A New Method for Estimating Signal-to-Noise Ratio in UAV Hyperspectral Images Based on Pure Pixel Extraction

Abstract

Signal-to-noise ratio (SNR) is an important radiation characteristic parameter for remote sensing image quality assessment as well as a key performance indicator for remote sensing sensors. At present, the SNR estimation methods based on regular segmentation or continuous segmentation are generally used to obtain image SNR. However, the land cover type has a great influence on the results of the SNR estimation method using regular segmentation, especially the high spectral resolution and high spatial resolution remote sensing images obtained by the low-altitude UAV hyperspectral sensor. In addition, some land cover types are difficult to achieve continuous segmentation. In view of this limitation of the existing SNR estimation methods, a new unsupervised method for estimating SNR in UAV hyperspectral images has been developed in this paper, called pure pixel extraction and spectral decorrelation (PPESDC). By directly extracting pure pixels in the spatial dimension and combining the correlation of the spectral dimension to obtain the SNR of the hyperspectral image, this new method replaces the conventional method of improving the segmentation algorithm to improve the accuracy of SNR estimation. Additionally, the box counting method is introduced to determine the image SNR aggregation interval. The results showed that the proposed method had higher accuracy and smaller errors than the other SNR estimation methods. Besides, this method had stronger robustness, it can be used for both radiance and reflectance (atmospherically corrected) UAV hyperspectral images.

Atmospheric correction of vegetation reflectance with simulation-trained deep learning for ground-based hyperspectral remote sensing

“…In this study we chose the Specim ImSpector V10E efficiency curve, which rises from slightly above 30% at 0.4 m to approximately 60% at 0.6 m, returning to slightly above 30% at 0.8 m, and falling to less than 5% at 1.0 m. This produces the photon flux curve (units of photons m sr ), which can be converted into spectral radiance (units of W m sr nm ). Due to the presence of sensor noise in the recorded spectral radiance, a peak signal-to-noise ratio (SNR) of 20:1 is commonly assumed for HSI sensors [ 75 – 77 ]. Provided that the instrument in the case study utilizes a charge-coupled device (CCD) sensor to measure the incident photon flux and convert the signal from analog to digital output, various sources of noise, including photon noise, dark current, photo response nonuniformity, and read-out noise, contribute to the SNR.…”