Landslide mapping with remote sensing: challenges and opportunities
Landslide mapping is the primary step for landslide investigation and prevention. At present,
both the accuracy and the degree of automation of landslide mapping with remote sensing …
both the accuracy and the degree of automation of landslide mapping with remote sensing …
Deep learning application in plant stress imaging: a review
Plant stress is one of major issues that cause significant economic loss for growers. The
labor-intensive conventional methods for identifying the stressed plants constrain their …
labor-intensive conventional methods for identifying the stressed plants constrain their …
Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection
There is a growing demand for detailed and accurate landslide maps and inventories
around the globe, but particularly in hazard-prone regions such as the Himalayas. Most …
around the globe, but particularly in hazard-prone regions such as the Himalayas. Most …
Survey on computational-intelligence-based UAV path planning
The key objective of unmanned aerial vehicle (UAV) path planning is to produce a flight path
that connects a start state and a goal state while meeting the required constraints …
that connects a start state and a goal state while meeting the required constraints …
Mapping paddy rice using a convolutional neural network (CNN) with Landsat 8 datasets in the Dongting Lake Area, China
M Zhang, H Lin, G Wang, H Sun, J Fu - Remote Sensing, 2018 - mdpi.com
Rice is one of the world's major staple foods, especially in China. Highly accurate monitoring
on rice-producing land is, therefore, crucial for assessing food supplies and productivity …
on rice-producing land is, therefore, crucial for assessing food supplies and productivity …
Deep learning for feature extraction in remote sensing: A case-study of aerial scene classification
Scene classification relying on images is essential in many systems and applications related
to remote sensing. The scientific interest in scene classification from remotely collected …
to remote sensing. The scientific interest in scene classification from remotely collected …
A two‐stream deep fusion framework for high‐resolution aerial scene classification
Y Yu, F Liu - Computational intelligence and neuroscience, 2018 - Wiley Online Library
One of the challenging problems in understanding high‐resolution remote sensing images
is aerial scene classification. A well‐designed feature representation method and classifier …
is aerial scene classification. A well‐designed feature representation method and classifier …
Adaptive deep sparse semantic modeling framework for high spatial resolution image scene classification
High spatial resolution (HSR) imagery scene classification, which involves labeling an HSR
image with a specific semantic class according to the geographical properties, has received …
image with a specific semantic class according to the geographical properties, has received …
Meta-analysis of deep neural networks in remote sensing: A comparative study of mono-temporal classification to support vector machines
SS Heydari, G Mountrakis - ISPRS Journal of Photogrammetry and Remote …, 2019 - Elsevier
Deep learning methods have recently found widespread adoption for remote sensing tasks,
particularly in image or pixel classification. Their flexibility and versatility has enabled …
particularly in image or pixel classification. Their flexibility and versatility has enabled …
Global-local attention network for aerial scene classification
Y Guo, J Ji, X Lu, H Huo, T Fang, D Li - IEEE Access, 2019 - ieeexplore.ieee.org
The classification performance of aerial scenes relies heavily on the discriminative power of
feature representation from high-spatial resolution remotely sensed imagery. The …
feature representation from high-spatial resolution remotely sensed imagery. The …