Convolutional network architectures for super-resolution/sub-pixel mapping of drone-derived images

Arun, P.; Herrmann, Ittai; Budhiraju, Krishna M.; Karnieli, Arnon

doi:10.1016/j.patcog.2018.11.033

Cited by 23 publications

(11 citation statements)

References 49 publications

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“…The potential of simultaneous spatio-spectral super-resolution in addition to sub-pixel classification of MS observations from aerial vehicles (drones) is examined in [79]. To achieve both tasks, a network inversion-based architecture is proposed where a CNN architecture accepts both the low-resolution image, as well as image-derived features such as histograms-of-oriented-gradients.…”

Section: Super-resolutionmentioning

confidence: 99%

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Tsagkatakis

Aidini

Fotiadou

et al. 2019

Sensors

116

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Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language understanding. In remote sensing, a large body of research has been devoted to the application of deep learning for typical supervised learning tasks such as classification. Less yet equally important effort has also been allocated to addressing the challenges associated with the enhancement of low-quality observations from remote sensing platforms. Addressing such channels is of paramount importance, both in itself, since high-altitude imaging, environmental conditions, and imaging systems trade-offs lead to low-quality observation, as well as to facilitate subsequent analysis, such as classification and detection. In this paper, we provide a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution, denoising, restoration, pan-sharpening, and fusion, among others. In addition to the detailed analysis and comparison of recently presented approaches, different research avenues which could be explored in the future are also discussed.

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Section: Super-resolutionmentioning

confidence: 99%

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Tsagkatakis

Aidini

Fotiadou

et al. 2019

Sensors

116

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“…Both of them are dedicated to describing the relationship between fractions in the local window and the spatial distribution of subpixels in the central coarse pixel, but they have difficulty learning elusive non-linear hidden representation. Recently, methods based on deep neural networks (DNNs) such as convolution neural networks (CNNs) [24]- [26] and generative adversarial networks (GANs) [27] for SRM have gradually emerged. Some SRM methods based on DNNs comprise two steps: fraction image super-resolution and land cover allocation for subpixels [28], [29], and the successful experience of DNNs in image super-resolution can be used as references for the first step of SRM.…”

Section: Introductionmentioning

confidence: 99%

Graph Convolutional Networks-Based Super-Resolution Land Cover Mapping

Zhang

Sun

Ling

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Super-resolution mapping (SRM) is an effective technology to solve the problem of mixed pixels because it can be used to generate fine-resolution land cover maps from coarseresolution remote sensing images. Current methods based on deep neural networks (DNNs) have been successfully applied to SRM, as they can learn complex spatial patterns from training data. However, they lack the ability to learn structural information between adjacent land cover classes, which is vital in the reconstruction of spatial distribution. In this article, an SRM method based on graph convolutional networks (GCNs), named SRM GCN , is proposed to improve SRM results by capturing structure information on the graph. In SRM GCN , a supervised inductive learning strategy with mini-graphs as input is considered, which is an extension of the graph convolutional network (GCN) framework. Furthermore, two operations are designed in terms of adjacency matrix construction and an information propagation rule to help reconstruct detailed information of geographical objects. Experiments on three datasets with different spatial resolutions demonstrate the qualitative and quantitative superiority of SRM GCN over three other popular SRM methods.

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“…Many efforts have also been made to increase the quality of moderate resolution platforms with advanced computing techniques, such as the super-resolution approach based on machine learning, with deep neural networks (DNN) and convolutional neural networks (CNN) being the most exploited ones [29][30][31][32]. For example, several convolutional network architectures were proposed to enhance the spatial details of drone-derived images [33]. Indeed, an intrinsic capability of deep learning is distributed learning, which distributes, among all the variables of the model, the knowledge of the dataset and the capability to extract such high-level, abstract features [34].…”

Section: Introductionmentioning

confidence: 99%

UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture

Mazzia

Comba

Khaliq

et al. 2020

Sensors

101

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Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are grown by rows. Indeed, in this framework, the satellite’s output could be biased by intra-row covering, giving inaccurate information about crop status. This paper presents a novel satellite imagery refinement framework, based on a deep learning technique which exploits information properly derived from high resolution images acquired by unmanned aerial vehicle (UAV) airborne multispectral sensors. To train the convolutional neural network, only a single UAV-driven dataset is required, making the proposed approach simple and cost-effective. A vineyard in Serralunga d’Alba (Northern Italy) was chosen as a case study for validation purposes. Refined satellite-driven normalized difference vegetation index (NDVI) maps, acquired in four different periods during the vine growing season, were shown to better describe crop status with respect to raw datasets by correlation analysis and ANOVA. In addition, using a K-means based classifier, 3-class vineyard vigor maps were profitably derived from the NDVI maps, which are a valuable tool for growers.

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Convolutional network architectures for super-resolution/sub-pixel mapping of drone-derived images

Cited by 23 publications

References 49 publications

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Graph Convolutional Networks-Based Super-Resolution Land Cover Mapping

UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture

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