Arctic Sea Ice Image Super-Resolution Based on Multi-Scale Convolution and Dual-Gating Mechanism
Arctic Sea Ice Concentration (SIC) is the ratio of ice-covered area to the total sea area of the Arctic Ocean, which is a key indicator for maritime activities. Nowadays, we often use passive microwave images to display SIC, but it has low spatial resolution, and most of the existing super-resolution methods of Arctic SIC don't take the integration of spatial and channel features into account and can't effectively integrate the multi-scale feature. To overcome the aforementioned issues, we propose MFM-Net for Arctic SIC super-resolution, which concurrently aggregates multi-scale information while integrating spatial and channel features. Extensive experiments on Arctic SIC dataset from the AMSR-E/AMSR-2 SIC DT-ASI products from Ocean University of China validate the effectiveness of porposed MFM-Net.
Code (0)
등록된 구현이 없습니다.
Tasks
Image Super-ResolutionSuper-ResolutionSimilar Papers 제목 키워드 기반
Clustering Guided Domain-Specific Pretrained Foundation Model for Very High-Resolution Arctic Remote Sensing
This study introduces a novel Arctic-focused remote sensing foundation model (RSFM) by combining diversity-aware regional-scale image curation with masked autoencoder (MAE) self-supervised pretraining of a Vision Transfo…
DeepBedMap: Using a deep neural network to better resolve the bed topography of Antarctica
To better resolve the bed elevation of Antarctica, we present DeepBedMap – a novel machine learning method that produces realistic Antarctic bed topography from multiple remote sensing data inputs. Our super-resolution d…
Generative Adversarial NetworkSuper-ResolutionFour decades of circumpolar super-resolved satellite land surface temperature data
Land surface temperature (LST) is an essential climate variable (ECV) crucial for understanding land-atmosphere energy exchange and monitoring climate change, especially in the rapidly warming Arctic. Long-term satellite…
Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic Permafrost Features
This paper introduces a real-time GeoAI workflow for large-scale image analysis and the segmentation of Arctic permafrost features at a fine-granularity. Very high-resolution (0.5m) commercial imagery is used in this ana…
Instance SegmentationPositionPredictionSegmentation+1Automated Detection of Antarctic Benthic Organisms in High-Resolution In Situ Imagery to Aid Biodiversity Monitoring
Monitoring benthic biodiversity in Antarctica is vital for understanding ecological change in response to climate-driven pressures. This work is typically performed using high-resolution imagery captured in situ, though …
Data AugmentationObject Detection