# Performance Comparison of Selected Filters in Fast Denoising of Oil Palm Hyperspectral Data
## 1 Introduction  
Hyperspectral imaging has been used in many applications, including oil, gas, and mineral exploration, agriculture, remote sensing, quality control, forensic science, ecology, medicine, and others [1, 2]. A hyperspectral camera captures the reflections of many bands of wavelengths called channels and stores the data in the form of stacked two-dimensional (2D) images. Since the images are captured together at the same location and time using different bands of wavelength, it is expected that overlaps of information can occur among images of adjacent bands. However, it is also known that some images contain exclusive information not available in others. When processed together, the images provide various complementing information useful for data analysis. For example, in hyperspectral data of oil palms, images of different bands are successfully used to detect early infections of Ganoderma, blast, freckle, and bagworms [3, 4, 5, 6]. Oil palm hyperspectral images are usually acquired from satellites or drones. Often, channels of long and short wavelengths contain more noise than those of the medium wavelengths. Noise in the form of black and white speckles reduces the quality of the images and interferes with the extraction and analysis of features or information in them [7, 8]. For example, noise in airborne hyperspectral data may interfere with the process of establishing a good correlation with the field spectroscopy data taken on theground. Consequently, if the noisy channels cannot be cleansed successfully, they are discarded because their values are too volatile to be useful. It is observed that only short- and long-wavelength channels are severely affected by speckle noise, and the other channels that make up most of the data are unaffected. In many reported studies, it is common to contaminate clean images with synthetic noise before they are denoised with the proposed methods. In this way, the original signal strength is known and therefore the SNRs can be calculated. However, it is more important to test the method on real data with severe noise to show the effectiveness of a denoising method. In this study, a framework to filter noise in oil palm hyperspectral data is introduced. To the best of our knowledge, this is the first attempt to denoise oil palm hyperspectral data using such an approach. There are two main strategies of denoising hyperspectral data, and they are the spatial-spectral and the deep learning neural network approaches [9; 10]. In essence, both methods seek to reduce variations or increase similarity in homogeneous areas by adjustment or replacement of pixels with values deemed correct [11; 12]. The effect is akin to lowpass filtering in the target areas [13]. Spatial-spectral approaches make use of spectral correlation, sparsity promotion, variation minimization, selective data content reduction, low-rank matrix or tensors, and others with the aim of reducing the volatility that contributes to the noisy variations in the channels [14; 15; 16; 17]. Normally, deep learning methods require a lot of data and substantial training to be effective. [PERSON] and [PERSON] (1999) used a combination of a linear unmixing model and Kalman filter for subpixel detection and classification of remotely sensed images [18]. [PERSON] et al. (2003) introduced a wavelet-based estimation scheme of noisy hyperspectral signals that filter out the noise content of the data in a similar fashion to that of the Wiener filter [2]. Since the clean data were available, the correlation matrix of the channels was able to be established in a straightforward fashion. The method was successfully tested on lunar lake hyperspectral data with added noise, and the results were good. [PERSON] and [PERSON] (2006) exploited a strong correlation between bands of hyperspectral data by enforcing sparsity of their wavelet representations to remove noise [19]. Their method was tested on AVIRIS data that had been injected with synthetic noise, and it outperformed the wavelet-based global soft-thresholding method.