AERSCIEA: An Efficient and Robust Satellite Color Image Enhancement Approach

— Image enhancement is an important preprocessing step in any image analysis process. It helps to catalyze the further image analysis process like Image segmentation. In this paper, an approach for satellite color image enhancement on HSV color space is introduced. Here, local contrast management is given main focus because noises exist on local regions are found over amplified when enhancement is done through global enhancement technique like histogram equalization. The color arrangement and computations are done in HSV color space. The V-channel has been extracted for the enhancement process as this is the channel which represents the intensity and thereby represents the luminance of an image. At first, the image is normalized to stabilize the pixel distribution. The normalized image channel is analyzed with Binary Search Based CLAHE (BSB-CLAHE) for local contrast enhancement. The results obtained from the experiments prove the superiority of the proposed approach.


I. INTRODUCTION
Image enhancement is a major step for any image analysis process.This has to be conducted if the results of the further analysis process are needed to be improved.Most importantly, if it is the case of satellite images, then this becomes a mandatory condition as because most of the satellite images consist of noises.Satellite images use to carry a large amount of information with them, so the presence of noises may hamper the extraction of such huge amount of useful information [1][2] [3].In the case of satellite images, a small block of the whole image concerns a lot, so, local contrast management should be given major priority here.Also, RGB color space is not able to deal with the satellite color image analysis properly [4] [5], hence we need a color space which has the ability to arrange the colors in a way so that any change of intensity will not distort the color information of the image.HSV color space has the ability to deal with the color in the same way that human eyes can perceive [6].It has a separate channel for intensity information (V-channel).
These are the reasons why we have selected HSV color space for the proposed color enhancement approach.
The outline of the paper is as follows: Section II presents a review of the previous work done in the field; Section III discusses the different steps involved in the proposed approach.From section IV to section VI, the topics involved in the proposed approach are explained with proper examples.Section VII is the experiment and results discussion section.The conclusion is given in the section VIII and finally, we have the reference section II.LITERATURE REVIEW In [7], an underwater image enhancement technique has been proposed which is a novel extension of histogram equalization (histogram specification) to overcome BPHEME (Brightness Preserving Histogram Equalization with Maximum Entropy), a drawback of Histogram Equalization.The experimental results claim that BPHEME enhances the image effectively and also preserve the original brightness quite well.
In [8], a new contrast enhancement technique for satellite images based on clipping or plateau histogram equalization is proposed.Here, a Bi-Histogram Equalization with Plateau Limit (BHEPL) is adopted for image decomposition and Self-Adaptive Plateau Histogram Equalization (SAPHE) for threshold calculation.The proposed enhancement technique performs comparatively better than the other available histogram equalization technique.
In [9], a genetic algorithm (GA) based histogram equalization technique is proposed.Here, images are enhanced with histogram equalization by reconfiguring their pixel spacing using optimization through GA.The results obtained are more optimized than the same done by other available techniques.
In [10], here the value channel of the HSV converted the image of the input RGB color image is enhanced in two steps: (1) It is divided into smaller blocks and in each block dynamic range compression is carried out using nonlinear transfer function, and (2)In each block, every pixel is further enhanced depending upon the centre neighborhood so that the contrast of the adjusted.Then it is converted back to RGB. results show the better performance o approach.
In [11], the authors propose a new un enhancement technique known as mixture models.The proposed method operates C and HSV color models.Results obtained combined together using Euclidean norm significantly improves the visual quality images by enhancing contrast, as well as re artifacts.

III. THE STEPS INVOLVED IN THE PROPOS
Step1.The input RGB color image conversion to HSV color space.
Step2.The Value channel (V Channel) ha from the HSV converted the image.The V chosen as this represents the luminance Another important fact is that H (Hue) chan affected during the enhancement process as information may be misjudged.
Step3.The extracted V channel has b between o and 1 to bring stability to the pixe Step4.The normalized V Channel has local contrast management by BSB-CLAHE Based-CLAHE).
Step5.The old V-channel has been enhanced V channel to obtain the enhanced Step6.The Enhanced HSV image is co RGB color space to obtain the final enhanc original input color image.

