3 research outputs found
Cross-Calibration of GF-1/WFV over a Desert Site Using Landsat-8/OLI Imagery and ZY-3/TLC Data
The wide field of view (WFV) is an optical imaging sensor on-board the Gao Fen 1 (GF-1). The WFV lacks an on-board calibrator, so on-orbit radiometric calibration is required. Zhong et al. proposed a method for cross-calibrating the charge-coupled device on-board the Chinese Huan Jing 1 (HJ-1/CCD) that can be applied to the GF-1/WFV. However, the accuracy is limited because of the wider radiometric dynamic range and the higher spatial resolution of the GF-1/WFV. Therefore, Landsat-8 Operational Land Imager (OLI) imagery with a radiometric resolution similar to that of the GF-1/WFV and DEM extracted from ZY-3 three-line array panchromatic camera (TLC) with a higher spatial resolution were used. A calibration site with uniform surface material and a natural topographic variation was selected, and a model of this site’s bidirectional reflectance distribution function (BRDF) was developed. The model has excellent agreement with the real situation, as shown by the comparison of the simulations to the actual OLI surface reflectance. Then, the model was used to calibrate the WFV. Compared with the TOA reflectance from synchronized Landsat-8/OLI images, all errors calculated with the calibration coefficients retrieved in this paper are less than 5%, much less than the errors calculated with the calibration coefficients given by the China Centre for Resource Satellite Data and Application (CRESDA)
Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery
The wide field of view (WFV) imaging system onboard the Chinese GaoFen-1
(GF-1) optical satellite has a 16-m resolution and four-day revisit cycle for
large-scale Earth observation. The advantages of the high temporal-spatial
resolution and the wide field of view make the GF-1 WFV imagery very popular.
However, cloud cover is an inevitable problem in GF-1 WFV imagery, which
influences its precise application. Accurate cloud and cloud shadow detection
in GF-1 WFV imagery is quite difficult due to the fact that there are only
three visible bands and one near-infrared band. In this paper, an automatic
multi-feature combined (MFC) method is proposed for cloud and cloud shadow
detection in GF-1 WFV imagery. The MFC algorithm first implements threshold
segmentation based on the spectral features and mask refinement based on guided
filtering to generate a preliminary cloud mask. The geometric features are then
used in combination with the texture features to improve the cloud detection
results and produce the final cloud mask. Finally, the cloud shadow mask can be
acquired by means of the cloud and shadow matching and follow-up correction
process. The method was validated using 108 globally distributed scenes. The
results indicate that MFC performs well under most conditions, and the average
overall accuracy of MFC cloud detection is as high as 96.8%. In the contrastive
analysis with the official provided cloud fractions, MFC shows a significant
improvement in cloud fraction estimation, and achieves a high accuracy for the
cloud and cloud shadow detection in the GF-1 WFV imagery with fewer spectral
bands. The proposed method could be used as a preprocessing step in the future
to monitor land-cover change, and it could also be easily extended to other
optical satellite imagery which has a similar spectral setting.Comment: This manuscript has been accepted for publication in Remote Sensing
of Environment, vol. 191, pp.342-358, 2017.
(http://www.sciencedirect.com/science/article/pii/S003442571730038X