Remote sensing for agricultural applications: A meta-review
Agriculture provides humanity with food, fibers, fuel, and raw materials that are paramount
for human livelihood. Today, this role must be satisfied within a context of environmental …
for human livelihood. Today, this role must be satisfied within a context of environmental …
Unmanned aerial vehicle for remote sensing applications—A review
H Yao, R Qin, X Chen - Remote Sensing, 2019 - mdpi.com
The unmanned aerial vehicle (UAV) sensors and platforms nowadays are being used in
almost every application (eg, agriculture, forestry, and mining) that needs observed …
almost every application (eg, agriculture, forestry, and mining) that needs observed …
ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data
Scene understanding of high resolution aerial images is of great importance for the task of
automated monitoring in various remote sensing applications. Due to the large within-class …
automated monitoring in various remote sensing applications. Due to the large within-class …
Spatial–spectral attention network guided with change magnitude image for land cover change detection using remote sensing images
Z Lv, F Wang, G Cui, JA Benediktsson… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Land cover change detection (LCCD) using remote sensing images (RSIs) plays an
important role in natural disaster evaluation, forest deformation monitoring, and wildfire …
important role in natural disaster evaluation, forest deformation monitoring, and wildfire …
Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective
MD Hossain, D Chen - ISPRS Journal of Photogrammetry and Remote …, 2019 - Elsevier
Image segmentation is a critical and important step in (GEographic) Object-Based Image
Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly …
Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly …
Landslide detection using deep learning and object-based image analysis
O Ghorbanzadeh, H Shahabi, A Crivellari… - Landslides, 2022 - Springer
Recent landslide detection studies have focused on pixel-based deep learning (DL)
approaches. In contrast, intuitive annotation of landslides from satellite imagery is based on …
approaches. In contrast, intuitive annotation of landslides from satellite imagery is based on …
Remote sensing image segmentation advances: A meta-analysis
I Kotaridis, M Lazaridou - ISPRS Journal of Photogrammetry and Remote …, 2021 - Elsevier
The advances in remote sensing sensors during the last two decades have led to the
production of very high spatial resolution multispectral images. In order to adapt to this rapid …
production of very high spatial resolution multispectral images. In order to adapt to this rapid …
Land cover change detection techniques: Very-high-resolution optical images: A review
Land cover change detection (LCCD) with remote sensing images is an important
application of Earth observation data because it provides insights into environmental health …
application of Earth observation data because it provides insights into environmental health …
Remote sensing image scene classification: Benchmark and state of the art
Remote sensing image scene classification plays an important role in a wide range of
applications and hence has been receiving remarkable attention. During the past years …
applications and hence has been receiving remarkable attention. During the past years …
Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: a review of sensor technology, measurement procedures, and data correction workflows
In the last 10 years, development in robotics, computer vision, and sensor technology has
provided new spectral remote sensing tools to capture unprecedented ultra-high spatial and …
provided new spectral remote sensing tools to capture unprecedented ultra-high spatial and …