Validation of the Calibration Coefficient of the GaoFen-1 PMS Sensor Using the Landsat 8 OLI

Abstract

Abstract:The panchromatic and multispectral (PMS) sensor is an optical imaging sensor aboard the Gao Fen-1 (GF-1) satellite. This work describes an approach to validate the calibration coefficients of the PMS sensors based on the image data of the Landsat 8 Operational Land Imager (OLI). Two image pairs, one obtained over the Dunhuang test site and the other over the Golmud test site, were used in this paper. Two spectral band adjustment factors (SBAF), given as the radiance SBAF and reflectance SBAF, were applied to correct the differences in the respective images caused by the relative spectral responses of the PMS sensor and OLI. Uncertainties in the SBAF values arising from atmospheric parameters and the absence of a measured ground spectrum were analyzed in this paper. The results show that the average relative differences of top-of-atmosphere radiance and reflectance values for each band between the PMS sensor and OLI images are 2%-5% for most bands, after SBAF correction with a measured ground spectrum or fitted spectrum. It is demonstrated that the OLI image can be used to validate the calibration coefficient of the PMS sensor, even if the image pairs are not imaged on the same day.

Radiometric Cross-Calibration of the Chilean Satellite FASat-C Using RapidEye and EO-1 Hyperion Data and a Simultaneous Nadir Overpass Approach

“…This has ensured that the most homogeneous and spatially-autocorrelated areas of Frenchman Flat were used for the cross-calibration of FASat-C ( Figure 6). This is in line with the research of Feng et al [56] and Gao et al [58] After excluding outliers, the following number of homogeneous samples were used for the calibration: 127 for the blue (B1), 125 for the green (B2), 128 for the red (B3) and 129 for the NIR (B4) bands, respectively. Since the amount of photons reaching the sensor and their energy vary according to wavelength, each band was considered as an independent experiment.…”

“…The previous steps were repeated until areas with the characteristics of the specific LED-Based Spectral Surface Monitoring (LSpec) calibration site were delineated. In order to reduce the influence of registration errors, caused by differences in spatial resolution [58,72], these criteria were applied for the identification of the most homogeneous areas, from which the samples for the cross-calibration of FASat-C were extracted.…”

Selecting Appropriate Spatial Scale for Mapping Plastic-Mulched Farmland with Satellite Remote Sensing Imagery

“…The GaoFen-1 (GF-1) satellite was launched on 26 April 2013, and represents the first satellite for the civilian High-Definition Earth Observation Satellite (HDEOS) program implemented in China [22]. The GF-1 satellite carries two high spatial resolution cameras (GF1-PMS1 and GF1_PMS2) with 60 km width and four medium spatial resolution wide field-of-view cameras (WFV) with 800 km width.…”

Ship Classification from Overhead Imagery using Synthetic Data and Domain Adaptation

“…While there are many feature extraction and classification methods utilized in that work, we will focus our baselines in this paper on the higher performing methods. For feature extraction, Hierarchical Multi-scale Local Binary Pattern (HMLBP) [13]- [15], Multi-linear Principal Components Analysis (MPCA) [16], [17], and Histograms of Oriented Gradients (HOG) [18] will be used. Our baseline classifiers will be the Support Vector Machine (SVM) [5] and Sparse Representation-based Classification (SRC) [19].…”