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220822 ||| eng |
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|a 9783036542300
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|a books978-3-0365-4230-0
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|a 9783036542294
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1 |
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|a Ulhaq, Anwaar
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245 |
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|a Advances in Object and Activity Detection in Remote Sensing Imagery
|h Elektronische Ressource
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260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
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300 |
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|a 1 electronic resource (170 p.)
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653 |
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|a urban vegetation
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653 |
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|a feature pyramid transformer
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653 |
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|a synthetic crowd data
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653 |
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|a deep learning (DL)
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|a urban forestry
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|a feature pyramid network (FPN)
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|a unmanned aerial vehicle
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|a n/a
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|a air-to-ground synchronization
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|a deep learning
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|a History of engineering & technology / bicssc
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|a UAV-assisted calibration
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|a multi-camera system
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|a Technology: general issues / bicssc
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|a dynamic feature refinement
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|a YOLOv3
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|a invasive species
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|a crowd estimation
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|a view-invariant description
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|a ship detection
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|a habitat identification
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|a multiview semantic vegetation index
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|a tidal flat water
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|a thermal imaging
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|a synthetic aperture radar (SAR)
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|a drone
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|a green view index (GVI)
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|a convolutional neural network (CNN)
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|a quad feature pyramid network (Quad-FPN)
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|a semantic segmentation
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|a similarity algorithm for water extraction
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|a cross-view matching
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|a spatiotemporal feature map
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|a space alignment
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|a 3D simulation
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|a arbitrary-oriented object detection in satellite optical imagery
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|a RGB vegetation index
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653 |
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|a adaptive dynamic refined single-stage transformer detector
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700 |
1 |
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|a Gomes, Douglas Pinto Sampaio
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700 |
1 |
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|a Ulhaq, Anwaar
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700 |
1 |
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|a Gomes, Douglas Pinto Sampaio
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041 |
0 |
7 |
|a eng
|2 ISO 639-2
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989 |
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|b DOAB
|a Directory of Open Access Books
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500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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028 |
5 |
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|a 10.3390/books978-3-0365-4230-0
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856 |
4 |
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|u https://www.mdpi.com/books/pdfview/book/5540
|7 0
|x Verlag
|3 Volltext
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856 |
4 |
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|u https://directory.doabooks.org/handle/20.500.12854/84556
|z DOAB: description of the publication
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|a 900
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|a 600
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|a 620
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|a The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms.
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