scholarly journals Effects of Spatial Resolution on the Satellite Observation of Floating Macroalgae Blooms

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1761
Author(s):  
Xinhua Wang ◽  
Qianguo Xing ◽  
Deyu An ◽  
Ling Meng ◽  
Xiangyang Zheng ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Yellow Sea ◽  
Satellite Images ◽  
Satellite Observation ◽  
The Yellow Sea ◽  
Low Resolution ◽  
High Resolution Satellite Images ◽  
High Resolution Images ◽  
Low Resolution Images

Satellite images with different spatial resolutions are widely used in the observations of floating macroalgae booms in sea surface. In this study, semi-synchronous satellite images with different resolutions (10 m, 16 m, 30 m, 50 m, 100 m, 250 m and 500 m) acquired over the Yellow Sea, are used to quantitatively assess the effects of spatial resolution on the observation of floating macroalgae blooms of Ulva prolifera. Results indicate that the covering area of macroalgae-mixing pixels (MM-CA) detected from high resolution images is smaller than that from low resolution images; however, the area affected by macroalgae blooms (AA) is larger in high resolution images than in low resolution ones. The omission rates in the MM-CA and the AA increase with the decrease of spatial resolution. These results indicate that satellite remote sensing on the basis of low resolution images (especially, 100 m, 250 m, 500 m), would overestimate the covering area of macroalgae while omit the small patches in the affected zones. To reduce the impacts of overestimation and omission, high resolution satellite images are used to show the seasonal changes of macroalgae blooms in 2018 and 2019 in the Yellow Sea.

scholarly journals Spatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping

2021 ◽  
Vol 13 (12) ◽  
pp. 2308
Author(s):  
Masoomeh Aslahishahri ◽  
Kevin G. Stanley ◽  
Hema Duddu ◽  
Steve Shirtliffe ◽  
Sally Vail ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Real World ◽  
Super Resolution ◽  
Aerial Images ◽  
Plant Phenotyping ◽  
Low Resolution ◽  
High Resolution Images ◽  
Synthetic Images ◽  
Low Resolution Images

Unmanned aerial vehicle (UAV) imaging is a promising data acquisition technique for image-based plant phenotyping. However, UAV images have a lower spatial resolution than similarly equipped in field ground-based vehicle systems, such as carts, because of their distance from the crop canopy, which can be particularly problematic for measuring small-sized plant features. In this study, the performance of three deep learning-based super resolution models, employed as a pre-processing tool to enhance the spatial resolution of low resolution images of three different kinds of crops were evaluated. To train a super resolution model, aerial images employing two separate sensors co-mounted on a UAV flown over lentil, wheat and canola breeding trials were collected. A software workflow to pre-process and align real-world low resolution and high-resolution images and use them as inputs and targets for training super resolution models was created. To demonstrate the effectiveness of real-world images, three different experiments employing synthetic images, manually downsampled high resolution images, or real-world low resolution images as input to the models were conducted. The performance of the super resolution models demonstrates that the models trained with synthetic images cannot generalize to real-world images and fail to reproduce comparable images with the targets. However, the same models trained with real-world datasets can reconstruct higher-fidelity outputs, which are better suited for measuring plant phenotypes.

scholarly journals Seasonal evolution of the sea-ice floe size distribution in the Beaufort and Chukchi seas

Elem Sci Anth ◽  
2018 ◽  
Vol 6 ◽  
Author(s):  
Harry L. Stern ◽  
Axel J. Schweiger ◽  
Margaret Stark ◽  
Jinlun Zhang ◽  
Michael Steele ◽  
...  
Keyword(s):  
High Resolution ◽  
Size Distribution ◽  
Power Law ◽  
Satellite Images ◽  
Low Resolution ◽  
Mean Power ◽  
Temporal Sampling ◽  
High Resolution Satellite Images ◽  
Wide Range ◽  
Low Resolution Images

The size distribution of ice floes in the polar seas affects the dynamics and thermodynamics of the ice cover, and ice-ocean models are beginning to include the floe size distribution (FSD) in their simulations. The FSD has previously been reported to follow a power law of the form x–α, where x is the floe size and –α characterizes how steeply the number of floes decreases as x increases. Different studies have found different values of α and different ranges of x over which the power law applies. We found that a power law describes the FSD in the Beaufort and Chukchi seas reasonably well over floe sizes from 2 to 30 km, based on 187 visible-band satellite images (resolution 250 m) acquired during spring through fall of 2013 and 2014. The mean power-law exponent goes through a seasonal cycle in which α increases in spring, peaks in July or August, and decreases in fall. June is the transition month from spring FSD to summer FSD. This cycle is consistent with the processes of floe break-up in spring followed by preferential melting of smaller floes in summer and the return of larger floes after fall freeze-up. We also analyzed 12 high-resolution satellite images acquired near the low-resolution images in space and time. We found that the FSDs from the high-resolution images follow power laws over floe sizes from 10 m to 3 km. While the power-law exponents of the corresponding high- and low-resolution images do not always match in a strict statistical sense, they suggest the plausibility that the FSD follows a single power law over a wide range of floe sizes. This study covers a larger spatial and temporal sampling space and is based on more satellite images than previous studies. Results have been used for model calibration and validation.

