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

Abstract

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.

Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops

“…The image condition module describes the radiation transfer in the atmosphere as this accounts for the absorption, reflection and scattering that will affect the HSI performance. As a result of these influences, the radiance at the sensor, , is composed of three main elements: Direct reflection; path radiation, ; and radiation reflected outside of the target, as shown in Figure 7 [ 70 ]. where is the radiation at the surface, X is the vector of the surface reflectance, is the vector from the background and the final term, , eliminates the direct reflection.…”

“…The spectral model contains the centre wavelength as well as the adjacent wavelengths radiation [ 70 ]. The effect of these adjacent wavelengths needs to be accounted for in the mean, , and the covariance, , where B is a linear transformation matrix of the spectral response functions.…”

“…The second compares the probabilities that the sensor makes a detection opposed to a false detection. The third calculates the total error based on the total probabilities of a false alarm or missed detection [ 70 ].…”

Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers

“…As a result, ground coverage comprised by an individual pixel in the image is practically large. Thus, most of the pixels are less likely to represent a pure spectrum and the pixel spectra are more likely to be combinations of the spectral responses from two or more types of materials (known as spectral mixing) [5,[14][15][16]. Thus, identification and extraction of pure pixel spectra for training samples in the supervised classifier become a tedious task and critically affects the performance of the classifier.…”

A Novel Hyperspectral Image Simulation Method Based on Nonnegative Matrix Factorization

“…Hyperspectral (HS) remote sensing technology can acquire a wide-wavelength-range and fine spectral information of an observation area through numerous spectral channels. The abundant spectral information enables widespread applications, such as soil contamination [1], geological mineral mapping [2], fire detection [3], and vegetation monitoring [4]. However, the coverage area and observation time restrict wider applications of HS images [5].…”