A Spatio-spectral Data Fusion Method for the Detection of Urban Areas in optical High Resolution Images by a Novel MRF Model

2006 ◽  
Author(s):  
F. Causa ◽  
G. Moser ◽  
S. Serpico
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Spectral Data ◽  
Urban Areas ◽  
Fusion Method ◽  
High Resolution Images

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>

scholarly journals OBJECTS GROUPING FOR SEGMENTATION OF ROADS NETWORK IN HIGH RESOLUTION IMAGES OF URBAN AREAS

2016 ◽  
Vol XLI-B7 ◽  
pp. 897-902 ◽  
Author(s):  
M. Maboudi ◽  
J. Amini ◽  
M. Hahn
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Urban Areas ◽  
Traffic Management ◽  
Large Scale ◽  
Route Planning ◽  
Technological Advancement ◽  
Image Objects ◽  
High Resolution Images ◽  
Road Segments

Updated road databases are required for many purposes such as urban planning, disaster management, car navigation, route planning, traffic management and emergency handling. In the last decade, the improvement in spatial resolution of VHR civilian satellite sensors – as the main source of large scale mapping applications – was so considerable that GSD has become finer than size of common urban objects of interest such as building, trees and road parts. This technological advancement pushed the development of “Object-based Image Analysis (OBIA)” as an alternative to pixel-based image analysis methods. <br><br> Segmentation as one of the main stages of OBIA provides the image objects on which most of the following processes will be applied. Therefore, the success of an OBIA approach is strongly affected by the segmentation quality. In this paper, we propose a purpose-dependent refinement strategy in order to group road segments in urban areas using maximal similarity based region merging. For investigations with the proposed method, we use high resolution images of some urban sites. The promising results suggest that the proposed approach is applicable in grouping of road segments in urban areas.

scholarly journals OBJECTS GROUPING FOR SEGMENTATION OF ROADS NETWORK IN HIGH RESOLUTION IMAGES OF URBAN AREAS

2016 ◽  
Vol XLI-B7 ◽  
pp. 897-902 ◽  
Author(s):  
M. Maboudi ◽  
J. Amini ◽  
M. Hahn
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Urban Areas ◽  
Traffic Management ◽  
Large Scale ◽  
Route Planning ◽  
Technological Advancement ◽  
Image Objects ◽  
High Resolution Images ◽  
Road Segments

Updated road databases are required for many purposes such as urban planning, disaster management, car navigation, route planning, traffic management and emergency handling. In the last decade, the improvement in spatial resolution of VHR civilian satellite sensors – as the main source of large scale mapping applications – was so considerable that GSD has become finer than size of common urban objects of interest such as building, trees and road parts. This technological advancement pushed the development of “Object-based Image Analysis (OBIA)” as an alternative to pixel-based image analysis methods. <br><br> Segmentation as one of the main stages of OBIA provides the image objects on which most of the following processes will be applied. Therefore, the success of an OBIA approach is strongly affected by the segmentation quality. In this paper, we propose a purpose-dependent refinement strategy in order to group road segments in urban areas using maximal similarity based region merging. For investigations with the proposed method, we use high resolution images of some urban sites. The promising results suggest that the proposed approach is applicable in grouping of road segments in urban areas.

scholarly journals Shadow detection improvement using spectral indices and morphological operators in high resolution images from urban areas

2015 ◽  
Vol XL-7/W3 ◽  
pp. 587-592 ◽  
Author(s):  
S. C. Azevedo ◽  
E. A. Silva ◽  
M. M. Pedrosa
Keyword(s):  
High Resolution ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Color Space ◽  
Tall Buildings ◽  
Shadow Detection ◽  
Detection Accuracy ◽  
Spectral Indices ◽  
High Resolution Images

While high-resolution remote sensing images have increased application possibilities for urban studies, the large number of shadow areas has created challenges to processing and extracting information from these images. Furthermore, shadows can reduce or omit information from the surface as well as degrading the visual quality of images. The pixels of shadows tend to have lower radiance response within the spectrum and are often confused with low reflectance targets. In this work, a shadow detection method was proposed using a morphological operator for dark pattern identification combined with spectral indices. The aims are to avoid misclassification in shadow identification through properties provided by them on color models and, therefore, to improve shadow detection accuracy. Experimental results were tested applying the panchromatic and multispectral band of WorldView-2 image from São Paulo city in Brazil, which is a complex urban environment composed by high objects like tall buildings causing large shadow areas. Black top-hat with area injunction was applied in PAN image and shadow identification performance has improved with index as Normalized Difference Vegetation Index (NDVI) and Normalized Saturation-Value Difference Index (NSDVI) ratio from HSV color space obtained from pansharpened multispectral WV-2 image. An increase in distinction between shadows and others objects was observed, which was tested for the completeness, correctness and quality measures computed, using a created manual shadow mask as reference. Therefore, this method can contribute to overcoming difficulties faced by other techniques that need shadow detection as a first necessary preprocessing step, like object recognition, image matching, 3D reconstruction, etc.

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