scholarly journals Estimates of Maize Plant Density from UAV RGB Images Using Faster-RCNN Detection Model: Impact of the Spatial Resolution

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
K. Velumani ◽  
R. Lopez-Lozano ◽  
S. Madec ◽  
W. Guo ◽  
J. Gillet ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Management Practices ◽  
Plant Density ◽  
Maize Plant ◽  
Low Resolution ◽  
High Resolution Images ◽  
Rgb Images ◽  
The Impact ◽  
Low Resolution Images

Early-stage plant density is an essential trait that determines the fate of a genotype under given environmental conditions and management practices. The use of RGB images taken from UAVs may replace the traditional visual counting in fields with improved throughput, accuracy, and access to plant localization. However, high-resolution images are required to detect the small plants present at the early stages. This study explores the impact of image ground sampling distance (GSD) on the performances of maize plant detection at three-to-five leaves stage using Faster-RCNN object detection algorithm. Data collected at high resolution (GSD≈0.3 cm) over six contrasted sites were used for model training. Two additional sites with images acquired both at high and low (GSD≈0.6 cm) resolutions were used to evaluate the model performances. Results show that Faster-RCNN achieved very good plant detection and counting (rRMSE=0.08) performances when native high-resolution images are used both for training and validation. Similarly, good performances were observed (rRMSE=0.11) when the model is trained over synthetic low-resolution images obtained by downsampling the native training high-resolution images and applied to the synthetic low-resolution validation images. Conversely, poor performances are obtained when the model is trained on a given spatial resolution and applied to another spatial resolution. Training on a mix of high- and low-resolution images allows to get very good performances on the native high-resolution (rRMSE=0.06) and synthetic low-resolution (rRMSE=0.10) images. However, very low performances are still observed over the native low-resolution images (rRMSE=0.48), mainly due to the poor quality of the native low-resolution images. Finally, an advanced super resolution method based on GAN (generative adversarial network) that introduces additional textural information derived from the native high-resolution images was applied to the native low-resolution validation images. Results show some significant improvement (rRMSE=0.22) compared to bicubic upsampling approach, while still far below the performances achieved over the native high-resolution images.

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.

Stereo Matching Algorithm Based on Pyramid Double Dynamic Programming

2014 ◽  
Vol 981 ◽  
pp. 352-355 ◽  
Author(s):  
Ji Zhou Wei ◽  
Shu Chun Yu ◽  
Wen Fei Dong ◽  
Chao Feng ◽  
Bing Xie
Keyword(s):  
Dynamic Programming ◽  
High Resolution ◽  
Stereo Matching ◽  
Control Points ◽  
Low Resolution ◽  
Matching Algorithm ◽  
Resolution Image ◽  
High Resolution Images ◽  
Candidate Control ◽  
Low Resolution Images

A stereo matching algorithm was proposed based on pyramid algorithm and dynamic programming. High and low resolution images was computed by pyramid algorithm, and then candidate control points were stroke on low-resolution image, and final control points were stroke on the high-resolution images. Finally, final control points were used in directing stereo matching based on dynamic programming. Since the striking of candidate control points on low-resolution image, the time is greatly reduced. Experiments show that the proposed method has a high matching precision.

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.

Focused Plenoptic Camera and Spatial Resolution Improving Technology

2013 ◽  
Vol 710 ◽  
pp. 419-423
Author(s):  
Juan Ning Zhao ◽  
Xiao Na Dong ◽  
Suo Chao Yuan
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Traditional Approach ◽  
Microlens Array ◽  
Low Resolution ◽  
New Approach ◽  
Plenoptic Camera ◽  
High Resolution Images

The focused plenoptic cameras based on the rays resampling of microlens array on the image formed by main lens, captures radiation on sensor includes the 4D radiance information.Because of both spatial and angular information are recorded on the sensor of fixed pixels number, when rendering image with fixed view there are limited pixels from sub_image are adopted, this results in disappointingly low resolution of the result image. Our approach presents a new approach to rendering an image with higher spatial resolution than the traditional approach, allowing us to render high resolution images that meet the high requirements.

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.

Sign in / Sign up

Export Citation Format

Share Document