Huijie Zhao

Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion

In this study, backward and forward fluorescence radiance within the emission spectrum of 640–850 nm were measured for leaves of soybean, cotton, peanut and wheat using a hyperspectral spectroradiometer coupled with an integration sphere. Fluorescence parameters of crop leaves were retrieved from the leaf hyperspectral measurements by inverting the FluorMODleaf model, a leaf-level fluorescence model able to simulate chlorophyll fluorescence spectra for both sides of leaves. This model is based on the widely used and validated PROSPECT (leaf optical properties) model. Firstly, a sensitivity analysis of the FluorMODleaf model was performed to identify and quantify influential parameters to assist the strategy for the inversion. Implementation of the Extended Fourier Amplitude Sensitivity Test (EFAST) method showed that the leaf chlorophyll content and the fluorescence lifetimes of photosystem I (PSI) and photosystem II (PSII) were the most sensitive parameters among all eight inputs of the FluorMODleaf model. Based on results of sensitivity analysis, the FluorMODleaf model was inverted using the leaf fluorescence spectra measured from both sides of crop leaves. In order to achieve stable inversion results, the Bayesian inference theory was applied. The relative absorption cross section of PSI and PSII and the fluorescence lifetimes of PSI and PSII of the FluorMODleaf model were retrieved with the Bayesian inversion approach. Results showed that the coefficient of determination (R2) and root mean square error (RMSE) between the fluorescence signal reconstructed from the inverted fluorescence parameters and measured in the experiment were 0.96 and 3.14 × 10−6 W·m−2·sr−1·nm−1, respectively, for backward fluorescence, and 0.92 and 3.84 × 10−6 W·m−2·sr−1·nm−1 for forward fluorescence. Based on results, the inverted values of the fluorescence parameters were analyzed, and the potential of this method was investigated.

Local density-based anomaly detection in hyperspectral image

Abstract. A local density-based anomaly detection (LDAD) method is proposed. LDAD is a nonparameter model-based method, which utilizes the pixel’s local density in hyperspectral images as a criterion to determine the pixel’s anomalousness. In this method, the local density is calculated as a function of the spectral distance between pixels. Distinct from the statistical-based method, there are no assumptions made on the background distributions. Due to the pairwise distance calculation between pixels, LDAD’s computational complexity is quadratic to the total number of pixels. To improve the efficiency, an optimization strategy by pruning is implemented to reduce the unnecessary computational costs. Experiments on real hyperspectral image suggest that the proposed anomaly detector can achieve better detection performance than its counterparts, while keeping the computational cost relatively low by applying the optimization.

Pixel-size-varying method for simulation of remote sensing images

Abstract Image simulation plays an important role in remote sensing system design and data processing algorithm development, supposing that the fidelity of the simulated images is high enough. Many remote sensing image simulation models generate the geometric characteristics of the images through a georeferencing, convolution, and resampling process. In the georeferencing and resampling steps, each pixel is taken as a point, meanwhile a shift-invariant detector point spread function (PSF) is used in the convolution step. It omits the footprint size variation caused by the ground relief, earth curvature, and oblique viewing. To improve the fidelity of the simulated images, a pixel-size-varying (PSV) method was proposed: the four corners of each detector in a whiskbroom, pushbroom, or staring imaging sensor are separately considered in the georeferencing step, the sensor detector PSF is abandoned from the convolution step, and then the PSV sampling is simulated using an overlapping-area-weighted sum of the oversampled pixels. A validation experiment was conducted in simulating EO-1 Hyperion L1R data from georeferenced HyMap reflectance data. It showed that the PSV method outperforms the traditional method in the spectral aspect and is equal to the traditional method in other aspects, by comparing the simulated images with the actual one.

Bidirectional reflectance effects over flat land surface from the charge-coupled device data sets of the HJ-1A and HJ-1B satellites

Abstract The HJ-1A and HJ-1B satellites were launched successfully on September 6, 2008. For effective monitoring of the environmental and natural disasters, both HJ-1A and HJ-1B carry a charge-coupled device (CCD) sensor, with each CCD sensor containing two cameras, which results in a ground swath of about 700 km for each satellite. The CCD can make cross-track multiple view angle measurements with a field of view of > 40 u2009 u2009 deg . The Earth’s surface can be covered completely within 48 h in four spectral bands from 0.43 to 0.90 μm. We have presented a method of extracting the hemispherical-directional reflectance factor (HDRF) from CCD imagery and normalizing HDRF to a standard geometric situation. After geometric correction and registration, radiometric calibration, and correction for atmospheric effects, multitemporal HDRFs were obtained for the flat land surface located in Northern China with different land cover types. The angular observations were extracted from a series of overpasses of the CCD aboard HJ-1A and HJ-1B. We then inverted the HDRFs by the semiempirical kernel-driven bidirectional reflectance distribution function (BRDF) model and normalized the HDRFs to nadir-viewing direction. This study shows the significance of directional effects in the HJ-1A and HJ-1B CCD data and the feasibility of normalizing HDRFs’ CCD data when the angular effects must be taken into account.

