## 3 Methods
### The HySpex Data Processing  
Since the data were taken from the laboratory, the distance between the camera lens and the target object was very short, so the influence of the atmosphere could be ignored. However, chaotic noise due to environmental interference and equipment defects still exists, resulting in a certain deviation. Therefore, the collected spectral data had to be specially processed (Figure 4).  
(1) Preprocessing: In order to minimize the influence of noise during imaging: Firstly, the hyperspectral images were calibrated to reflectance through the following steps: (a) dark current subtraction, (b) flat field correction, (c) correction for the absolute reflectance properties of Spectralon. Step a removed instrument spatial and spectral non-uniformities, while steps b and c corrected for reflectance data. Then, the Savizky-Golay filtering (SG) method [47; 48] was selected to fit the data to improve the signal-to-noise ratio (S/N) [49].  
(2) Image processing: The SWIR hyperspectral images were processed using ENVI 4.7 processing software. The mineral represented by each reference spectrum was determined by comparing the spectrum with the SWIR spectral United States Geological Survey (USGS) spectral library [50]. A customer library was established for mineral identification.  
(3) Mineral recognition: To address the dimensionality and separate the noise from the data problem of hyperspectral data, a forward maximum noise fraction (MNF) transform was performed on the image. The pixel purity index (PPI) of HySpex data was calculated, based on the data transformed  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Technical Specification** & **SWIR-320m-e** \\ \hline spectral region & 1000–2500 nm \\ Pixel space & 620 \\ Spectral channels & 300 \\ spectral resolution & 6.5 nm \\ Spectral sampling bandwidth & 6.25 nm \\ Number of wavelengths & 256 \\ Instantaneous field of view & 20\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main technical parameters of HySpex imaging spectrometer.  
by MNF, using PPI tools, to obtain the purest pixel of spectrum. We combined the n-D Visualizer tool with results after MNF and PPI, used to localize, recognize and collect the purest pixel of data, to obtain pure end member spectrum. Next came matching pixel and reference spectra in n-D angle, using spectrum angle mapping (SAM), and judging the similarity of end member and reference spectra by calculating the angle between them--the smaller the angle is, the higher the degree of matching and similarity (Figure 4).  
(4) Spectral parameter extraction: The feature minerals of chlorite and garnet in the alteration map were masked to obtain the single thematic layer. The mineral map of hySpex data was extracted from mineral spectral parameters (the absorption wavelength position and depth of samples) by continuum-removal.  
An initial set of reference spectra by manually selecting some to represent the observed variability in the measured SWIR hyperspectral images. As shown in Figure 5b, chlorite SMR-13 standard spectrum in USGS spectrum library refers to when G 05 is a filter and spectral angle matching is carried out. Among them, the most sensitive wavelength is located near 2250 nm. Figure 5a shows the quartz spectral characteristics in the high-grade iron ore, which is slightly more weakly absorbing than quartz HS32 in the standard spectrum, and the overall waveform is consistent. Namely, it is mainly composed of water absorption characteristics of 1400 and 1900, and absorption characteristics of Fe and Mg around 2250-2350 nm. Figure 5c refers to an arbitrary garnet short-wave infrared spectrum. This mineral is different from chlorite and quartz, and the overall spectrum is relatively gentle. Note the obvious absorption feature near 1200 nm, which probably resulted from the Fe\({}^{2+}\) crystal field effect of garnet.  
Figure 4: Spectral analysis and identification process.