scholarly journals Updating Scarce High Resolution Images with Time Series of Coarser Images: a Bayesian Data Fusion Solution

10.5772/6581 ◽  
2009 ◽  
Author(s):  
Dominique Fasbender ◽  
Valrie Obsomer ◽  
Patrick Bogaert ◽  
Pierre Defourny
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Data Fusion ◽  
High Resolution Images ◽  
Bayesian Data Fusion

scholarly journals A Change-Driven Image Foveation Approach for Tracking Plant Phenology

2020 ◽  
Vol 12 (9) ◽  
pp. 1409
Author(s):  
Ewerton Silva ◽  
Ricardo da S. Torres ◽  
Bruna Alberton ◽  
Leonor Patricia C. Morellato ◽  
Thiago S. F. Silva
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Image Sequences ◽  
Plant Phenology ◽  
Memory Storage ◽  
High Resolution Images ◽  
The Creation ◽  
Computational Resources ◽  
Space Variant

One of the challenges in remote phenology studies lies in how to efficiently manage large volumes of data obtained as long-term sequences of high-resolution images. A promising approach is known as image foveation, which is able to reduce the computational resources used (i.e., memory storage) in several applications. In this paper, we propose an image foveation approach towards plant phenology tracking where relevant changes within an image time series guide the creation of foveal models used to resample unseen images. By doing so, images are taken to a space-variant domain where regions vary in resolution according to their contextual relevance for the application. We performed our validation on a dataset of vegetation image sequences previously used in plant phenology studies.

A Spatiotemporal data Fusion method for generating a high-resolution, regular and cloud-free time series of satellite images

2020 ◽  
Author(s):  
Aojie Shen ◽  
Yanchen Bo ◽  
Duoduo Hu
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Data Fusion ◽  
Satellite Images ◽  
Free Time ◽  
Spatiotemporal Data ◽  
Kriging Interpolation ◽  
Fusion Method ◽  
Fusion Methods ◽  
Fine Resolution

<p>Scientific research of land surface dynamics in heterogeneous landscapes often require remote sensing data with high resolutions in both space and time. However, single sensor could not provide such data at both high resolutions. In addition, because of cloud pollution, images are often incomplete. Spatiotemporal data fusion methods is a feasible solution for the aforementioned data problem. However, for existing data fusion methods, it is difficult to address the problem constructed regular and cloud-free dense time-series images with high spatial resolution. To address these limitations of current spatiotemporal data fusion methods, in this paper, we presented a novel data fusion method for fusing multi-source satellite data to generate s a high-resolution, regular and cloud-free time series of satellite images.</p><p>We incorporates geostatistical theory into the fusion method, and takes the pixel value as a random variable which is composed of trend and a zero-mean second-order stationary residual. To fuse satellite images, we use the coarse-resolution image with high frequency observation to capture the trend in time, and use Kriging interpolation to obtain the residual in fine-resolution scale to provide the informative spatial information. In this paper, in order to avoid the smoothing effect caused by spatial interpolation, Kriging interpolation is performed only in time dimension. For certain region, the temporal correlation between pixels is fixed after the data reach stationary. So for getting the weight in temporal Kriging interpolation, we can use the residuals obtained from coarse-resolution images to construct the temporal covariance model. The predicted fine-resolution image can be obtained by returning the trend value of pixel to their own residual until the each pixel value was obtained. The advantage of the algorithm is to accurately predict fine-resolution images in heterogeneous areas by integrating all available information in the time-series images with fine spatial resolution.  </p><p>We tested our method to fuse NDVI of MODIS and Landsat at Bahia State where has heterogeneous landscape, and generated 8-day time series of NDVI for the whole year of 2016 at 30m resolution. By cross-validation, the average R<sup>2 </sup>and RMSE between NDVI from fused images and from observed images can reach 95% and 0.0411, respectively. In addition, experiments demonstrated that our method also can capture correct texture patterns. These promising results demonstrated this novel method can provide effective means to construct regular and cloud-free time series with high spatiotemporal resolution. Theoretically, the method can predict the fine-resolution data required on any given day. Such a capability is helpful for monitoring near-real-time land surface and ecological dynamics at the high-resolution scales most relevant to human activities.</p><p> </p>

