Affected by the sensor itself, illumination, atmosphere, terrain and other factors, even if imaging the same region at the same time, the spectral characteristics of ground objects in different remote sensing images are also very different, and the surface parameters, ground object classification and target recognition results of the inversion are also different, which brings great uncertainty to quantitative analysis. The relative radiation correction effect of PIF, method is obvious and the operation is simple, and the accuracy of the effect depends greatly on the selection of the PIF point. The general relative radiometric correction methods are linearization correction without considering the nonlinear difference of multi-temporal images. At present, most radiation normalization methods assume that the transformation relation between images is linear, extract PIF points and establish radiation transformation model. In this paper, Kernel Canonical Correlation Analysis (KCCA) is used for the first time to normalize the radiation between multi-temporal hyperspectral images, which can greatly reduce the nonlinear difference in relative radiation correction. Based on the theory of nuclear canonical correlation analysis, the radiation normalization method of multi-temporal aerial hyperspectral images is proposed. The feature points of PIF are extracted in the nuclear projection space, and the nonlinear model is used for the radiation normalization of hyperspectral images, to improve the radiation normalization accuracy of multi-temporal hyperspectral images. Compared with Canonical Correlation Analysis (CCA), the number and precision of PIF point extraction can be significantly improved. This method can satisfy the radiation normalization between aerial hyperspectral multi-temporal images.
The polarization of water-leaving radiance (Lw) is more sensitive to microphysical properties (e.g. particles shapes, size distributions, compositions, and refractive index) of hydrosols than the unpolarised radiance. Hence, the polarized Lw can be used to extract additional information on oceanic constituents, which is complementary to the spectral and angular radiance measurements. In this study, the polarization characteristics of underwater upwelling radiance in turbid waters with respect to suspended particulate matters have been investigated. The full Stokes components of the underwater upwelling radiance in the visible spectrum are calculated using a radiative transfer model. And then, the influences of suspended particulate matter concentrations on the directional variations and the polarization of underwater upwelling radiance are examined. The results reveal that the polarization of underwater upwelling radiance (I, Q, U, DOP) shows significant multidirectional variations with respect to observation geometries, wavelengths, and solar zenith angles. Moreover, the polarization of underwater upwelling radiance is highly related to the suspended particulate matter concentrations. It demonstrates the potential of using the polarized signal to retrieve particle concentrations in coastal waters. Therefore, the development of in-situ instrumentations and next generation of ocean color sensors should able to measure the polarization properties of water-leaving radiance are recommended.
The inversion of total suspended particulate matter (TSM) from ocean color remote sensing data in coastal waters is still highly inaccurate due to contributions of various oceanic constituents and non-linear independently variation of each other. Since the absorption and scattering by molecules, aerosols, and hydrosols and reflection, transmission over the sea surface, the initially completely unpolarized sunlight becomes partially polarized after transmitting in the coupled atmosphere-ocean system (AOS). Hence, the polarization of the sunlight, which contains embedded information on atmospheric and water optical properties, has largely been neglected. In addition, the parallel polarization radiance (PPR) has two significant advantages in effectively diminishing the sun-glint contamination and enhancing the ocean color signal at the top-of-atmosphere (TOA). In this study, the directional variations in parallel polarized water-leaving radiance of suspended particulate matters in coastal waters, based on the vector radiative transfer simulations (RT), were examined. The simulations reveal that the traditional radiation intensity (I) and parallel polarization radiance (PPR) display significant multidirectional and spectral variations with respect to the observation geometries, and TSM concentrations. Moreover, the water-leaving (Lw) radiance for I and PPR have the same angular distribution pattern and magnitude under different bands. In addition, the relative fraction of Lw to Lt for PPR is large than I, indicating that the PPR can improve to retrieve the Lw radiance at the TOA. Furthermore, an exponent relationship between the Rrs_p and the TSM concentration has been established with low corresponding AD (1.258%) and RMSE (0.202). It demonstrates that the polarization of the Lw radiance is closely related to oceanic constituents, and has great potential for the retrieval of TSM concentrations.
It is crucial to enhance the lower contrast Remote sensing images to obtain more details information for further remote sensing image processing and application. In this letter here, a self-adaptive remote sensing image contrast enhancement method has been proposed. The method is an improvement, based on gradient and intensity histogram equalization (GIHE) by using the advantage of histogram compaction transform (HCT). Firstly, we obtained two enhanced images by GIHE and HCT, respectively. Then furthermore, the two enhanced images were normalized with a self-adaptive paremeter, which based on standard deviation and mean of the gradient. Finally and then, we modified the normalized image by dual-gamma function for preserving the local details. It’s evidenced that the proposed method have more richer details and better subjective visual quality, compared with the other methods. The experimental results depicted in terms of PSNR, MAE and Q. Comparing with the other methods, the proposed method had richer details and better subjective visual quality.
Hangzhou Bay waters are often characterized by extremely high total suspended particulate matter (TSM) concentration due to terrestrial inputs, bottom sediment resuspension and human activities. The spatial-temporal variability of TSM directly contributes to the transport of carbon, nutrients, pollutants, and other materials. Therefore, it is essential to maintain and monitor sedimentary environment in coastal waters. Traditional field sampling methods limit observation capability for insufficient spatial-temporal resolution. Thus, it is difficult to synoptically monitor high diurnal dynamics of TSM. However, the in-orbit operation of the world’s first geostationary satellite ocean color sensor, GOCI, thoroughly changes this situation with hourly observations of covered area. Taking advantage of GOCI high spatial-temporal resolution, we generated TSM maps from GOCI Level-1B data after atmospheric correction based on six TSM empirical algorithms. Validation of GOCI-retrieved normalized water-leaving radiances and TSM concentration was presented in comparison with matched-up in-situ measurements. The mean absolute percentage differences of those six TSM regional algorithms were 24.52%, 163.93%, 195.50%, 70.50%, 121.02%, 82.72%, respectively. In addition, the discrepancy reasons were presented, taking more factors such as diversified satellite data, various study area, and different research season into consideration. It is effective and indispensable to monitor and catch the diurnal dynamics of TSM in Hangzhou Bay coastal waters, with hourly GOCI observations data and appropriate inversion algorithm.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.