Analyzing the effect of synthetic scene resolution, sampling interval, and signal-to-noise ratio on hyperspectral imaging sensor simulations

A Combined Quantitative Evaluation Model for the Capability of Hyperspectral Imagery for Mineral Mapping

To analyze the influence factors of hyperspectral remote sensing data processing, and quantitatively evaluate the application capability of hyperspectral data, a combined evaluation model based on the physical process of imaging and statistical analysis was proposed. The normalized average distance between different classes of ground cover is selected as the evaluation index. The proposed model considers the influence factors of the full radiation transmission process and processing algorithms. First- and second-order statistical characteristics (mean and covariance) were applied to calculate the changes for the imaging process based on the radiation energy transfer. The statistical analysis was combined with the remote sensing process and the application performance, which consists of the imaging system parameters and imaging conditions, by building the imaging system and processing models. The season (solar zenith angle), sensor parameters (ground sampling distance, modulation transfer function, spectral resolution, spectral response function, and signal to noise ratio), and number of features were considered in order to analyze the influence factors of the application capability level. Simulated and real data collected by Hymap in the Dongtianshan area (Xinjiang Province, China), were used to estimate the proposed model’s performance in the application of mineral mapping. The predicted application capability of the proposed model is consistent with the theoretical analysis.

Spectral super-resolution reflectance retrieval from remotely sensed imaging spectrometer data

Existing atmospheric correction methods retrieve surface reflectance keeping the same nominal spectral response functions (SRFs) as that of the airborne/spaceborne imaging spectrometer radiance data. Since the SRFs vary dependent on sensor type and configuration, the retrieved reflectance of the same ground object varies from sensor to sensor as well. This imposes evident limitations on data validation efforts between sensors at surface reflectance level. We propose a method to retrieve super-resolution reflectance at the surface, by combining the first-principles atmospheric correction method FLAASH (fast line-of-sight atmospheric analysis of spectral hypercubes) with spectral super-resolution of imaging spectrometer radiance data. This approach is validated by comparing airborne AVIRIS (airborne visible/infrared imaging spectrometer) and spaceborne Hyperion data. The results demonstrate that the super-resolution reflectance in spectral bands with sufficiently high signal-to-noise ratio (SNR) serves as intermediate quantity to cross validate data originating from different imaging spectrometers.

A digital sensor simulator of the pushbroom Offner hyperspectral imaging spectrometer

Sensor simulators can be used in forecasting the imaging quality of a new hyperspectral imaging spectrometer, and generating simulated data for the development and validation of the data processing algorithms. This paper presents a novel digital sensor simulator for the pushbroom Offner hyperspectral imaging spectrometer, which is widely used in the hyperspectral remote sensing. Based on the imaging process, the sensor simulator consists of a spatial response module, a spectral response module, and a radiometric response module. In order to enhance the simulation accuracy, spatial interpolation-resampling, which is implemented before the spatial degradation, is developed to compromise the direction error and the extra aliasing effect. Instead of using the spectral response function (SRF), the dispersive imaging characteristics of the Offner convex grating optical system is accurately modeled by its configuration parameters. The non-uniformity characteristics, such as keystone and smile effects, are simulated in the corresponding modules. In this work, the spatial, spectral and radiometric calibration processes are simulated to provide the parameters of modulation transfer function (MTF), SRF and radiometric calibration parameters of the sensor simulator. Some uncertainty factors (the stability, band width of the monochromator for the spectral calibration, and the integrating sphere uncertainty for the radiometric calibration) are considered in the simulation of the calibration process. With the calibration parameters, several experiments were designed to validate the spatial, spectral and radiometric response of the sensor simulator, respectively. The experiment results indicate that the sensor simulator is valid.