This paper attempts to undertake the study of three types of noise such as Salt and Pepper (SPN), Random variation Impulse Noise (RVIN), Speckle (SPKN). Different noise densities have been removed between 10% to 60% by using five types of filters as Mean Filter (MF), Adaptive Wiener Filter (AWF), Gaussian Filter (GF), Standard Median Filter (SMF) and Adaptive Median Filter (AMF). The same is applied to the Saturn remote sensing image and they are compared with one another. The comparative study is conducted with the help of Mean Square Errors (MSE) and Peak-Signal to Noise Ratio (PSNR). So as to choose the base method for removal of noise from remote sensing image.
Digital image processing is the most important technique used in remote sensing. It has helped in the access to technical data in digital and multi-wavelength, services of computers in terms of speed of processing the data and the possibilities of big storage. Several studies can also take the benefit of it such as technical diversity of the digital image processing, replication sites and maintaining the accuracy of the original data. Noise is removable using iterative median filtering in spatial domain which requires much less processing time than removal by frequency domain Fourier transforms [1]. Weight Median Filter (WMF) based on threshold decomposition removes impulsive noise with an excellent image detail processing capability compared to nonlinear filter and linear filter [2]. Standard Median Filtering (SMF) is a non-linear, low-pass filtering method which can be used to remove 'speckle' noise from an image. A median filter can out perform linear, low pass filters, on this type of noisy image became it can potentially remove all the noise without affecting the 'clean' pixels. Median filters remove isolated pixels, whether they are bright or dark. Adaptive Median Filter (AMF) is designed to eliminate the problems faced by the Standard Median Filter [3]. Adaptive Filter (AF) changes its behavior based on the statistical characteristics of the image inside the filter window. Adaptive filter performance is usually superior to non-adaptive counterparts. The improved performance is at the cost of added filter complexity. Mean and variance are two important statistical measures using which adaptive filters can be designed [4].There are many methods for reducing noise. Traditional median filter and mean filter are used to reduce salt-pepper noise and Gaussian noise respectively. When these two noises exist in the image at the same time, use of only one filter method can not achieve the designed result [5].
Remote sensing is used to obtain information about a target or an area or a phenomenon through the analysis of certain information which is obtained by the remote sensor. It does not touch these objects to verify. Images obtained by satellites are useful in many environmental applications such as tracking of earth resources, geographical mapping, prediction of agricultural crops, urban growth, weather, flood and fire control etc. Space image application includes recognition and analysis of objects in the images, obtained from deep space-probe missions.
Noise is any undesired information that contaminates an image. Noise appears in image from various sources. The digital image acquisition process, which converts an optical image into a continuous electrical signal that is then sampled, is primary process by which noise appears in digital image. There are several ways through which noise can be introduced into an image, depending on how the image is created. Satellite image, containing the noise signals and lead to a distorted image and not being able to understand and study it properly, requires the use of appropriate filters to limit or reduce much of the noise. It helps the possibility of better interpretation of the content of the image.
There are three common types of image nose: ISSN (Online): 1694-0784 ISSN (Print): 1694-0814
This type of noise is also called the Gaussian noise or normal noise is randomly occurs as white intensity values. Gaussian distribution noise can be expressed by:
Where: P(x) is the Gaussian distribution noise in image; µ and σ is the mean and standard deviation respectively.
This type contains random occurrences of both black and white intensity values, and often caused by threshold of noise image.
Salt & pepper distribution noise can be expressed by:
Where: p1, p2 are the Probabilities Density Function (PDF), p(x) is distribution salt and pepper noise in image and A, B are the arrays size image. Gaussian and salt & Pepper are called impulsive noise.
If the multiplicative noise is added in the image, speckle noise is a ubiquitous artifact that limits the interpretation of optical coherence of remote sensing image. The distribution noise can be expressed by: J = I + n*I (3) Where, J is the distribution speckle noise image, I is the input image and n is the uniform noise image by mean o and variance v.
Mean Filter (MF) is a simple linear filter, intuitive and easy to implement method of smoothing images, i.e. reducing the amount of intensity variation between one pixel and the next. It is often used to reduce noise in images. The idea of mean filtering is simply to replace each pixel value in an image with the mean (average) value of its neighbors, including itself. This has the effect of eliminating pixel values which are unrepresentative of their surroundings. Mean filtering is usually thought of as a convolution filter. Like other convolutions it is based around a kernel, which represents the shape and size of the neighborhood to be sampled when calculating the mean
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