As a new hyperspectral remote sensing technology, the thermal infrared hyperspectral remote sensing has the ability to observe the surface day and night. Instead of the surface temperature retrieval, the ultimate goal of the hyperspectral thermal infrared remote sensing data is to retrieve surface emissivity which can be used for mineral detection, ground parameters inversion and target identification and so on. Due to the inevitable atmospheric influence during the data acquisition process of thermal infrared hyperspectral imager, the atmospheric radiation information is included in the hyperspectral thermal infrared remote sensing data. The atmospheric correction with high precision is the premise of effectively implementing the subsequent applications. The aircraft, especially the unmanned aerial vehicle rising in recent years, is an important platform for remote sensing because of its flexibility, immunity to the cloud cover and high spatial resolution. The aircraft flies through the atmosphere when working, which makes the direct measurement possible for the atmosphere. On the basis of summarizing the features of the atmospheric correction methods for the hyperspectral thermal infrared remote sensing data, and the principle and process of atmospheric radiative transfer, the paper proposed an overall vision for the atmospheric correction based on the measured atmospheric downwelling radiance at the aircraft flight height, designed an atmospheric correction system for airborne thermal infrared imaging spectrometer, and stated the atmospheric correction procedure of hyperspectral thermal infrared remote sensing data using the system. The proposed system and procedure for atmospheric correction are able to acquire the atmospheric downwelling thermal infrared radiation information in real time, so without too many assumptions, it is possible to dramatically increase the precision of the atmospheric correction for the airborne hyperspectral thermal infrared remote sensing data and gives a promise to the objectivity of the data.
In view of the confusing problem of urban river network water and building shadows in hyperspectral images, we analyzed typical shadow and water spectrum in AISA hyperspectral image. On the basis of Normalized Difference Vegetation Index (NDVI), the 588 nm height factor was introduced to constitute an anti-shadow water extraction method (ASWEM). Compared with NDVI extraction results, this method can effectively suppress shadows, especially those cast in buildings, improve water extraction accuracy and reduce water body commission error. The commission error is reduced from 45% to 10.4%, and Kappa coefficient is increased from 0.664 to 0.863. The change of spatial scale has a significant impact on the water extraction results. The lower the image resolution, the more serious the water leakage is, and some small rivers will not be able to extract. However, due to the influence of the mixed pixels, the spectral characteristics of the shadows are weakened to some extent, and the commission error is reduced. As the resolution decreases further, the number and mixing of mixed pixels increases, and the commission error increases.
Eccentric angle between IMU and the collimation axis of airborne sensor is one of main reasons causing geometric correction error. Currently, the application of the hyperspectral and LIDAR integrated system are greatly affected by lack of universal calibration methods which can calibrate the hyperspectral data and LiDAR data simultaneously. An eccentric angle calibration model for hyperspectral and LiDAR integrated system is proposed, meanwhile a self-calibration method using “#” shaped flight strip is designed to validate the new calibration model. Firstly, the homogeneous points are searched from all geometrically corrected flight strips by automatic matching methods. Secondly, control points obtained by averaging the coordinates of the homogeneous points are applied to solve the calibration model to get the eccentric angle. Finally, the point clouds are corrected geometrically with the solved eccentric angle, then the above steps are repeated until the solved eccentric angles are stable. An experiment was carried to testify the new calibration model and resolving method, which shows that the proposed model is of high-precision, validity and the resolving method is of fast convergence. The airborne sensors can acquire a plane precision of 0.807 m (about 1.2 pixels) without any ground control points, and the LiDAR can acquire a plane precision of 0.437 m and elevation precision of 0.15m.
The inherent optical property is a significant bridge between the hyperspectral remote sensing data and water color and water quality parameters. Based on the water optical radiation transfer process and existing quasi-analytical algorithm (QAA), this study provides an improved algorithm, namely a linear spectral backscattering coefficient constraint quasianalytical algorithm (LSBCC-QAA), suitable for the retrieval of inherent optical properties for turbid inland waters to address the deficiency of the QAA on the retrieval of inherent optical properties for turbid inland waters. LSBCC-QAA uses the water-leaving reflectance of the bands between 1600 and 1700 nm to estimate the water surface reflectance of the bands between 400 and 900 nm and selects 700~850 nm as the reference wavelengths to estimate the water backscattering coefficients, taking full advantage of the continuity of the backscattering coefficient spectrum. The preliminary validated results show that the particle absorption coefficient, particle backscattering coefficient and phytoplankton absorption coefficient retrieved by LSBCC-QAA are more consistent with the actual situation than those retrieved by the common QAA_v6 algorithm or QAA-Turbid algorithm. Compared with the measured particle diffuse attenuation coefficient, the error of the LSBCC-QAA retrieved particle diffuse attenuation coefficient ranges from 16.0% to 22.9%, and the average error is 18.4%.
