spatial enhancement

SPATIAL FEATURE MANIPULATION

ABIN V. ARKKATTU

Image enhancement is the process of making images more useful.

The reasons for doing this include:

Highlighting interesting detail in images

Removing noise from images

Making images more visually appealing

Methods of Enhancement1. Contrast manipulation

Contrast stretching = expand the DN values beyond their natural range to fill the 0-255 range.

2. Spatial feature manipulationRefers to image texture.

Smooth areas have low spatial frequencies, gray values change gradually.

Rough areas have high spatial frequencies and gray values change abruptly.

Methods of Enhancement3. Multi-image manipulation.

Two or more images combined mathematically, commonly by ratios.

Used to develop green vegetative index images, e.g., the NDVI.

SPATIAL FEATURE MANIPULATION

SPATIAL FILTERING

CONVOLUTION

EDGE ENHANCEMENT

DIRECTIONAL FIRST DIFFRENCING

FOURIER ANALYSIS

Filters

Low-pass filter –

designed to emphasize larger, homogeneous areas of similar tone

and reduce smaller detail.

low-pass filters smooth the appearance of an image.

High-pass filters do the opposite –

sharpen the appearance of fine detail in an image.

Directional, or edge detection filters are designed to highlight

linear features, such as roads or field boundaries.

enhance features which are oriented in specific directions.

useful for detection of linear geologic structures.

Original image Low frequency component image

High frequency component image

Low Pass Filter

Image Frequencies

• Low Frequency Components = Slow Changes in Pixel Intensity

• regions of uniform intensity

High Frequency component of image

and filtering

•High Frequency Components = Rapid Changes in Pixel Intensity

•regions with lots of details

High Frequency Component

Convolution Spatial filtering is but one spatial application of the generic image processing operation called convolution. Convolving an image involves the following procedures.

•A moving window is established that contains an array of coefficients or weighing factors. Such arrays are referred to as operators or kernels , and they are normally an odd number of pixels in size (eg. 3 x 3,5 x 5)

•The kernel is moved throughout the original image and the DN at the center of the kernel in a second(convoluted) output image is obtained by multiplying each coefficient in the kernel by the corresponding DN in the original image and adding all the resulting products. This operation is performed for each pixel in the original image.

Convolution

The generic image processing operation

Spatial filter convolutionProcedure

Establish a moving window (operators/kernels)Moving the window throughout the original image

Example

(a) KernelSize: odd number of pixels (3x3, 5x5, 7x7, …)Can have different weighting scheme (Gaussian distribution, …)

(b) original image DN (c) convolved image DN

Pixels around border cannot be convolved

The purpose of edge enhancement is to highlight fine detail in an image or to restore, at least partially, detail that has been blurred (either in error or as a consequence of a particular method of image acquisition).

EDGE ENHANCEMENT

Edge enhancement

Typical procedures

Roughness kernel size

Rough smallSmooth large

Add back a fraction of gray level to the high frequency component image

High frequency exaggerate local contrast but lose low frequency brightness information

DIRECTIONAL FIRST DIFFRENCING

Determine the first derivative of gray levels with respect to agiven direction.

Normally add the display value median back to keep all positive values.

It is another enhancement technique aimed at emphasizing edges in image data.

It is a procedure that systematically compares each pixel in an image to one of its immediately adjacent neighbors and displays the difference in terms of the gray levels of an output image.

This process is mathematically asking to determine the first derivative of gray levels with respect to a given direction.

The direction used can be horizontal, vertical , or diagonal.

Fourier analysis

Spatial domain frequency domain

Fourier transform

Conceptual description

Fit a continuous function through the discrete DN values if they were plotted along each row and column in an imageThe “peaks and valleys” along any given row or column can be described mathematically by a combination of sine and cosine waves with various amplitudes, frequencies, and phases

Fourier spectrum

Low frequency centerHigh frequency outwardVertical aligned features horizontal componentsHorizontal aligned features vertical components

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