scholarly journals Super-Resolution Restoration of Spaceborne Ultra-High-Resolution Images Using the UCL OpTiGAN System

2021 ◽  
Vol 13 (12) ◽  
pp. 2269
Author(s):  
Yu Tao ◽  
Jan-Peter Muller
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Processing System ◽  
Super Resolution ◽  
Processing Scheme ◽  
Qualitative And Quantitative ◽  
Video Frames ◽  
High Resolution Satellite Images ◽  
High Resolution Images ◽  
Image Super Resolution

We introduce a robust and light-weight multi-image super-resolution restoration (SRR) method and processing system, called OpTiGAN, using a combination of a multi-image maximum a posteriori approach and a deep learning approach. We show the advantages of using a combined two-stage SRR processing scheme for significantly reducing inference artefacts and improving effective resolution in comparison to other SRR techniques. We demonstrate the optimality of OpTiGAN for SRR of ultra-high-resolution satellite images and video frames from 31 cm/pixel WorldView-3, 75 cm/pixel Deimos-2 and 70 cm/pixel SkySat. Detailed qualitative and quantitative assessments are provided for the SRR results on a CEOS-WGCV-IVOS geo-calibration and validation site at Baotou, China, which features artificial permanent optical targets. Our measurements have shown a 3.69 times enhancement of effective resolution from 31 cm/pixel WorldView-3 imagery to 9 cm/pixel SRR.

scholarly journals A GENERATIVE ADVERSARIAL NETWORK APPROACH FOR SUPER-RESOLUTION OF SENTINEL-2 SATELLITE IMAGES

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 9-14
Author(s):  
F. Pineda ◽  
V. Ayma ◽  
C. Beltran
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Structural Similarity ◽  
Generative Adversarial Network ◽  
Acceptable Result ◽  
Adversarial Network ◽  
High Resolution Satellite Images ◽  
Sentinel 2

Abstract. High-resolution satellite images have always been in high demand due to the greater detail and precision they offer, as well as the wide scope of the fields in which they could be applied; however, satellites in operation offering very high-resolution (VHR) images has experienced an important increase, but they remain as a smaller proportion against existing lower resolution (HR) satellites. Recent models of convolutional neural networks (CNN) are very suitable for applications with image processing, like resolution enhancement of images; but in order to obtain an acceptable result, it is important, not only to define the kind of CNN architecture but the reference set of images to train the model. Our work proposes an alternative to improve the spatial resolution of HR images obtained by Sentinel-2 satellite by using the VHR images from PeruSat1, a peruvian satellite, which serve as the reference for the super-resolution approach implementation based on a Generative Adversarial Network (GAN) model, as an alternative for obtaining VHR images. The VHR PeruSat-1 image dataset is used for the training process of the network. The results obtained were analyzed considering the Peak Signal to Noise Ratios (PSNR) and the Structural Similarity (SSIM). Finally, some visual outcomes, over a given testing dataset, are presented so the performance of the model could be analyzed as well.

scholarly journals Semi-Coupled Convolutional Sparse Learning for Image Super-Resolution

2019 ◽  
Vol 11 (21) ◽  
pp. 2593
Author(s):  
Li ◽  
Zhang ◽  
Jiao ◽  
Liu ◽  
Yang ◽  
...  
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Mapping Function ◽  
Linear Mapping ◽  
Sparse Learning ◽  
Proposed Model ◽  
Filter Size ◽  
High Resolution Images ◽  
Resolution Problem ◽  
Image Super Resolution

In the convolutional sparse coding-based image super-resolution problem, the coefficients of low- and high-resolution images in the same position are assumed to be equivalent, which enforces an identical structure of low- and high-resolution images. However, in fact the structure of high-resolution images is much more complicated than that of low-resolution images. In order to reduce the coupling between low- and high-resolution representations, a semi-coupled convolutional sparse learning method (SCCSL) is proposed for image super-resolution. The proposed method uses nonlinear convolution operations as the mapping function between low- and high-resolution features, and conventional linear mapping can be seen as a special case of the proposed method. Secondly, the neighborhoods within the filter size are used to calculate the current pixel, improving the flexibility of our proposed model. In addition, the filter size is adjustable. In order to illustrate the effectiveness of SCCSL method, we compare it with four state-of-the-art methods of 15 commonly used images. Experimental results show that this work provides a more flexible and efficient approach for image super-resolution problem.