Time series of high-resolution images enhances efforts to monitor post-fire condition and recovery, Waldo Canyon fire, Colorado, USA

2018 ◽  
Vol 27 (10) ◽  
pp. 699 ◽  
Author(s):  
Melanie K. Vanderhoof ◽  
Clifton Burt ◽  
Todd J. Hawbaker
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Vegetation Index ◽  
Burn Severity ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Object Based ◽  
Fire Condition ◽  
Normalised Difference Vegetation Index ◽  
High Resolution Images

Interpretations of post-fire condition and rates of vegetation recovery can influence management priorities, actions and perception of latent risks from landslides and floods. In this study, we used the Waldo Canyon fire (2012, Colorado Springs, Colorado, USA) as a case study to explore how a time series (2011–2016) of high-resolution images can be used to delineate burn extent and severity, as well as quantify post-fire vegetation recovery. We applied an object-based approach to map burn severity and vegetation recovery using Worldview-2, Worldview-3 and QuickBird-2 imagery. The burned area was classified as 51% high, 20% moderate and 29% low burn-severity. Across the burn extent, the shrub cover class showed a rapid recovery, resprouting vigorously within 1 year, whereas 4 years post-fire, areas previously dominated by conifers were divided approximately equally between being classified as dominated by quaking aspen saplings with herbaceous species in the understorey or minimally recovered. Relative to using a pixel-based Normalised Difference Vegetation Index (NDVI), our object-based approach showed higher rates of revegetation. High-resolution imagery can provide an effective means to monitor post-fire site conditions and complement more prevalent efforts with moderate- and coarse-resolution sensors.

scholarly journals An Unmixing-Based Bayesian Model for Spatio-Temporal Satellite Image Fusion in Heterogeneous Landscapes

2019 ◽  
Vol 11 (3) ◽  
pp. 324 ◽  
Author(s):  
Jie Xue ◽  
Yee Leung ◽  
Tung Fung
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Land Surface ◽  
Satellite Images ◽  
Satellite Image ◽  
Heterogeneous Landscapes ◽  
Bayesian Data Fusion ◽  
Spatio Temporal

Studies of land surface dynamics in heterogeneous landscapes often require satellite images with a high resolution, both in time and space. However, the design of satellite sensors often inherently limits the availability of such images. Images with high spatial resolution tend to have relatively low temporal resolution, and vice versa. Therefore, fusion of the two types of images provides a useful way to generate data high in both spatial and temporal resolutions. A Bayesian data fusion framework can produce the target high-resolution image based on a rigorous statistical foundation. However, existing Bayesian data fusion algorithms, such as STBDF (spatio-temporal Bayesian data fusion) -I and -II, do not fully incorporate the mixed information contained in low-spatial-resolution pixels, which in turn might limit their fusion ability in heterogeneous landscapes. To enhance the capability of existing STBDF models in handling heterogeneous areas, this study proposes two improved Bayesian data fusion approaches, coined ISTBDF-I and ISTBDF-II, which incorporate an unmixing-based algorithm into the existing STBDF framework. The performance of the proposed algorithms is visually and quantitatively compared with STBDF-II using simulated data and real satellite images. Experimental results show that the proposed algorithms generate improved spatio-temporal-resolution images over STBDF-II, especially in heterogeneous areas. They shed light on the way to further enhance our fusion capability.

scholarly journals Advances on coastal erosion assessment from satellite earth observations: exploring the use of Sentinel products along with very high resolution sensors

2020 ◽  
pp. 412-421
Author(s):  
Paula Gomes da Silva ◽  
Anne-Laure Beck ◽  
Jara Martinez Sanchez ◽  
Raúl Medina Santanmaria ◽  
Martin Jones ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
High Frequency ◽  
Coastal Erosion ◽  
Satellite Images ◽  
Horizontal Shift ◽  
Earth Observations ◽  
High Resolution Images ◽  
Very High ◽  
Sentinel 2

