Advances in Object and Activity Detection in Remote Sensing Imagery

• Autore: Ulhaq Anwaar
• Pubblicazione: Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
• Descrizione fisica: 1 online resource (170 p.)
• Soggetto topico: History of engineering & technology; Technology: general issues
• Soggetto non controllato: 3D simulation; adaptive dynamic refined single-stage transformer detector; air-to-ground synchronization; arbitrary-oriented object detection in satellite optical imagery; convolutional neural network (CNN); cross-view matching; crowd estimation; deep learning; deep learning (DL); drone; dynamic feature refinement; feature pyramid network (FPN); feature pyramid transformer; green view index (GVI); habitat identification; invasive species; multi-camera system; multiview semantic vegetation index; n/a; quad feature pyramid network (Quad-FPN); RGB vegetation index; semantic segmentation; ship detection; similarity algorithm for water extraction; space alignment; spatiotemporal feature map; synthetic aperture radar (SAR); synthetic crowd data; thermal imaging; tidal flat water; UAV-assisted calibration; unmanned aerial vehicle; urban forestry; urban vegetation; view-invariant description; YOLOv3
• Persona (resp. second.): GomesDouglas Pinto Sampaio; UlhaqAnwaar
• Sommario/riassunto: 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.
• Titolo autorizzato: Advances in Object and Activity Detection in Remote Sensing Imagery
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910576871603321
• Lo trovi qui: Univ. Federico II