Accurate radiometric calibration for thermal infrared hyperspectral data is the precondition for further quantitative applications. A thermal infrared hyperspectral field calibration method which is based on non-uniformity repair had been proposed in this paper. Firstly correct global non-uniformity phenomenon caused by detector response difference, with moment matching algorithm; Secondly correct local non-uniformity phenomenon caused by the environment changes, with department moment matching algorithm; thirdly design an aviation experimental, use outfield target to calculate every band’s calibration coefficient. The thermal infrared hyperspectral field experiments show that this method can effectively eliminate the phenomenon of non-uniformity of thermal infrared hyperspectral images, and get high accuracy radiometric calibration results.
Majority of pixels, in the nature, are non-isothermal in three dimensions, especially for the pixels in meter-scale, tens- meter-scale or hundreds-meter-scale which are paid extensive attention by the researchers in geoscience field. The three-dimensional non-isothermal phenomenon even exists in some pixels in centimeter-scale. For the geosciencific researches, it is significant to determine the component temperatures of a pixel precisely. The airborne WSIS (Wide Spectrum Imaging Spectrometer) data with VNIR (visible-near infrared), SWIR (short-wave infrared) and TIR (thermal infrared) bands were used in the study. First, the components of all the pixels in the image were determined by the linear mixing method. Second, each component emissivity of each pixel was calculated based on an emissivity priori knowledge base. Last, a temperature and emissivity separation algorithm was used to inverse the mean temperature of each pixel, regarded as initial value, the Planck function was linearized to construct a multi-band equation set, and the component temperatures of every pixel were inversed by the Bayesian retrieval technique. The results suggest that the inversion precision of the pixel component temperatures is improved effectively by the Bayesian retrieval technique with the assistance of the VNIR and SWIR hyperspectral remote sensing data.
The cooling water discharged from the coastal plants flow into the sea continuously, whose temperature is higher than original sea surface temperature (SST). The fact will have non-negligible influence on the marine environment in and around where the plants site. Hence, it’s significant to monitor the temporal and spatial variation of the warm-water discharge for the assessment of the effect of the plant on its surrounding marine environment. The paper describes an approach for the dynamic monitoring of the warm-water discharge of coastal plants based on the airborne high-resolution thermal infrared remote sensing technology. Firstly, the geometric correction was carried out for the thermal infrared remote sensing images acquired on the aircraft. Secondly, the atmospheric correction method was used to retrieve the sea surface temperature of the images. Thirdly, the temperature-rising districts caused by the warm-water discharge were extracted. Lastly, the temporal and spatial variations of the warm-water discharge were analyzed through the geographic information system (GIS) technology. The approach was applied to Qinshan nuclear power plant (NPP), in Zhejiang Province, China. In considering with the tide states, the diffusion, distribution and temperature-rising values of the warm-water discharged from the plant were calculated and analyzed, which are useful to the marine environment assessment.
Most of the pixels in thermal infrared remote sensing images are three-dimensional non-isothermal pixel, especially for the pixels with the size of meters, tens of meters or hundreds of meters which have received widespread attention in geoscience and remote sensing. Even though the sizes of some pixels reach centimeters, the three-dimensional non-isothermal phenomenon may still arise. So, it is very important to accurately determine the component temperatures in one pixel for the related researches in geoscience. The remote sensing data used to carry out the related inversion experiments in this paper was the airborne remote sensing data obtained by WSIS (Wide Spectrum Imaging Spectrometer) the imaging wave bands of which include VNIR (visible light and near infrared), SWIR (short wave infrared) and TIR (thermal infrared). Firstly, the components of all the pixels in the image were determined through the VNIR images using linear mixing spectral model. Secondly, the emissivity of each component in every pixel in the image was determined according to a prior knowledge base of emissivity of many surface features. Thirdly, the so called average temperature of every pixel was retrieved using the TES (temperature and emissivity separation) algorithm. The retrieved temperature was regarded as initial value. The multi-band equations were established after the linearization of Planck function, and the component temperatures of every pixel in the image were inversed. The results show that the accuracy of the component temperatures inversion in one pixel can be improved obviously, with the combination of the VNIR, SWIR and TIR images.
Sea ice density is one of the significant physical properties of sea ice and the input parameters in the estimation of the engineering mechanical strength and aerodynamic drag coefficients; also it is an important indicator of the ice age. The sea ice in the Bohai Sea is a solid, liquid and gas-phase mixture composed of pure ice, brine pockets and bubbles, the density of which is mainly affected by the amount of brine pockets and bubbles. The more the contained brine pockets, the greater the sea ice density; the more the contained bubbles, the smaller the sea ice density. The reflectance spectrum in 350~2500 nm and density of sea ice of different thickness and ages were measured in the Liaodong Bay of the Bohai Sea during the glacial maximum in the winter of 2012-2013. According to the measured sea ice density and reflectance spectrum, the characteristic bands that can reflect the sea ice density variation were found, and the sea ice density spectrum index (SIDSI) of the sea ice in the Bohai Sea was constructed. The inversion model of sea ice density in the Bohai Sea which refers to the layer from surface to the depth of penetration by the light was proposed at last. The sea ice density in the Bohai Sea was estimated using the proposed model from Hyperion image which is a hyperspectral image. The results show that the error of the sea ice density inversion model is about 0.0004 g•cm-3. The sea ice density can be estimated through hyperspectral remote sensing images, which provide the data support to the related marine science research and application.
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