Geographical Knowledge-Driven Representation Learning for Remote Sensing Images
نویسندگان
چکیده
The proliferation of remote sensing satellites has resulted in a massive amount images. However, due to human and material resource constraints, the vast majority images remain unlabeled. As result, it cannot be applied currently available deep learning methods. To fully utilize remaining unlabeled images, we propose Geographical Knowledge-driven Representation (GeoKR) method for improving network performance reduce demand annotated data. global land cover products geographical location associated with each image are regarded as knowledge provide supervision representation pretraining. An efficient pretraining framework is proposed eliminate noises caused by imaging times resolutions difference between knowledge. A large-scale dataset Levir-KR constructed support It contains 1431950 from Gaofen series various resolutions. Experimental results demonstrate that our outperforms ImageNet self-supervised methods significantly reduces burden data annotation on downstream tasks, such scene classification, semantic segmentation, object detection, cloud/snow detection. demonstrates can used novel paradigm neural networks. Codes will https://github.com/flyakon/Geographical-Knowledge-driven-Representaion-Learning.
منابع مشابه
Tensor Representation and Manifold Learning Methods for Remote Sensing Images
One of the main purposes of earth observation is to extract interested information and knowledge from remote sensing (RS) images with high efficiency and accuracy. However, with the development of RS technologies, RS system provide images with higher spatial and temporal resolution and more spectral channels than before, and it is inefficient and almost impossible to manually interpret these im...
متن کاملKnowledge-driven Information Mining in Remote-Sensing Image Archives
attributes Involving a significant amount of high-level reasoning about the meaning and purpose of the objects or scenes depicted (e.g. illegal plantation). They are outside the scope of the current research activity. Figure 2. Logical representation of data and derived attributes extraction of primitive features and the reduction of the resulting data into primitive feature clusters. The primi...
متن کاملKnowledge Based Interpretation of Multisensor and Multitemporal Remote Sensing Images
The increasing amount of remotely sensed imagery from multiple platforms requires efficient analysis techniques. The leading idea of the presented work is to automate the interpretation of multisensor and multitemporal remote sensing images by the use of common prior knowledge about landscape scenes. The presented system is able to use specific map knowledge of a geoinformation system (GIS), in...
متن کاملRemote Sensing and Geographical Information Systems
Today remote sensing and Geographical Information Systems (GIS) appear quite regularly in the media. Geographical Information Systems are credited with almost magical qualities, whereas "cutting edge" satellite imagery is reputed to be able to do remarkable things such as, for example, reading from space the license plate of a military vehicle. It does not take much imagination to envision how ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Geoscience and Remote Sensing
سال: 2022
ISSN: ['0196-2892', '1558-0644']
DOI: https://doi.org/10.1109/tgrs.2021.3115569