Single-frame super resolution of remote-sensing images by convolutional neural networks

Tuna, Caglayan; Ünal, Gözde; Sertel, Elif

doi:10.1080/01431161.2018.1425561

Cited by 52 publications

(24 citation statements)

References 21 publications

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“…In another example, [22] use deep neural networks for simultaneous 4× super-resolution and colorization of satellite imagery. Several papers [41,25,35,20,28] modify or leverage SRCNN [7] and/or VDSR [15] to successfully super-resolve Jilin-1, SPOT, Pleiades, Sentinel-2, and Landsat imagery.…”

Section: Super-resolution Techniques and Application To Overhead Imagerymentioning

confidence: 99%

The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery

Shermeyer

Etten

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

190

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We explore the application of super-resolution techniques to satellite imagery, and the effects of these techniques on object detection algorithm performance. Specifically, we enhance satellite imagery beyond its native resolution, and test if we can identify various types of vehicles, planes, and boats with greater accuracy than native resolution. Using the Very Deep Super-Resolution (VDSR) framework and a custom Random Forest Super-Resolution (RFSR) framework we generate enhancement levels of 2×, 4×, and 8× over five distinct resolutions ranging from 30 cm to 4.8 meters. Using both native and super-resolved data, we then train several custom detection models using the SIMRDWN object detection framework. SIMRDWN combines a number of popular object detection algorithms (e.g. SSD, YOLO) into a unified framework that is designed to rapidly detect objects in large satellite images. This approach allows us to quantify the effects of super-resolution techniques on object detection performance across multiple classes and resolutions. We also quantify the performance of object detection as a function of native resolution and object pixel size. For our test set we note that performance degrades from mean average precision (mAP) = 0.53 at 30 cm resolution, down to mAP = 0.11 at 4.8 m resolution. Super-resolving native 30 cm imagery to 15 cm yields the greatest benefit; a 13 − 36% improvement in mAP. Superresolution is less beneficial at coarser resolutions, though still provides a small improvement in performance.

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Section: Super-resolution Techniques and Application To Overhead Imagerymentioning

confidence: 99%

The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery

Shermeyer

Etten

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

190

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“…Super-resolution, which aims to enhance spatial resolution, is an ongoing research topic in computer vision and remote sensing [114]. The latest super-resolution trend focused on example (learning)-based techniques, including a training phase between low-resolution and high-resolution pairs of images [116]. Example-based techniques have seen enhanced accuracies by the introduction of CNNs to generic superresolution problems [117].…”

Section: Other Applicationsmentioning

confidence: 99%

“…Example-based techniques have seen enhanced accuracies by the introduction of CNNs to generic superresolution problems [117]. However, RS imageries exhibit a different level of complexity than images in other fields such as computer vision, which delayed the use of CNNs in RS image super-resolution until 2018 by introducing a specific super-resolution CNN architecture to adapt with multispectral satellite imagery [116]. An overview of the related papers shows that all the CNN models were 2D structured in which Adam and SGD equally were used for parameter optimizations with the epoch numbers ranging from 80 to 600.…”

Section: Other Applicationsmentioning

confidence: 99%

A Meta-Analysis of Convolutional Neural Networks for Remote Sensing Applications

Ghanbari

Mahdianpari

Homayouni

et al. 2021

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

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Since the rise of deep learning in the past few years, convolutional neural networks (CNNs) have quickly found their place within the remote sensing (RS) community. As a result, they have transitioned away from other machine learning techniques, achieving unprecedented improvements in many specific RS applications. This paper presents a meta-analysis of 416 peer-reviewed journal articles, summarizes CNN advancements, and its current status under RS applications. The review process includes a statistical and descriptive analysis of a database comprised of 23 fields, including 1) general characteristics such as various applications, study objectives, sensors, and data types, and 2) algorithm specifications such as different types of CNN models, parameter settings, and reported accuracies. This review begins with a comprehensive survey of the relevant articles without considering any specific criteria to give readers an idea of general trends, then investigates CNNs within different RS applications to provide specific directions for the researchers. Finally, a conclusion summarizes potentialities, critical issues, and challenges related to the observed trends.

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“…The results demonstrate that using a pre-training SRNN network leads to significantly worse performance compared to “naive” methods such as bicubic interpolation, while fine-tuning the network to the particular type of observation, leads to a marginal increase in performance (0.3 dB). Similarly, in [52], the case of CNN for single-image super-resolution by factors of 2,3 and 4 is investigated using observations from the SPOT 6 and 7 and the Pleiades 1A and 1B satellites. The authors compare the VDSR and the SRCNN architectures and report significant benefits of the VSDR compared to the SRCNN; however the gains offered by the VDSR are marginal compared to bicubic interpolation (≤1 dB in PSNR).…”

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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Single-frame super resolution of remote-sensing images by convolutional neural networks

Cited by 52 publications

References 21 publications

The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery

The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery

A Meta-Analysis of Convolutional Neural Networks for Remote Sensing Applications

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

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