scholarly journals High Resolution Satellite Images for Instantaneous Shoreline Extraction Using New Enhancement Algorithms

Geosciences ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 123 ◽  
Author(s):  
Donatella Dominici ◽  
Sara Zollini ◽  
Maria Alicandro ◽  
Francesca Della Torre ◽  
Paolo Buscema ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Satellite Images ◽  
Human Life ◽  
Essential Element ◽  
Action Planning ◽  
High Resolution Satellite Images ◽  
High Resolution Images ◽  
Territorial Government ◽  
Very High

Knowledge of a territory is an essential element in any future planning action and in appropriate territorial and environmental requalification action planning. The current large-scale availability of satellite data, thanks to very high resolution images, provides professional users in the environmental, urban planning, engineering, and territorial government sectors, in general, with large amounts of useful data with which to monitor the territory and cultural heritage. Italy is experiencing environmental emergencies, and coastal erosion is one of the greatest threats, not only to the Italian heritage and economy, but also to human life. The aim of this paper is to find a rapid way of identifying the instantaneous shoreline. This possibility could help government institutions such as regions, civil protection, etc., to analyze large areas of land quickly. The focus is on instantaneous shoreline extraction in Ortona (CH, Italy), without considering tides, using WorldView-2 satellite images (50-cm resolution in panchromatic and 2 m in multispectral). In particular, the main purpose of this paper is to compare commercial software and ACM filters to test their effectiveness.

Changing Information Extraction of Coastal Accretion-Erosion from High Resolution Satellite Images

2012 ◽  
Vol 610-613 ◽  
pp. 3685-3688 ◽  
Author(s):  
Yan Gu ◽  
Ying Zhang ◽  
Zheng Jun Wang
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Dynamic Balance ◽  
High Tide ◽  
Tidal Level ◽  
High Resolution Satellite Images ◽  
High Resolution Images ◽  
The Mean ◽  
Sea Reclamation ◽  
High Tide Line

Methods for extracting information about coastline, mean high tide line and mean low tide line from satellite images are investigated based on the satellite images which have a spatial resolution of 10m and were obtained in the coastal area of Yancheng of Jiangsu province in 2006, 2008 and 2009, respectively. The evolution of the coastal zone influenced by human activities such as harbor construction and sea reclamation for farming is analyzed. The results show that (1) comparing with low resolution RS images, the high resolution images can be used to extract more subtle culture features, from which the mean high tidal line can be extracted; (2) by combing with the tidal level of the day and based on the high tidal line extracted already, the instantaneous water line on the images and leaner relationship among them, the mean low tidal line may possibly be worked out; (3) it has been being in an accretion status since 2006, with an increasing magnitude every year, while the mean low tide line was in a dynamic balance status from 2006 to 2008, but was eroded by 840m from 2008 to 2009, being very distinct in its change.

scholarly journals Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification

2018 ◽  
Author(s):  
Zheng Wang ◽  
Mang Ye ◽  
Fan Yang ◽  
Xiang Bai ◽  
Shin'ichi Satoh
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Feature Representation ◽  
Image Feature ◽  
Low Resolution ◽  
Open World ◽  
In Series ◽  
High Resolution Images ◽  
Public Dataset ◽  
Image Super Resolution

Person re-identification (REID) is an important task in video surveillance and forensics applications. Most of previous approaches are based on a key assumption that all person images have uniform and sufficiently high resolutions. Actually, various low-resolutions and scale mismatching always exist in open world REID. We name this kind of problem as Scale-Adaptive Low Resolution Person Re-identification (SALR-REID). The most intuitive way to address this problem is to increase various low-resolutions (not only low, but also with different scales) to a uniform high-resolution. SR-GAN is one of the most competitive image super-resolution deep networks, designed with a fixed upscaling factor. However, it is still not suitable for SALR-REID task, which requires a network not only synthesizing high-resolution images with different upscaling factors, but also extracting discriminative image feature for judging person’s identity. (1) To promote the ability of scale-adaptive upscaling, we cascade multiple SRGANs in series. (2) To supplement the ability of image feature representation, we plug-in a reidentification network. With a unified formulation, a Cascaded Super-Resolution GAN (CSR-GAN) framework is proposed. Extensive evaluations on two simulated datasets and one public dataset demonstrate the advantages of our method over related state-of-the-art methods.

