Results 241 to 250 of about 65,450 (292)

A Spatial Consistency-Guided Sampling Algorithm for UAV Remote Sensing Heterogeneous Image Matching. [PDF]

Sensors (Basel)
Chen R, Lv H, Zhou J, Chen Z, Li T, Zhang X, Li Y, Zhan Z.   +7 more
europepmc   +1 more source

Multimodal Building Damage Assessment Method Fusing Adaptive Attention Mechanism and State-Space Modeling. [PDF]

Sensors (Basel)
Zhu R, Lan X.
europepmc   +1 more source

A bibliometric review of deep learning in crop monitoring: trends, challenges, and future perspectives. [PDF]

Front Artif Intell
Zhang R, Wu X, Li J, Zhao P, Zhang Q, Wuri L, Zhang D, Zhang Z, Yang L.   +8 more
europepmc   +1 more source

ABSTRACTS

Precision Radiation Oncology, EarlyView.
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Self-Supervised Remote Sensing Image Retrieval

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020
Current remote sensing platforms generate a vast amount of imagery but the best current methods to index and retrieve that data require expensive and difficult to procure labels. In this paper, we aim to address this problem by presenting a performant content based image retrieval (CBIR) system that is capable of indexing and retrieval using only ...
Kane Walter, Matthew J. Gibson, Arcot Sowmya   +2 more
openaire   +1 more source

Content-based retrieval for remotely sensed images

SPIE Proceedings, 2001
The work describes an innovative technique to automatically extract and manage remote sensing image-content. Simple but very flexible numeric recognition methodologies allow the content-based retrieval from huge remotely sensed image database. The most important result of this methodology is a tool for the information retrieval based on example.
Michele Bruzzo, Ferdinando Giordano, Laura Pagani, DELLEPIANE, SILVANA, Giancarlo Bo   +4 more
openaire   +3 more sources

Deep Hash Learning for Remote Sensing Image Retrieval

IEEE Transactions on Geoscience and Remote Sensing, 2021
The content-based remote sensing image retrieval (CBRSIR) has attracted increasing attention with the number of remote sensing (RS) images growing explosively. Benefiting from the strong capacity of the deep convolutional neural network (DCNN), the performance of CBRSIR has been improved in recent years.
Chao Liu, Jingjing Ma, Xu Tang, Fang Liu, Xiangrong Zhang, Licheng Jiao   +5 more
openaire   +1 more source