scholarly journals AN ENHANCED ALGORITHM FOR AUTOMATIC RADIOMETRIC HARMONIZATION OF HIGH-RESOLUTION OPTICAL SATELLITE IMAGERY USING PSEUDOINVARIANT FEATURES AND LINEAR REGRESSION

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 115-120
Author(s):  
M. Langheinrich ◽  
P. Fischer ◽  
M. Probeck ◽  
G. Ramminger ◽  
T. Wagner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Multispectral Imaging ◽  
Satellite Image ◽  
Spectral Composition ◽  
Remote Sensing Data ◽  
Image Data ◽  
Optical Remote Sensing ◽  
Surface Reflection ◽  
Sensing Data

The growing number of available optical remote sensing data providing large spatial and temporal coverage enables the coherent and gapless observation of the earth’s surface on the scale of whole countries or continents. To produce datasets of that size, individual satellite scenes have to be stitched together forming so-called mosaics. Here the problem arises that the different images feature varying radiometric properties depending on the momentary acquisition conditions. The interpretation of optical remote sensing data is to a great extent based on the analysis of the spectral composition of an observed surface reflection. Therefore the normalization of all images included in a large image mosaic is necessary to ensure consistent results concerning the application of procedures to the whole dataset. In this work an algorithm is described which enables the automated spectral harmonization of satellite images to a reference scene. As the stable and satisfying functionality of the proposed algorithm was already put to operational use to process a high number of SPOT-4/-5, IRS LISS-III and Landsat-5 scenes in the frame of the European Environment Agency's Copernicus/GMES Initial Operations (GIO) High-Resolution Layer (HRL) mapping of the HRL Forest for 20 Western, Central and (South)Eastern European countries, it is further evaluated on its reliability concerning the application to newer Sentinel-2 multispectral imaging products. The results show that the algorithm is comparably efficient for the processing of satellite image data from sources other than the sensor configurations it was originally designed for.

scholarly journals Fusion of High- and Medium-Resolution Optical Remote Sensing Imagery and GlobeLand30 Products for the Automated Detection of Intra-Urban Surface Water

2020 ◽  
Vol 12 (24) ◽  
pp. 4037
Author(s):  
Zhi Li ◽  
Xiaomei Yang
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Automated Detection ◽  
Urban Surface ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Model Based

Intra-urban surface water (IUSW) is an indispensable resource for urban living. Accurately acquiring and updating the distributions of IUSW resources is significant for human settlement environments and urban ecosystem services. High-resolution optical remote sensing data are used widely in the detailed monitoring of IUSW because of their characteristics of high resolution, large width, and high frequency. The lack of spectral information in high-resolution remote sensing data, however, has led to the IUSW misclassification problem, which is difficult to fully solve by relying only on spatial features. In addition, with an increasing abundance of water products, it is equally important to explore methods for using water products to further enhance the automatic acquisition of IUSW. In this study, we developed an automated urban surface-water area extraction method (AUSWAEM) to obtain accurate IUSW by fusing GaoFen-1 (GF-1) images, Landsat-8 Operational Land Imager (OLI) images, and GlobeLand30 products. First, we derived morphological large-area/small-area water indices to increase the salience of IUSW features. Then, we applied an adaptive segmentation model based on the GlobeLand30 product to obtain the initial results of IUSW. Finally, we constructed a decision-level fusion model based on expert knowledge to eliminate the problem of misclassification resulting from insufficient information from high-resolution remote sensing spectra and obtained the final IUSW results. We used a three-case study in China (i.e., Tianjin, Shanghai, and Guangzhou) to validate this method based on remotely sensed images, such as those from GF-1 and Landsat-8 OLI. We performed a comparative analysis of the results from the proposed method and the results from the normalized differential water index, with average kappa coefficients of 0.91 and 0.55, respectively, which indicated that the AUSWAEM improved the average kappa coefficient by 0.36 and obtained accurate spatial patterns of IUSW. Furthermore, the AUSWAEM displayed more stable and robust performance under different environmental conditions. Therefore, the AUSWAEM is a promising technique for extracting IUSW with more accurate and automated detection performance.

