Effectiveness of Contrast Limited Adaptive Histogram Equalization on Multispectral Satellite Imagery

Research Paper (undergraduate) 2016 29 Pages

Geography / Earth Science - Miscellaneous



(i) Acknowledgement

(ii) Abstract

(iii) Contents

(iv) List of Figures

(v) List of Tables

1 Introduction
1.1 Global Histogram Equalization
1.2 Adaptive Histogram Equalization
1.3 Contrast Limited Adaptive Histogram Equalization

2 Data Acquisition

3 Methodology
3.1 Mean Square Error (MSE)
3.2 Peak Signal-to-Noise Ratio (PSNR)
3.3 Degree of Contrast Enhancement (DCE)

4 Comparative Analysis

5 Results and Inferences

6 Conclusions

7 References


I express my deep gratitude to Dr. H. Ramesh, Assistant Professor, Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal for his valuable guidance, sportive encouragement, and valuable suggestions throughout my internship. I wish to put on record my deep reverence and esteemed thanks to him.

I express my sincere thanks to Dr. G. S. Dwarakish, Professor and Head, Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal for extending the facilities in the department throughout my dissertation work. I am also thankful to Dr. A. P. Krishna, Professor and Head, Department of Remote Sensing, Birla Institute of Technology, Mesra for giving me the permission to pursue my summer internship from National Institute of Technology Karnataka, Surathkal.

My special thanks to all my friends in NITK for their everlasting support and encouragement throughout my internship. I express earnest thanks to all the teaching and non-teaching staff, Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka for their direct or indirect help during the course of my internship.

I also express my deep gratitude to my beloved parents and sister for their moral support and constant encouragement throughout my internship.



Contrast Limited Adaptive Histogram Equalization technique (CLAHE) is a widely used form of contrast enhancement, used predominantly in enhancing medical imagery like X-rays and to enhance features in ordinary photographs.

This work is aimed to understand the effectiveness of using this technique in multispectral satellite imagery and to study its effectiveness in different regions of the electromagnetic spectrum. This work also aimed in analysing variations of spatial and spectral resolutions of a sensor affect the performance of the CLAHE technique by means of comparing quantitative parameters of the enhanced images between the sensors.

A new performance parameter called Degree of Contrast Enhancement (DCE) has also been formulated so as to quantify the amount of increase/decrease in contrast between the enhanced and original images on application of the CLAHE algorithm on it.

A general idea of the feature that can be enhanced in each spectral region was also studied. The results showed that the technique was most effective for shorter wavelengths when compared to longer wavelength regions.

A comparative study between the CLAHE technique and the conventional global histogram equalization technique resulted in the former technique emerging superior of the two and thereby reconstructed images of better quality.


1.1 Concept of Adaptive Histogram Equalization

1.2 Redistribution of pixels above clip limit in Contrast Limited Adaptive Histogram Equalization

3.1 Flowchart of the methodology adopted

4.1 Original and enhanced images of the study area in LISS III

4.2 Original and enhanced images of the study area in AWiFS

4.3 Original and enhanced images of the study area in Landsat 8 OLI

4.4 Original and enhanced images of the study area in Sentinel 2A MSI

5.1 Variation of Mean Square Error with respect to wavelength

5.2 Variation of Peak Signal-to-Noise Ratio with respect to wavelength

5.3 Variation of Degree of Contrast Enhancement with respect to wavelength


2.1 Details of the datasets used for the study

5.1 Observations for the Green band in the four datasets

5.2 Observations for the Red band in the four datasets

5.3 Observations for the NIR band in the four datasets

5.4 Observations for the SWIR band in the four datasets

5.5 Comparison of the quality parameters obtained by employing GHE and CLAHE techniques on AWiFS image


Image enhancement is among the simplest and most appealing areas of digital image processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or simply to highlight certain features of interest in an image. It is important to keep in mind that enhancement is a very subjective area of image processing. Improvement in quality of degraded images can be achieved by using application of certain enhancement techniques7.

Image enhancement for satellite imagery can be classified into three types10 11

a) Spatial Enhancement (Filtering),
b) Spectral Enhancement (Band ratio-ing) and
c) Radiometric Enhancement (Contrast Enhancement)

The present study is dealing with the radiometric enhancement of the satellite imagery by applying Contrast Limited Adaptive Histogram Equalization technique. This technique is extensively used in enhancement of medical images like X-rays of various body organs and is found to give satisfactory results in that domain6.

