Advances in Object and Activity Detection in Remote Sensing Imagery

Edited by
May 2022
170 pages
  • ISBN978-3-0365-4229-4 (Hardback)
  • ISBN978-3-0365-4230-0 (PDF)

This book is a reprint of the Special Issue Advances in Object and Activity Detection in Remote Sensing Imagery that was published in

Environmental & Earth Sciences

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.

  • Hardback
© 2022 by the authors; CC BY-NC-ND license
multi-camera system; space alignment; UAV-assisted calibration; cross-view matching; spatiotemporal feature map; view-invariant description; air-to-ground synchronization; tidal flat water; YOLOv3; similarity algorithm for water extraction; arbitrary-oriented object detection in satellite optical imagery; adaptive dynamic refined single-stage transformer detector; feature pyramid transformer; dynamic feature refinement; synthetic aperture radar (SAR); ship detection; convolutional neural network (CNN); deep learning (DL); feature pyramid network (FPN); quad feature pyramid network (Quad-FPN); crowd estimation; 3D simulation; unmanned aerial vehicle; synthetic crowd data; invasive species; thermal imaging; habitat identification; deep learning; drone; multiview semantic vegetation index; urban forestry; green view index (GVI); semantic segmentation; urban vegetation; RGB vegetation index; n/a