This work proposes the use of automatic co-registered satellite images to obtain large, high frequency and highly accurate shorelines time series. High resolution images are used to co-register Landsat and Sentinel-2 images. 90% of the co-registered images presented vertical and horizontal shift lower than 3 m. Satellite derived shorelines presented errors lower than mission’s precision. A discussion is presented on the applicability of those shorelines through an application to Tordera Delta (Spain).

scholarly journals OBJECT-BASED FOREST COVER MONITORING USING GAOFEN-2 HIGH RESOLUTION SATELLITE IMAGES

2016 ◽  
Vol XLI-B8 ◽  
pp. 1437-1440 ◽  
Author(s):  
S. M. Li ◽  
Z. Y. Li ◽  
E. X. Chen ◽  
Q. W. Liu
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Forest Cover ◽  
Reference Data ◽  
Spectral Characteristics ◽  
Difference Image ◽  
Regional Area ◽  
Object Based ◽  
High Resolution Satellite Images ◽  
High Resolution Images

Forest cover monitoring is an important part of forest management in local or regional area. The structure and tones of forest can be identified in high spatial remote sensing images. When forests cover change, the spectral characteristics of forests is also changed. In this paper a method on object-based forest cover monitoring with data transformation from time series of high resolution images is put forward. First the NDVI difference image and the composite of PC3,PC4, PC5 of the stacked 8 layers of time series of high resolution satellites are segmented into homogeneous objects. With development of the object-based ruleset classification system, the spatial extent of deforestation and afforestation can be identified over time across the landscape. Finally the change accuracy is achieved with reference data.

scholarly journals AUTOMATED DETECTION OF OIL DEPOTS FROM HIGH RESOLUTION IMAGES: A NEW PERSPECTIVE

2015 ◽  
Vol II-3/W4 ◽  
pp. 149-156 ◽  
Author(s):  
A. O. Ok ◽  
E. Başeski
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Test Site ◽  
Radial Symmetry ◽  
Google Earth ◽  
Industrial Test ◽  
New Approach ◽  
High Resolution Images ◽  
Overall Performance ◽  
New Perspective

This paper presents an original approach to identify oil depots from single high resolution aerial/satellite images in an automated manner. The new approach considers the symmetric nature of circular oil depots, and it computes the radial symmetry in a unique way. An automated thresholding method to focus on circular regions and a new measure to verify circles are proposed. Experiments are performed on six GeoEye-1 test images. Besides, we perform tests on 16 Google Earth images of an industrial test site acquired in a time series manner (between the years 1995 and 2012). The results reveal that our approach is capable of detecting circle objects in very different/difficult images. We computed an overall performance of 95.8% for the GeoEye-1 dataset. The time series investigation reveals that our approach is robust enough to locate oil depots in industrial environments under varying illumination and environmental conditions. The overall performance is computed as 89.4% for the Google Earth dataset, and this result secures the success of our approach compared to a state-of-the-art approach.

FeXIV Line Emission Polarization of the July 11, 1991 Solar Corona

1994 ◽  
Vol 144 ◽  
pp. 541-547
Author(s):  
J. Sýkora ◽  
J. Rybák ◽  
P. Ambrož
Keyword(s):  
High Resolution ◽  
Solar Corona ◽  
Emission Line ◽  
Solar Eclipse ◽  
White Light ◽  
Preliminary Analysis ◽  
Line Emission ◽  
Total Solar Eclipse ◽  
Degree Of Polarization ◽  
High Resolution Images

AbstractHigh resolution images, obtained during July 11, 1991 total solar eclipse, allowed us to estimate the degree of solar corona polarization in the light of FeXIV 530.3 nm emission line and in the white light, as well. Very preliminary analysis reveals remarkable differences in the degree of polarization for both sets of data, particularly as for level of polarization and its distribution around the Sun’s limb.

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