Deep learning-based semantic segmentation of urban features in satellite images: A review and meta-analysis
Availability of very high-resolution remote sensing images and advancement of deep
learning methods have shifted the paradigm of image classification from pixel-based and …
learning methods have shifted the paradigm of image classification from pixel-based and …
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 …
Building extraction in very high resolution remote sensing imagery using deep learning and guided filters
Very high resolution (VHR) remote sensing imagery has been used for land cover
classification, and it tends to a transition from land-use classification to pixel-level semantic …
classification, and it tends to a transition from land-use classification to pixel-level semantic …
An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images
A Abdollahi, B Pradhan, AM Alamri - Geocarto International, 2022 - Taylor & Francis
Building objects is one of the principal features that are essential for updating the geospatial
database. Extracting building features from high-resolution imagery automatically and …
database. Extracting building features from high-resolution imagery automatically and …
Using long short-term memory recurrent neural network in land cover classification on Landsat and Cropland data layer time series
Land cover maps are significant in assisting agricultural decision making. However, the
existing workflow of producing land cover maps is very complicated and the result accuracy …
existing workflow of producing land cover maps is very complicated and the result accuracy …
CCANet: Class-constraint coarse-to-fine attentional deep network for subdecimeter aerial image semantic segmentation
G Deng, Z Wu, C Wang, M Xu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Semantic segmentation is important for the understanding of subdecimeter aerial images. In
recent years, deep convolutional neural networks (DCNNs) have been used widely for …
recent years, deep convolutional neural networks (DCNNs) have been used widely for …
Remote sensing for monitoring photovoltaic solar plants in Brazil using deep semantic segmentation
MVCV Costa, OLF Carvalho, AG Orlandi, I Hirata… - Energies, 2021 - mdpi.com
Brazil is a tropical country with continental dimensions and abundant solar resources that
are still underutilized. However, solar energy is one of the most promising renewable …
are still underutilized. However, solar energy is one of the most promising renewable …
Semantic segmentation of remote-sensing images through fully convolutional neural networks and hierarchical probabilistic graphical models
Deep learning (DL) is currently the dominant approach to image classification and
segmentation, but the performances of DL methods are remarkably influenced by the …
segmentation, but the performances of DL methods are remarkably influenced by the …
Detecting clouds in multispectral satellite images using quantum-kernel support vector machines
A Miroszewski, J Mielczarek, G Czelusta… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Support vector machines (SVMs) are well-established classifiers that are effectively
deployed in an array of classification tasks. In this article, we consider extending classical …
deployed in an array of classification tasks. In this article, we consider extending classical …
An overview of deep learning methods for image registration with focus on feature-based approaches
K Kuppala, S Banda, TR Barige - … Journal of Image and Data Fusion, 2020 - Taylor & Francis
Image registration is an essential pre-processing step for several computer vision problems
like image reconstruction and image fusion. In this paper, we present a review on image …
like image reconstruction and image fusion. In this paper, we present a review on image …