IV. HSV COLOR SPACE:
Color space can be defined as a mathematic describe the organization of different colors represents different color attributes with re more components that help to learn accu color spectrum looks like [4] [12].HSV frequently adopted color space because of organizing any color image in the same way eyes can perceive.In this color space, channels: Hue (H), Saturation(S) and Valu one represents an angle in the range [0,2π] related to color.Different hue angles re colors.The second one, i.e., S channel sho hue is with respect to a white reference.This of as a radial distance from the central axis at the center to 1 at the outer surface.F represents a percentage value goes from percentage value exactly shows the a illuminated with respect a particular color.channel is the luminance channel in HSV with the 'V' channel of HSV color space, i measure and extract the luminance level o which makes it perfect color space f e pixel and its e image has been B. The  Finally, the value 0 to 100.This amount of light lor.This means V-SV color space.So, pace, it is possible to l of a color image, for color image enhancement and also for color ima pseudo code to convert an RGB ima can be found in [13].
The coordinate system showing the three channels of HSV colo following figure: V. IMAGE NORMALIZATION In general 'normalization' can be considered as a preprocessing stage to find out a new stable range from an existing one [14].While when the concerned area is image processing, then 'image normalization' can be defined as a technique for changing the ranges of pixel intensity values and thereby bringing stability to it [15].Normalization of the image is required in order to shape the pixels of the image into a range that is more proverbial to the dealt application.Image variations may occur during image acquisition process.These variations may take different forms like illumination, noise or occlusion which are no way relevant to object identity.So, we need a process to eliminate such unwanted variations to retain a standard image containing no artifacts.Image normalization is such a process to make the image artifact free and thereby get it ready for the further image analysis process [15][16].This way the accuracy of the image enhancement can be increased to a satisfiable extent.In our proposed approach, the V channel of the HSV converted image has been normalized and bring its range to fall within 0 and 1.Following equation performs this task: Say, V1 is the current V-value and V2 is the normalized one.V2={V1-minimum (V1)}/{maximum (V1)-minimum (V1)}; In figure 5,( b) is the normalized version of (a) obtained through the application of the above equation.

VI. BINARY SEARCH BASED CLAHE (BSB-CLAHE)
Contrast management is one of the most needed steps involved in an image enhancement process.With this, the contrast of an image can be modified to a satisfactory level such that the result of the whole image analysis process becomes more efficient.Contrast management is of two types: (1) Global contrast management and, (2) local contrast management.Histogram Equalization (HE) is an example of global contrast management and Adaptive Histogram Equalization (AHE) [17] is an example of local contrast management.Histogram equalization is a frequently adopted image enhancement technique which enhances the contrast of an image by spreading the intensities values over the entire available dynamic range [1][4].But, it has a drawback that it tends to over enhance the image contrast if there are high peaks in the histogram [18].AHE works on the basis of computing several histograms and using them to redistribute the lightness values of the image [19] [20].But, its main drawback is that it over amplify the noises in homogenous DIBYA JYOTI BORA: AN EFFICIENT AND ROBUST SATELLITE COLOR IMAGE ENHANCEMENT APPROACH regions.For this reason, we have gone for the other option that is CLAHE.CLAHE means we need to put a limit on the contrast enhancement.For some intensity levels, the slope of the CDF (Cumulative Distributive Function) at the intensity level is directly proportional to the contrast enhancement of those intensity levels [4] [20].Again, at a bin location, the slope of the CDF can be determined by the height of the histogram for that concerned bin.This implies that by limiting or clipping the height of the histogram to a certain level, we can limit the slope of the CDF function and thereby the contrast enhancement can be limited [4].So, the only thing we need a mechanism for clipping the histogram before evaluating its CDF for putting a limit on the contrast enhancement done by AHE.For this, we have introduced Binary Search Based CLAHE.Here, we have adopted a Binary Search Method [4]

VII. EXPERIMENTS AND RESULTS
Matlab is chosen for implementation of our proposed approach.The system used for conducting the experiments has an i5 processor with 64 bit Windows 10 operating system.The satellite image data are collected from Earth Science World Image Bank [22] and The USC-SIPI Image Database [23].
To measure the quality of the enhancement results, we have selected the MSE (Mean Squared Error) and PSNR (Peak Signal to Noise Ratio) metric.The cumulative squared error between the compressed and the original image will be given by the first one, whereas the second one will give the peak error [24][25].Following equation [2] is used calculate MSE : where, ( , ) I x y is the original input color image, ( , ) is the output enhanced image.M, N stands for the dimensions of the images.
The formula for PSNR [25] is: Where i MAX contains the maximum possible pixel value of the image.For a better enhancement of the image, the MSE value should be as low as possible and PSNR values should be as higher as possible.
The following is the series of images illustrating the results obtained at every stage of the proposed approach.The image is the aerial image of San Francisco (Golden Gate) consisting of 512x512 pixels with size 768kb.The proposed approach has been applied to around 30 different satellite images.To compare the results with existent state of the art algorithms, we have selected D.Ghimire'sMethod [10] as the recent one in the area.Also, as Histogram Equalization is the most popular and frequently adopted method for image enhancement, so this is selected for comparing the same.The results for 4 images are shown below: Image 1: This is the image of Woodland Hills, Ca. [23] consisting of 512x512 pixels with size 768kb.The MSE values for all the images are found low for the proposed approach in comparison to histogram equalization and D.Ghimire's Method, while the respective PSNR values for the proposed approach are higher than the same of histogram equalization and.Also, in the case of image 4, we have seen the proposed approach is producing a better result even in the situation when salt and pepper noises are added to the input color image.This implies that the proposed approach succeeds to produce better enhancement of satellite color image than histogram equalization and D. Ghimire's Method.