scholarly journals Thick Cloud Removal in High-Resolution Satellite Images Using Stepwise Radiometric Adjustment and Residual Correction

2019 ◽  
Vol 11 (16) ◽  
pp. 1925 ◽  
Author(s):  
Zhiwei Li ◽  
Huanfeng Shen ◽  
Qing Cheng ◽  
Wei Li ◽  
Liangpei Zhang
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Real Data ◽  
Target Image ◽  
High Resolution Satellite Images ◽  
High Resolution Images ◽  
Residual Correction ◽  
Geographical Maps ◽  
Cloud Removal ◽  
Cloud Mask

Cloud cover is a common problem in optical satellite imagery, which leads to missing information in images as well as a reduction in the data usability. In this paper, a thick cloud removal method based on stepwise radiometric adjustment and residual correction (SRARC) is proposed, which is aimed at effectively removing the clouds in high-resolution images for the generation of high-quality and spatially contiguous urban geographical maps. The basic idea of SRARC is that the complementary information in adjacent temporal satellite images can be utilized for the seamless recovery of cloud-contaminated areas in the target image after precise radiometric adjustment. To this end, the SRARC method first optimizes the given cloud mask of the target image based on superpixel segmentation, which is conducted to ensure that the labeled cloud boundaries go through homogeneous areas of the target image, to ensure a seamless reconstruction. Stepwise radiometric adjustment is then used to adjust the radiometric information of the complementary areas in the auxiliary image, step by step, and clouds in the target image can be removed by the replacement with the adjusted complementary areas. Finally, residual correction based on global optimization is used to further reduce the radiometric differences between the recovered areas and the cloud-free areas. The final cloud removal results are then generated. High-resolution images with different spatial resolutions and land-cover change patterns were used in both simulated and real-data cloud removal experiments. The results suggest that SRARC can achieve a better performance than the other compared methods, due to the superiority of the radiometric adjustment and spatial detail preservation. SRARC is thus a promising approach that has the potential for routine use, to support applications based on high-resolution satellite images.

Image Super-Resolution via Saliency Sparse Representation

2014 ◽  
Vol 568-570 ◽  
pp. 659-662
Author(s):  
Xue Jun Zhang ◽  
Bing Liang Hu
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Saliency Detection ◽  
Computational Cost ◽  
Super Resolution ◽  
Compact Representation ◽  
High Resolution Images ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Marked Area

The paper proposes a new approach to single-image super resolution (SR), which is based on sparse representation. Previous researchers just focus on the global intensive patch, without local intensive patch. The performance of dictionary trained by the local saliency intensive patch is more significant. Motivated by this, we joined the saliency detection to detect marked area in the image. We proposed a sparse representation for saliency patch of the low-resolution input, and used the coefficients of this representation to generate the high-resolution output. Compared to precious approaches which simply sample a large amount of image patch pairs, the saliency dictionary pair is a more compact representation of the patch pairs, reducing the computational cost substantially. Through the experiment, we demonstrate that our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods.

Image Super-Resolution via Low-Rank Representation

2014 ◽  
Vol 568-570 ◽  
pp. 652-655 ◽  
Author(s):  
Zhao Li ◽  
Le Wang ◽  
Tao Yu ◽  
Bing Liang Hu
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Low Rank ◽  
Low Resolution ◽  
Resolution Image ◽  
High Resolution Image ◽  
High Resolution Images ◽  
Low Rank Representation ◽  
Image Super Resolution ◽  
Single Image Super Resolution

This paper presents a novel method for solving single-image super-resolution problems, based upon low-rank representation (LRR). Given a set of a low-resolution image patches, LRR seeks the lowest-rank representation among all the candidates that represent all patches as the linear combination of the patches in a low-resolution dictionary. By jointly training two dictionaries for the low-resolution and high-resolution images, we can enforce the similarity of LLRs between the low-resolution and high-resolution image pair with respect to their own dictionaries. Therefore, the LRR of a low-resolution image can be applied with the high-resolution dictionary to generate a high-resolution image. Unlike the well-known sparse representation, which computes the sparsest representation of each image patch individually, LRR aims at finding the lowest-rank representation of a collection of patches jointly. LRR better captures the global structure of image. Experiments show that our method gives good results both visually and quantitatively.

