scholarly journals Editorial for the Special Issue “Remote Sensing of Target Detection in Marine Environment”

2019 ◽  
Vol 11 (14) ◽  
pp. 1689
Author(s):  
Ferdinando Nunziata ◽  
Armando Marino ◽  
Domenico Velotto
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Marine Environment ◽  
Special Issue ◽  
Amount Of Information

Remote sensing is a powerful tool used to obtain an unprecedented amount of information about the ocean from a distance, usually from satellites or aircrafts [...]

Measuring Exposome Associated with Health

2020 ◽  
pp. 46-50
Keyword(s):  
Mass Spectrometry ◽  
Remote Sensing ◽  
High Resolution ◽  
Biological Effect ◽  
High Resolution Mass Spectrometry ◽  
The Body ◽  
Ecological Environment ◽  
Special Issue ◽  
The Life Course ◽  
Resolution Mass

The concept of exposome has received increasing discussion, including the recent Special Issue of Science –"Chemistry for Tomorrow's Earth,” about the feasibility of using high-resolution mass spectrometry to measure exposome in the body, and tracking the chemicals in the environment and assess their biological effect. We discuss the challenges of measuring and interpreting the exposome and suggest the survey on the life course history, built and ecological environment to characterize the sample of study, and in combination with remote sensing. They should be part of exposomics and provide insights into the study of exposome and health.

scholarly journals Editorial for Special Issue “Remote Sensing in Applications of Geoinformation”

2020 ◽  
Vol 13 (1) ◽  
pp. 33
Author(s):  
Silas Michaelides
Keyword(s):  
Remote Sensing ◽  
Special Issue ◽  
Diffusion Of Knowledge

The diffusion of knowledge and information is currently more forceful than ever [...]

scholarly journals Editorial for Special Issue “Monitoring Land Subsidence Using Remote Sensing”

2021 ◽  
Vol 13 (9) ◽  
pp. 1771
Author(s):  
Massimo Fabris ◽  
Nicola Cenni ◽  
Simone Fiaschi
Keyword(s):  
Remote Sensing ◽  
Land Subsidence ◽  
Geological Hazard ◽  
Special Issue ◽  
The World

Land subsidence is a geological hazard that affects several different communities around the world [...]

scholarly journals Editorial for the Special Issue “Advanced Artificial Intelligence and Deep Learning for Remote Sensing”

2021 ◽  
Vol 13 (15) ◽  
pp. 2883
Author(s):  
Gwanggil Jeon
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Deep Learning ◽  
Special Issue ◽  
The Earth

Remote sensing is a fundamental tool for comprehending the earth and supporting human–earth communications [...]

scholarly journals Improved YOLO Network for Free-Angle Remote Sensing Target Detection

2021 ◽  
Vol 13 (11) ◽  
pp. 2171
Author(s):  
Yuhao Qing ◽  
Wenyi Liu ◽  
Liuyan Feng ◽  
Wanjia Gao
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Target Detection ◽  
Multiple Scales ◽  
Classification Problem ◽  
Input Image ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Regression Problem ◽  
Public Datasets

Despite significant progress in object detection tasks, remote sensing image target detection is still challenging owing to complex backgrounds, large differences in target sizes, and uneven distribution of rotating objects. In this study, we consider model accuracy, inference speed, and detection of objects at any angle. We also propose a RepVGG-YOLO network using an improved RepVGG model as the backbone feature extraction network, which performs the initial feature extraction from the input image and considers network training accuracy and inference speed. We use an improved feature pyramid network (FPN) and path aggregation network (PANet) to reprocess feature output by the backbone network. The FPN and PANet module integrates feature maps of different layers, combines context information on multiple scales, accumulates multiple features, and strengthens feature information extraction. Finally, to maximize the detection accuracy of objects of all sizes, we use four target detection scales at the network output to enhance feature extraction from small remote sensing target pixels. To solve the angle problem of any object, we improved the loss function for classification using circular smooth label technology, turning the angle regression problem into a classification problem, and increasing the detection accuracy of objects at any angle. We conducted experiments on two public datasets, DOTA and HRSC2016. Our results show the proposed method performs better than previous methods.

scholarly journals Foreword to the Special Issue on Paving the Way for the Future of Urban Remote Sensing

2020 ◽  
Vol 13 ◽  
pp. 6533-6536
Author(s):  
Sebastien Lefevre ◽  
Thomas Corpetti ◽  
Monika Kuffer ◽  
Hannes Taubenbock ◽  
Clement Mallet
Keyword(s):  
Remote Sensing ◽  
Special Issue ◽  
Urban Remote Sensing ◽  
The Future ◽  
The Way

scholarly journals Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

2020 ◽  
Vol 12 (11) ◽  
pp. 1772
Author(s):  
Brian Alan Johnson ◽  
Lei Ma
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Online Survey ◽  
Accuracy Assessment ◽  
Learning Approaches ◽  
Special Issue ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Wide Range

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

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