Adaptive histogram equalization (AHE) is a computer image processing technique used to improve contrast in images. It differs from ordinary histogram equalization in the respect that the adaptive method computes several histograms, each corresponding to a distinct section of the image, and uses them to redistribute the lightness values of the image. It is therefore suitable for improving the local contrast and enhancing the definitions of edges in each region of an image.

However, AHE has a tendency to over amplify noise in relatively homogeneous regions of an image. A variant of adaptive histogram equalization called contrast limited adaptive histogram equalization (CLAHE) prevents this by limiting the amplification 7. Ordinary histogram equalization uses the same transformation derived from the image histogram to transform all pixels. This works well when the distribution of pixel values is similar throughout the image. However, when the image contains regions that

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are significantly lighter or darker than most of the image, the contrast in those regions will not be sufficiently enhanced.

1.1 Global Histogram Equalization

An alternative technique for contrast enhancement which has been widely used is global histogram equalization13. In this method, the intensity values in the image are altered such that the resulting image has a constant intensity histogram. This transformation may be accomplished by the use of the cumulative distribution function of the pixel intensities as the intensity remapping function. Such images utilize the available display levels well, but because the contrast enhancement is based on the statistics of the entire image, some levels will be used for the depiction of parts of the image which are diagnostically unimportant, such as the background of the image.

1.2 Adaptive Histogram Equalization

Adaptive histogram equalization (AHE) developed by Ketcham et al, Hummel, Pizer et al14 15 16 improves on this by transforming each pixel with a transformation function derived from a neighbourhood region. It was first developed for use in aircraft cockpit displays15 17. In its simplest form, each pixel is transformed based on the histogram of a square surrounding the pixel, as in shown Fig 1. The derivation of the transformation functions from the histograms is exactly the same as for ordinary histogram equalization. The transformation function is proportional to the cumulative distribution function (CDF) of pixel values in the neighbourhood18.

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Fig 1.1 Concept of Adaptive Histogram Equalization

Pixels near the image boundary have to be treated specially, because their neighbourhood would not lie completely within the image. This applies for example to the pixels to the left or above the blue pixel in Fig 1.1. This can be solved by extending the image by mirroring pixel lines and columns with respect to the image boundary. Simply copying the pixel lines on the border is not appropriate, as it would lead to a highly peaked neighbourhood histogram.

1.3 Contrast Limited Adaptive Histogram Equalization

Contrast Limited AHE (CLAHE) developed by Pizer et al19 20, differs from ordinary adaptive histogram equalization in its contrast limiting6. This feature can also be applied to global histogram equalization, giving rise to contrast limited histogram equalization (CLHE), which is rarely used in practice.

In the case of CLAHE, the contrast limiting procedure has to be applied for each neighbourhood from which a transformation function is derived. CLAHE was developed to prevent the over-amplification of noise that adaptive histogram equalization can give rise to. This is achieved by limiting the contrast enhancement of AHE. The contrast amplification in the vicinity of a given pixel value is given by the slope of the transformation function. This is proportional to the slope of the neighbourhood cumulative distribution function (CDF) and therefore to the value of the histogram at that pixel value. CLAHE limits the amplification by clipping the histogram at a predefined value before computing the CDF. This limits the slope of the CDF and therefore of the transformation function. The value at which the histogram is clipped, the so-called clip limit, depends on the normalization of the histogram and thereby on the size of the neighbourhood region. Common values limit the resulting amplification is between 3 and 4. It is advantageous not to discard the part of the histogram that exceeds the clip limit but to redistribute it equally among all histogram bins (Fig 1.2).

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Fig 1.2 Redistribution of pixels above clip limit in Contrast Limited Adaptive Histogram Equalization

The redistribution will push some bins over the clip limit again (region shaded green in the Fig 1.2), resulting in an effective clip limit that is larger than the prescribed limit and the exact value of which depends on the image. If this is undesirable, the redistribution procedure can be repeated recursively until the excess is negligible. This technique has not yet been applied to satellite imagery which are large in size and contain various features that need to be distinguished from each other accurately. Hence the use of this technique in the geospatial domain is studied.