Integrated System for Auto-Registered Hyperspectral and 3D Structure Measurement at the Point Scale

Hyperspectral and 3D structure measurement are among the active research areas of remote sensing in recent years. The combination of these two kinds of information can provide improved outcomes distinctly, which is widely used in vegetation physiology, precision agriculture and radiative transfer modeling. However, the registration and synchronization has been overlooked in data acquisition. The mismatched characteristics have limited the potential application of the hyperspectral and 3D structure data as a complete data set. This paper proposes a laboratory prototype which can integrate the hyperspectral and 3D structure measurement at the point scale. The prism dispersion and laser triangulation ranging are performed in a common optical path as a result of the coplanar design of the critical optical devices. The hyperspectral data and depth data of the same object point are acquired from the same focal plane, which makes the data auto-registered spatially and temporally. Test experiment verifies the accuracy of the data provided by the prototype and the actual measurement experiment demonstrates the feasibility of the design in vegetation observation.

Identification of the man-made barium copper silicate pigments among some ancient Chinese artifacts through spectroscopic analysis

This article describes the complementary application of non-invasive micro-Raman spectroscopy and energy dispersive X-ray fluorescence spectrometry to the characterization of some ancient Chinese silicate artifacts. A total of 28 samples dated from fourth century BC to third century AD were analyzed. The results of chemical analysis showed that the vitreous PbO-BaO-SiO2 material was used to sinter these silicate artifacts. The barium copper silicate pigments including BaCuSi4O10, BaCuSi2O6 and BaCu2Si2O7 were widely identified from colorful areas of the samples by Raman spectroscopy. In addition, other crystalline phases such as Fe2O3, BaSi2O5, BaSO4, PbCO3 and quartz were also identified. The present study provides very valuable information to trace the technical evolution of man-made barium copper silicate pigments and their close relationship with the making of ancient PbO-BaO-SiO2 glaze and glass.

The analysis of errors for field experiment based on POV-Ray

The experiment plays an important role in the remote sensing applications and theoretical studies. In this study, the image rendering software POV-Ray was used to investigate the errors in the process of near-surface remote sensing experiment, by analyzing the features of four components fractions (sunlit leaves, shaded leaves, sunlit soil and shaded soil) of typical row plants. The study shows: the model of pointing the sensor toward the targets while moving along the track induces the lowest errors among the three commonly-adopted experimental modes to sample the directional information of the targets; the mode of rotating the sensor around the fixing point brings about fluctuations around the true values, due to the variations of the targets during the experiment; increasing the distance from the sensor to the targets can improve the precision; however, it is not the larger, the better.

Analysis of noise impact on geo-object recognition in infrared bands using simulated data

Infrared spectrums play an important role in the information extraction of rock and minerals. Spectrum simulation is a fundamental issue of land surface scene simulation and image simulation of remote sensing systems. Signal to Noise Ratio is regarded as an essential parameter of instrument and remote sensing image. In this study, we used MODTRAN to simulate apparent radiance and different levels of additive white Gaussian noise was added to the simulated spectrum. In the section of noise impact on object recognition, Spectral Feature Fitting was chosen to compare the fit of simulated spectra with different noise levels to reference apparent radiance spectra without noise. Relative error is also calculated for the accuracy assessment which is helpful for validation and improvement of instrument parameters.

Simulation of remote sensing imaging motion blur based on image motion vector field

Abstract The motion blur simulation technique is widely used in remote sensing of an image chain simulation. However, the traditional method, which models the motion blur through a point spread function (PSF), is not precise enough when the imaging area is rugged or the motion of the platform is unstable. A physically based simulation model of motion blur is proposed. The model uses an image motion vector (IMV) field to describe the relative motion presented on the image plane during the exposure time. Based on the IMV field, the opto-electrons blurring model is built to simulate the blurring effect. A physical experiment was made to validate the model. The experiment result demonstrates that the simulation result generated by the model provided is more precise than the traditional PSF method, and a more complex motion status can be presented by the proposed method.

Correction for remaining effects in push-broom Hyperspectral radiance data

Systematic errors remaining in Hyperspectral radiance data degrade the data quality and the ability in subsequent application, thereby resulting in inconvenience for users. In order to remove errors, a correction chain is established. Remaining effects related to nonideal characteristics such as abnormal pixels and image nonuniformity, stripes, smile and keystone properties are investigated. First, based on image chain approach of push-broom Hyperspectral sensors, the backgrounds and causes of remaining effects are presented. Then after the influence of each remaining effect and the correction algorithm on Hyperspectral data is analyzed and the relation between the individual processing is confirmed in proper sequence. Finally, the correction chain has been applied to the airborne Push-broom Hyperspectral Imager (PHI) data. Experimental results indicate that the visual effects are enhanced and the SNR is increased with 91.9% maximum, and the sharp peaks of reflectance spectral profiles are eliminated. The correction chain can correct the remaining effects and improve the quality of Hyperspectral data, as well as the performance of image subsequent application.