scholarly journals Urban Classification Using Multi-temporal Sentinel-1 Data Based On Coherence Characteristics

2021 ◽  
Vol 37 (1) ◽  
Author(s):  
Le Minh Hang ◽  
Tran Anh Tuan
Keyword(s):  
Remote Sensing ◽  
European Space Agency ◽  
Remote Sensing Data ◽  
Image Data ◽  
Development Planning ◽  
Optical Remote Sensing ◽  
Satellite Mission ◽  
Sar Images ◽  
Sensing Data ◽  
Polarization Image

Classification urban features plays an important part in monitoring and development planning of the area. Optical remote sensing data is currently used in study land use/land cover. However, optical remote sensing data are affected by clouds and weather. Hence, it is difficult to update information. Sentinel-1 is the satellite mission which conducted by the European Space Agency (ESA). Sentinel-1 is composed of two satellites, Sentinel-1A and Sentinel-1B which carried C-band Synthetic Aperture Radar (SAR) instrument, 10m spatial resolution and provided free of charge. SAR images, which is an active microwave data, is not affected by weather, day and night. In this article, the authors present the experimental results of using coherence technique of two SAR images acquised at different times to classify urban features. The classification accuracy by using VV and VH polarization images were respectively 89% and 93%. VH polarization image data used in classification urban feature is better than VV polarization image.

scholarly journals Estimation of debris cover and its temporal variation using optical satellite sensor data: a case study in Chenab basin, Himalaya

2009 ◽  
Vol 55 (191) ◽  
pp. 444-452 ◽  
Author(s):  
A. Shukla ◽  
R.P. Gupta ◽  
M.K. Arora
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Remote Sensing Data ◽  
Image Data ◽  
Optical Remote Sensing ◽  
Glacier Area ◽  
Sensing Data ◽  
Debris Cover ◽  
Topographical Maps

AbstractDebris cover over glaciers greatly affects their rate of ablation and is a sensitive indicator of glacier health. This study focuses on estimation of debris cover over Samudratapu glacier, Chenab basin, Himalaya, using optical remote-sensing data. Remote-sensing image data of IRS-1C LISS-III (September 2001), IRS-P6 AWiFS (September 2004) and Terra ASTER (September 2004) along with Survey of India topographical maps (1963) were used in the study. Supervised classification of topographically corrected reflectance image data was systematically conducted to map six land-cover classes in the glacier terrain: snow, ice, mixed ice and debris, debris, valley rock, and water. An accuracy assessment of the classification was conducted using the ASTER visible/near-infrared data as the reference. The overall accuracies of the glacier-cover maps were found to range from 83.7% to 89.1%, whereas the individual class accuracy of debris-cover mapping was found to range from 82% to 95%. This shows that supervised classification of topographically corrected reflectance data is effective for the extraction of debris cover. In addition, a comparative study of glacier-cover maps generated from remote-sensing data (supervised classification) of September 2001 and September 2004 and Survey of India topographical maps (1963) has highlighted the trends of glacier depletion and recession. The glacier snout receded by about 756 m from 1963 to 2004, and the total glacier area was reduced by 13.7 km2 (from 110 km2 in 1963). Further, glacier retreat is found to be accompanied by a decrease in mixed ice and debris and a marked increase in debris-cover area. The area covered by valley rock is found to increase, confirming an overall decrease in the glacier area. The results from this study demonstrate the applicability of optical remote-sensing data in monitoring glacier terrain, and particularly mapping debris-cover area.

Characterization of pool evolution in Niamey degree (Niger) based on high resolution optical remote sensing data

2005 ◽  
Author(s):  
Abdelaziz Kallel ◽  
Mehrez Zribi ◽  
Sylvie Le Hégarat-Mascle ◽  
Sylvain Massuel ◽  
Luc Descroix
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Sensing Data

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market
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