Algorithm Steps7:

1. Obtain all the inputs: Image, Number of regions in row and column directions, Number of bins for the histograms used in building image transform function (dynamic range), Clip limit for contrast limiting (normalized from 0 to 1)
2. Pre-process the inputs: Determine real clip limit from the normalized value if necessary, pad the image before splitting it into regions
3. Process each contextual region (tile) thus producing gray level mappings: Extract a single image region, make a histogram for this region using the specified number of bins, clip the histogram using clip limit, create a mapping (transformation function) for this region
4. Interpolate gray level mappings in order to assemble final CLAHE image: Extract cluster of four neighbouring mapping functions, process image region partly overlapping each of the mapping tiles, extract a single pixel, apply four mappings to that pixel, and interpolate between the results to obtain the output pixel; repeat over the entire image

Syntax: J = adapthisteq(I,param1,val1,param2,val2...) adapthisteq(I) enhances the contrast of the grayscale image I by transforming the values using contrast-limited adaptive histogram equalization (CLAHE) in MATLAB R2013a®21 22. CLAHE operates on small regions in the image, called tiles , rather than the entire image. Each tile's contrast is enhanced, so that the histogram of the output region approximately matches the histogram specified by the 'Distribution' parameter. The neighbouring tiles are then combined using bilinear interpolation to eliminate artificially induced boundaries. The contrast, especially in homogeneous areas, can be limited to avoid amplifying any noise that might be present in the image.

Parameter names can be abbreviated, and case does not matter. The additional parameters include ‘NumTiles’ for specifying the number of tiles by row and column with the default set at [8 8], ‘ClipLimit’ to define the limit of clipping the histogram, ‘NBins’ to define the number of bins in the histogram, ‘Distribution’ to define the distribution of the histogram - either ‘uniform’ for a flat histogram, ‘rayleigh’ for a bell shaped histogram or ‘exponential’22.


The area under study covers the towns of Hejamadi, Karnad and Nadsal, of Mangalore district which lie along the Malabar coast, enroute Mangalore to Udupi. This area contains a variety of land cover and land use features. The Arabian Sea lies towards the west of the mainland and the Shambhavi river flows through the town Hejamadi.

Datasets from four sensors of different make and spatial resolutions have been obtained, namely the Advanced Wide-Field Sensor (AWiFS) (56m) and Linear Imaging Self-Scanning Camera (LISS III) (23.5m) from the IRS Resourcesat-I mission, Operational Land Imager (OLI) (30m) from the Landsat 8 mission and Multi-Spectral Instrument (MSI) (10m in visible and 20m in SWIR region) from the Sentinel 2A mission.

The details of the datasets collected and used for the present study are given in Table 2.1

Table 2.1 Details of the datasets used for the study

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The visible and IR bands of the satellite imagery were used for the study and methodology shown in Fig.3.1 is adopted. The images were enhanced using the CLAHE technique in MATLAB R2013a®. The technique was applied to enhance the green, red, NIR and SWIR bands of all the four datasets separately. The single band images were taken as the input images in MATLAB and the adaptive histogram equalization function was used over the image. The distribution of the neighbourhood was assumed to take the form of the Rayleigh distribution and a clip limit of 0.01 was used for all the images. The effectiveness of the enhancement technique was evaluated using two performance parameters such as Mean Square Error (MSE), Peak Signal-to- Noise Ratio (PSNR) and Degree of Contrast Enhancement (DCE).

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Fig 3.1 Flowchart of the methodology adopted

3.1 Mean Square Error (MSE)

MSE is the simplest and most widely used quality metric and is computed by averaging the squared intensity differences of original image and enhanced image3. The mean squared error (MSE) for all practical purposes allows us to compare the “true” pixel values of original image to degraded image. The MSE represents the average of the squares of the "errors" between actual image and noisy image. The error is the amount by which the values of the original image differ from the degraded image. Mean square error (MSE) is given by Eqn. (1).

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f (i, j) is the original image, F (i, j) is the enhanced image and M *N is the size of image with respect to rows and columns.

3.2 Peak Signal to Noise Ratio (PSNR)

PSNR is used to calculate the ratio between the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation3 1 4. The term peak signal-to-noise ratio (PSNR) is an expression for the ratio between the maximum possible value (power) of a signal and the power of distorting noise that affects the quality of its representation. Because many signals have very wide dynamic range, (ratio between the largest and smallest possible values of a changeable quantity) the PSNR is usually expressed in terms of the logarithmic decibel scale.

Image enhancement or improving the visual quality of a digital image can be subjective. Saying that one method provides a better quality image could vary from person to person. For this reason, it is necessary to establish quantitative/empirical measures to compare the effects of image enhancement algorithms on image quality. Using the same set of tests images, different image enhancement algorithms can be compared systematically to identify whether a particular algorithm produces better results or not. The metric under investigation is the peak-signal-to-noise ratio. If we can show that an algorithm or set of algorithms can enhance a degraded known image to more closely resemble the original, then we can more accurately conclude that it is a better algorithm.



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effectiveness contrast limited adaptive histogram equalization multispectral satellite imagery



Title: Effectiveness of Contrast Limited Adaptive Histogram Equalization on Multispectral Satellite Imagery