Object detection and image segmentation with deep learning on earth observation data: A review-part i: Evolution and recent trends
Deep learning (DL) has great influence on large parts of science and increasingly
established itself as an adaptive method for new challenges in the field of Earth observation …
established itself as an adaptive method for new challenges in the field of Earth observation …
Object detection and image segmentation with deep learning on Earth observation data: A review—Part II: Applications
In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by
investigating aggregated classes. The increase in data with a very high spatial resolution …
investigating aggregated classes. The increase in data with a very high spatial resolution …
Small-object detection in remote sensing images with end-to-end edge-enhanced GAN and object detector network
The detection performance of small objects in remote sensing images has not been
satisfactory compared to large objects, especially in low-resolution and noisy images. A …
satisfactory compared to large objects, especially in low-resolution and noisy images. A …
A new spatial-oriented object detection framework for remote sensing images
Although the orientation and scale properties of the objects in remote sensing images have
been widely considered in the modern deep learning-based object detection methods, the …
been widely considered in the modern deep learning-based object detection methods, the …
Unsupervised human detection with an embedded vision system on a fully autonomous UAV for search and rescue operations
Unmanned aerial vehicles (UAVs) play a primary role in a plethora of technical and scientific
fields owing to their wide range of applications. In particular, the provision of emergency …
fields owing to their wide range of applications. In particular, the provision of emergency …
Vehicle detection from UAV imagery with deep learning: A review
Vehicle detection from unmanned aerial vehicle (UAV) imagery is one of the most important
tasks in a large number of computer vision-based applications. This crucial task needed to …
tasks in a large number of computer vision-based applications. This crucial task needed to …
How well do deep learning-based methods for land cover classification and object detection perform on high resolution remote sensing imagery?
Land cover information plays an important role in mapping ecological and environmental
changes in Earth's diverse landscapes for ecosystem monitoring. Remote sensing data have …
changes in Earth's diverse landscapes for ecosystem monitoring. Remote sensing data have …
DRBox-v2: An improved detector with rotatable boxes for target detection in SAR images
Q An, Z Pan, L Liu, H You - IEEE Transactions on Geoscience …, 2019 - ieeexplore.ieee.org
Convolutional neural network (CNN)-based methods have been successfully applied to
SAR target detection. Different from prevalently used detection approaches with rectangle …
SAR target detection. Different from prevalently used detection approaches with rectangle …
Object detection in remote sensing images based on improved bounding box regression and multi-level features fusion
The objective of detection in remote sensing images is to determine the location and
category of all targets in these images. The anchor based methods are the most prevalent …
category of all targets in these images. The anchor based methods are the most prevalent …
[HTML][HTML] Towards collaborative robotics in top view surveillance: A framework for multiple object tracking by detection using deep learning
Collaborative Robotics is one of the high-interest research topics in the area of academia
and industry. It has been progressively utilized in numerous applications, particularly in …
and industry. It has been progressively utilized in numerous applications, particularly in …