MAPPING OF VARIATIONS IN THE ANAPA BAY BAR LANDSCAPE-MORPHOLOGIC STRUCTURE WITH HIGH-RESOLUTION SATELLITE IMAGES

2017 ◽  
Author(s):  
Valentina Kravtsova ◽  
Valentina Kravtsova ◽  
Ekaterina Chalova ◽  
Ekaterina Chalova ◽  
Vayacheslav Krylenko ◽  
...  
Keyword(s):  
High Resolution ◽  
Black Sea ◽  
Wind Direction ◽  
Anthropogenic Impact ◽  
Satellite Images ◽  
Morphological Structure ◽  
The Black Sea ◽  
Anthropogenic Influence ◽  
High Resolution Satellite Images ◽  
High Resolution Images

The Anapa bay bar is at present one of only a few sand beaches in the Black Sea coastal zone of Russia. The bay bar includes three main belts – beach, dune belt and hillocky sands. A strong anthropogenic impact is observed: the landscape-morphological structure of the dune belt is disturbed, so monitoring of the bay bar is essential . For this purpose we had compiled a series of maps of landscape-morphological structure for the Blagoveschensk and Vityazevo-Anapa parts of the bay bar using high-resolution images from WorldView-2 satellite. Interpretation of stereo-pairs of multitemporal images was carried out at the scale of 1:2000, while a series of maps was compiled at the scale of 1:5000. Twelve sites with different landscape-morphologic structure are covered by these maps and characterized. The structure depends on geomorphologic neighborhood (adjacency to the cliff or to the lagoon) and aspect to wind direction, but mainly on the degree of anthropogenic influence. So the dune belt has been formed at the beach in some areas, but in other areas the dune belt is located behind the beach, or sometimes has disappeared. The compiled maps clearly reflect these variations and show their mainly anthropogenic origin. These maps will help to investigate adaptive solutions for Anapa bay bar conservation and protection.

MAPPING OF VARIATIONS IN THE ANAPA BAY BAR LANDSCAPE-MORPHOLOGIC STRUCTURE WITH HIGH-RESOLUTION SATELLITE IMAGES

2017 ◽  
Author(s):  
Valentina Kravtsova ◽  
Valentina Kravtsova ◽  
Ekaterina Chalova ◽  
Ekaterina Chalova ◽  
Vayacheslav Krylenko ◽  
...  
Keyword(s):  
High Resolution ◽  
Black Sea ◽  
Wind Direction ◽  
Anthropogenic Impact ◽  
Satellite Images ◽  
Morphological Structure ◽  
The Black Sea ◽  
Anthropogenic Influence ◽  
High Resolution Satellite Images ◽  
High Resolution Images

The Anapa bay bar is at present one of only a few sand beaches in the Black Sea coastal zone of Russia. The bay bar includes three main belts – beach, dune belt and hillocky sands. A strong anthropogenic impact is observed: the landscape-morphological structure of the dune belt is disturbed, so monitoring of the bay bar is essential . For this purpose we had compiled a series of maps of landscape-morphological structure for the Blagoveschensk and Vityazevo-Anapa parts of the bay bar using high-resolution images from WorldView-2 satellite. Interpretation of stereo-pairs of multitemporal images was carried out at the scale of 1:2000, while a series of maps was compiled at the scale of 1:5000. Twelve sites with different landscape-morphologic structure are covered by these maps and characterized. The structure depends on geomorphologic neighborhood (adjacency to the cliff or to the lagoon) and aspect to wind direction, but mainly on the degree of anthropogenic influence. So the dune belt has been formed at the beach in some areas, but in other areas the dune belt is located behind the beach, or sometimes has disappeared. The compiled maps clearly reflect these variations and show their mainly anthropogenic origin. These maps will help to investigate adaptive solutions for Anapa bay bar conservation and protection.

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