A review of deep learning methods for semantic segmentation of remote sensing imagery
X Yuan, J Shi, L Gu - Expert Systems with Applications, 2021 - Elsevier
Semantic segmentation of remote sensing imagery has been employed in many
applications and is a key research topic for decades. With the success of deep learning …
applications and is a key research topic for decades. With the success of deep learning …
Change detection techniques for remote sensing applications: A survey
A Asokan, J Anitha - Earth Science Informatics, 2019 - Springer
Change detection captures the spatial changes from multi temporal satellite images due to
manmade or natural phenomenon. It is of great importance in remote sensing, monitoring …
manmade or natural phenomenon. It is of great importance in remote sensing, monitoring …
Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels
Large-scale high-resolution land-cover mapping is a way to comprehend the Earth's surface
and resolve the ecological and resource challenges facing humanity. High-resolution (≤ 1 …
and resolve the ecological and resource challenges facing humanity. High-resolution (≤ 1 …
Analysis on change detection techniques for remote sensing applications: A review
Satellite images taken on the earth's surface are analyzed to identify the spatial and
temporal changes that have occurred naturally or manmade. Real-time prediction of change …
temporal changes that have occurred naturally or manmade. Real-time prediction of change …
Rethinking transformers for semantic segmentation of remote sensing images
Transformer has been widely applied in image processing tasks as a substitute for
convolutional neural networks (CNNs) for feature extraction due to its superiority in global …
convolutional neural networks (CNNs) for feature extraction due to its superiority in global …
Semantic segmentation of large-size VHR remote sensing images using a two-stage multiscale training architecture
Very-high resolution (VHR) remote sensing images (RSIs) have significantly larger spatial
size compared to typical natural images used in computer vision applications. Therefore, it is …
size compared to typical natural images used in computer vision applications. Therefore, it is …
Applying fully convolutional architectures for semantic segmentation of a single tree species in urban environment on high resolution UAV optical imagery
This study proposes and evaluates five deep fully convolutional networks (FCNs) for the
semantic segmentation of a single tree species: SegNet, U-Net, FC-DenseNet, and two …
semantic segmentation of a single tree species: SegNet, U-Net, FC-DenseNet, and two …
Unmanned aerial vehicle (UAV)-Based pavement image stitching without occlusion, crack semantic segmentation, and quantification
Unmanned Aerial Vehicle (UAV)-based pavement distress detection offers efficient and safe
advantages. However, obstructions from road vehicles and the slender shape of cracks in …
advantages. However, obstructions from road vehicles and the slender shape of cracks in …
Dense semantic labeling with atrous spatial pyramid pooling and decoder for high-resolution remote sensing imagery
Y Wang, B Liang, M Ding, J Li - Remote Sensing, 2018 - mdpi.com
Dense semantic labeling is significant in high-resolution remote sensing imagery research
and it has been widely used in land-use analysis and environment protection. With the …
and it has been widely used in land-use analysis and environment protection. With the …
Evaluation of deep neural networks for semantic segmentation of prostate in T2W MRI
In this paper, we present an evaluation of four encoder–decoder CNNs in the segmentation
of the prostate gland in T2W magnetic resonance imaging (MRI) image. The four selected …
of the prostate gland in T2W magnetic resonance imaging (MRI) image. The four selected …