Multispectral Image:
A multispectral image is an image that captures image data at specific frequencies across the electromagnetic spectrum [26].
The spectrum is generally divided into many bands.Each band is a monochrome image of the scene taken with a different sensor.As for example, Landsat 5 consists of 7 band images with the wavelength of the bands being between 450 and 1250 nm.Multispectral images mostly used in remote sensing applications [27].It is found that there has always been a need of enhancing multispectral radiance data to produce an image that will be more suitable for visual interpretation.To analyze the efficiency of our proposed approach in enhancing the multispectral image, we have collected a LAN file, paris.lan,containing a 7-band 512-by-512 Landsat image which covers a part of Paris, France.From the results, it is clearly visible that histogram equalization is producing over enhancement of the contrast of the original multispectral image.In this case, both the techniques: D. Ghimire's Method and our proposed method succeeds to deal with the over enhancement problem.But, the proposed approach is producing better enhancement of the image because of the fact that the normalized distribution of the concerned V-channel makes it easier to enhance the local contrast more efficiently and thereby resulting in a smooth global contrast enhancement of the image.

VIII. Conclusion:
In any image analysis process, image enhancement is always considered as an important preprocessing step.Through image enhancement, the noises or irrelevant information is tried to remove to the maximum possible extent.Contrast management is one of the important tasks during enhancement process.It is of two types: global and local.Global contrast management although possessing low 12 PROCEEDINGS OF RICE.GOPESHWAR, 2017 computational complexity, but fails to produce better enhancement results and maximum times enhanced images are found suffering from noises.So, local contrast management is needed for better enhancement.In this paper, a BSB-CLAHE based enhancement technique for satellite color images is introduced where local contrast management is given main focus.The enhancement process is done on HSV color space because of its capability to deal with satellite image in a far better way than RGB color space does.Normalization procedure is adopted here to bring stability in the range of pixels distribution of the input color image.The results of the proposed approach are found quite satisfactory and hence establish a good framework for satellite color image enhancement.In future, the application of the proposed approach will be extended to different emerging areas like medical color image enhancement, underwater color image enhancement etc.
[17][21] for determining the points at which the clipping should be done.The algorithm is discussed below:Step1.Say T is the top and B is the bottom of the concerned ClipLevel.Step2.Repeat until T-B< , 0 [ ] [ ]where is negligibly small a Find Mwhere MisthemiddlebetweenTand B b Find S where S isthe sumof excess above Mineac redistribute the excess into each bin equally.

Fig. 5 :
Fig. 5: (a) Original Image; (b) HSV Converted Image; (c) V-Channel; (d)Normalized V-Channel; (e) BSB-CLAHE Enhanced V-Channel; (f)Enhanced Image in HSV Color space; (g) Final Enhanced Color Image; (h) Histogram of the Original V-Channel; and (i) Histogram of the enhanced V-Channel by Our Proposed Approach.

Chart 1 :Image 2 :Fig. 7 :
(a) MSE Values Comparison; and (b) PSNR Values Comparison.This is the image of Stockton [23] consisting of 1024x1024 pixels and of size 3072kb.(a) Original Image; (b) Enhanced Image by Histogram Equalization; (c) Enhanced Image By D. Ghimire's Method; (d) Enhanced Image by the Proposed Approach.

Chart 3 :Image 4 :Fig. 9 :
Fig. 9: (a) Original Image; (b) Noisy Image; (c) Enhanced Image By Histogram Equalization, (d) Enhanced Image By D. Ghimire's Method; and (e) Enhanced Image By the Proposed Approach a) MSE Values Comparison; and (b) PSNR Values Comparison.So, from the experimental results, it is observed that the enhancement done by our proposed approach is noise free, and every little region is enhanced equally with clear identification of the present objects.While the resultsDIBYA JYOTI BORA: AN EFFICIENT AND ROBUST SATELLITE COLOR IMAGE ENHANCEMENT APPROACHobtained by the histogram equalization are somewhat noise over amplified and thereby appearance of high darkness shades are found in the resultant image.D.Ghimire's Method succeeds to produce good enhancement.But, visually the enhancement results of our proposed approach are better than both the Histogram Equalization and D.Ghimire's Method.