Currently, under supervised learning, a model pre-trained by a large-scale nature scene
dataset and then fine-tuned on a few specific task labeling data is the paradigm that has …

Breast cancer is a common malignancy and early detection and treatment of it is crucial.
Computer-aided diagnosis (CAD) based on deep learning has significantly advanced …

Semantic segmentation of very-high-resolution (VHR) remote sensing images plays an
important role in the intelligent interpretation of remote sensing since it predicts pixel-level …

Currently, the most advanced high-resolution remote sensing image (HRRSI) semantic
labeling methods rely on deep neural networks. However, HRRSIs naturally have a serious …

The more detailed and accurate earth observation has been made driven by the progress of
satellites and sensors optical photography technology, which poses both an opportunity and …

Over the past few years, there have been significant advancements in deep learning
technology, leading to remarkable progress in the field of image analysis. However, when it …

Remote sensing image registration can benefit from a machine learning method based on
the likelihood of predicting semantic spatial position distributions. Semantic segmentation of …

Geospatial object segmentation is a fundamental task in remote sensing image
interpretation. Although deep learning has shown great potential for this task, it often suffers …

Semantic segmentation is a fundamental but meaningful task in the remote sensing image
understanding community. Great progress has been made in optical sensor photography …

Multi-spectral semantic segmentation has shown great advantages under poor illumination
conditions, especially for remote scene understanding of autonomous vehicles, since the …