestation events from background noise. Experimental results demonstrate that DeforestNet outperforms existing mod- els, achieving higher precision, recall, and F1-scores in deforestation detection. Ad- ditionally, it generalizes well to other change detection tasks, as evidenced by its performance on the LEVIR-CD urban building change detection dataset. This research contributes a robust and efficient framework for accurate change detection in remote sensing imagery, offering valuable tools for environmental moni- toring and aiding global efforts in sustainable forest management and conservation.
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Ph. D.