Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities
The synergistic combination of deep learning (DL) models and Earth observation (EO)
promises significant advances to support the Sustainable Development Goals (SDGs). New …
promises significant advances to support the Sustainable Development Goals (SDGs). New …
Pixel level fusion techniques for SAR and optical images: A review
SC Kulkarni, PP Rege - Information Fusion, 2020 - Elsevier
Image Fusion is a process of combining two or more images into a single image which is
more informative and hence more useful from an interpretation point of view. With the rapid …
more informative and hence more useful from an interpretation point of view. With the rapid …
Deep learning meets SAR: Concepts, models, pitfalls, and perspectives
Deep learning in remote sensing has received considerable international hype, but it is
mostly limited to the evaluation of optical data. Although deep learning has been introduced …
mostly limited to the evaluation of optical data. Although deep learning has been introduced …
MS-CapsNet: A novel multi-scale capsule network
Capsule network is a novel architecture to encode the properties and spatial relationships of
the feature in an image, which shows encouraging results on image classification. However …
the feature in an image, which shows encouraging results on image classification. However …
Image restoration for remote sensing: Overview and toolbox
Remote sensing provides valuable information about objects and areas from a distance in
either active (eg, radar and lidar) or passive (eg, multispectral and hyperspectral) modes …
either active (eg, radar and lidar) or passive (eg, multispectral and hyperspectral) modes …
Learning a dilated residual network for SAR image despeckling
In this paper, to break the limit of the traditional linear models for synthetic aperture radar
(SAR) image despeckling, we propose a novel deep learning approach by learning a non …
(SAR) image despeckling, we propose a novel deep learning approach by learning a non …
SAR2SAR: A semi-supervised despeckling algorithm for SAR images
Speckle reduction is a key step in many remote sensing applications. By strongly affecting
synthetic aperture radar (SAR) images, it makes them difficult to analyze. Due to the difficulty …
synthetic aperture radar (SAR) images, it makes them difficult to analyze. Due to the difficulty …
Speckle2Void: Deep self-supervised SAR despeckling with blind-spot convolutional neural networks
Information extraction from synthetic aperture radar (SAR) images is heavily impaired by
speckle noise, and hence, despeckling is a crucial preliminary step in scene analysis …
speckle noise, and hence, despeckling is a crucial preliminary step in scene analysis …
Multi-scale single image dehazing using perceptual pyramid deep network
Haze adversely degrades quality of an image thereby affecting its aesthetic appeal and
visibility in outdoor scenes. Single image dehazing is particularly challenging due to its ill …
visibility in outdoor scenes. Single image dehazing is particularly challenging due to its ill …
Deep learning for SAR image despeckling
Speckle filtering is an unavoidable step when dealing with applications that involve
amplitude or intensity images acquired by coherent systems, such as Synthetic Aperture …
amplitude or intensity images acquired by coherent systems, such as Synthetic Aperture …