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Large Selective Kernel Network for Remote Sensing Object Detection

IEEE International Conference on Computer Vision, 2023
Recent research on remote sensing object detection has largely focused on improving the representation of oriented bounding boxes but has overlooked the unique prior knowledge presented in remote sensing scenarios.
Yuxuan Li   +5 more
semanticscholar   +1 more source

RingMo: A Remote Sensing Foundation Model With Masked Image Modeling

IEEE Transactions on Geoscience and Remote Sensing, 2023
Deep learning approaches have contributed to the rapid development of remote sensing (RS) image interpretation. The most widely used training paradigm is to use ImageNet pretrained models to process RS data for specified tasks.
Xian Sun   +14 more
semanticscholar   +1 more source

Geologic Remote Sensing

Science, 1981
Remote-sensing techniques are now being used routinely in geologic interpretation for mineral and energy exploration, plant siting, waste disposal, and the development of models for regional and continental tectonics. New spaceborne methods and associated technologies are being developed to produce data from which geologic information about large areas
A F, Goetz, L C, Rowan
openaire   +2 more sources

REMOTE SENSING OPERATIONS

International Journal of Neural Systems, 1995
We show that Neural Networks can efficiently model multivalued transfer functions. We propose a method related to conditional density approximation p(y/x) and test the validity of the approach on a remote sensing problem.
F, Badran   +3 more
openaire   +2 more sources

Scale in Remote Sensing and GIS

, 2023
Introduction: Scale, Multiscaling, Remote Sensing, and GIS, M.F. Goodchild and D.A. Quattrochi Multiscale Nature of Spatial Data in Scaling Up Environmental Models, L. Bian Scale Dependence of NDVI and its Relationship to Mountainous Terrain, S.J. Walsh,
D. Quattrochi, M. Goodchild
semanticscholar   +1 more source

FFCA-YOLO for Small Object Detection in Remote Sensing Images

IEEE Transactions on Geoscience and Remote Sensing
Issues, such as insufficient feature representation and background confusion, make detection tasks for small object in remote sensing arduous. Particularly, when the algorithm will be deployed on board for real-time processing, which requires extensive ...
Yin Zhang   +5 more
semanticscholar   +1 more source

A Multilevel Multimodal Fusion Transformer for Remote Sensing Semantic Segmentation

IEEE Transactions on Geoscience and Remote Sensing
Accurate semantic segmentation of remote sensing data plays a crucial role in the success of geoscience research and applications. Recently, multimodal fusion-based segmentation models have attracted much attention due to their outstanding performance as
Xianping Ma   +3 more
semanticscholar   +1 more source

SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing

AAAI Conference on Artificial Intelligence, 2023
Remote sensing imagery, despite its broad applications in helping achieve Sustainable Development Goals and tackle climate change, has not yet benefited from the recent advancements of versatile, task-agnostic vision language models (VLMs).
Zhecheng Wang   +4 more
semanticscholar   +1 more source

RS3Mamba: Visual State Space Model for Remote Sensing Image Semantic Segmentation

IEEE Geoscience and Remote Sensing Letters
Semantic segmentation of remote sensing images is a fundamental task in geoscience research. However, convolutional neural networks (CNNs) and transformers have some significant shortcomings.
Xianping Ma, Xiaokang Zhang, Man-on Pun
semanticscholar   +1 more source

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