Lecture 7: Image Processing and Interpretation

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• • • • • • • • • • • • • • • • • • • • • • •

Class Activity –Concept Map

Image Enhancement

Linear Contrast Stretch Equalized Contrast Stretch

Spatial Filtering

Low-pass Filters High-pass Filters Directional Filters Image Ratios Principle Components Analysis

Contrast enhancement Image Processing

photo-interpretation digital image processing classification techniques

Image interpretation Photointerpretation machine-processing manipulations Image Restoration and Rectification Image Enhancement Image Classification

Unsupervised Classifications Supervised Classifications .

Image Restoration

•

Striping

•

line dropouts

•

Image Enhancement

•

Spatial Filtering

. •

Contrast Stretching

•

Image Histogram

•

Contrast Stretching

•

Low-pass Filters

•

High-pass Filters

•

Directional Filters

What is an Image?

• An image is an array, or a matrix, of square pixels (picture elements) arranged in columns and rows.

Figure 1: An image — an array or a matrix of pixels arranged in columns and rows.

Black and white image

• In a (8-bit) greyscale image each picture element has an assigned intensity that ranges from 0 to 255. A grey scale image is what people normally call a black and white image, but the name emphasizes that such an image will also include many shades of grey.

An example:8-bit greyscale image

•

Each pixel has a value from 0 (black) to 255 (white). The possible range of the pixel values depend on the colour depth of the image, here 8 bit = 256 tones or greyscales.

Pixel Values, DN

•

Pixel Values:

The magnitude of the electromagnetic energy (or, intensity) captured in a digital image is represented by positive digital numbers. • The

digital numbers

are in the form of

binary digits (or 'bits')

power of 2 which vary from 0 to a selected

Image Type 8-bit image 16-bit image 24-bit image Pixel Value 2 8 = 256 2 16 2 24 = 65536 = 16777216 Color Levels 0-255 0-65535 0-16777215

16 million colors!!!

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Image Type Pixel Value Color Levels 8-bit image 2 8 = 256 0-255 16-bit image 2 16 = 65536 0-65535 24-bit image 2 24 = 16777216 0-16777215

DN

Image below, brighter portions relate to higher energy levels

True color image

•

A true-colour image assembled from three greyscale images coloured red, green and blue. Such an image may contain up to 16 million different colors.

Image Resolution

•

Image Resolution:

the resolution of a digital image is dependant on the range in magnitude (i.e. range in brightness) of the pixel value. With a 2-bit image the maximum range in brightness is 2 2 = 4 values ranging from 0 to 3, resulting in a low resolution image. In an 8-bit image the maximum range in brightness is 2 8 = 256 values ranging from 0 to 255, which is a higher resolution image

2-bit Image (4 grey levels) 8-bit Image (256 grey levels)

two prime approaches in the use of remote sensing

• •

1) standard photo-interpretation of scene content 2) use of digital image processing and classification techniques that are generally the mainstay of practical applications of information extracted from sensor data sets To accomplish this, we will utilize just one Landsat TM subscene that covers the Morro Bay area on the south-central coast of California

Image interpretation

• relies on one or both of these approaches: – Photointerpretation :the interpreter uses his/her knowledge and experience of the real world to recognize scene objects (features, classes, materials) in photolike renditions of the images acquired by aerial or satellite surveys of the targets (land; sea; atmospheric; planetary) that depict the targets as visual scenes with variations of gray-scale tonal or color patterns (more generally, spatial or spectral variability that mirror the differences from place to place on the ground) – machine-processing manipulations information (usually computer-based) that analyze and reprocess the raw data into new visual or numerical products, which then are interpreted either by approach 1 or are subjected to appropriate decision-making algorithms that identify and classify the scene objects into sets of

Image Processing = CASI

• Computer-Assisted Scene Interpretation ( CASI ); also called Image Processing • The techniques fall into three broad categories : – Image Restoration and Rectification – Image Enhancement – Image Classification • There is a variety of CASI methods: contrast stretching, band ratioing, band transformation, Principal Component Analysis, Edge Enhancement, Pattern Recognition, and Unsupervised and Supervised Classification

Image Classification

• In classifying features in an image we use the

elements of visual interpretation

to

identify homogeneous groups of pixels

which represent various

features or land cover classes

of interest. In digital images it is possible to model this process, to some extent, by using two methods:

Unsupervised Classifications

and

Supervised Classifications .

•

Unsupervised Classifications

this is a computerized method without direction from the analyst in which pixels with similar digital numbers are grouped together into

spectral classes

using statistical procedures such as

nearest neighbour

and

cluster analysis

. The resulting image may then be interpreted by comparing the clusters produced with maps, airphotos, and other materials related to the image site.

•

Supervised Classification :

Training areas

•

Limitations to Image Classification :

have to be

approached with caution

is a

complex process

because it with many assumptions. In

supervised classifications

, training areas not have unique spectral characteristics resulting in incorrect classification. may

Unsupervised classifications

may require field checking in order to identify spectral classes if they cannot be verified by other means (i.e. maps and airphotos).

Classification

• Classification is probably the most informative means of interpreting remote sensing data •The output from these methods can be combined with other computer-based programs •The output can itself become input for organizing and deriving information utilizing what is known as Geographic Information Systems (GIS)

Image Processing Procedures

•

Image Restoration :

most recorded images are subject to distortion due to

noise

which degrades the image. Two of the more

common errors

that occur in multi-spectral imagery are

striping (or banding)

and

line dropouts

Image Processing Procedures

• •

Dropped Lines

are errors that occur in the sensor response and/or data recording and transmission which loses a row of pixels in the image.

Image Enhancement

• One of the strengths of image processing is that it gives us the

ability to enhance the view

of an area by manipulating the pixel values, thus making it

easier for visual interpretation.

• There are several

techniques

which we can use to enhance an image, such as

Contrast Stretching Filtering

.

and Spatial

Image Enhancement

•

Image Histogram

: For every digital image the pixel value represents the magnitude of an observed characteristic such as brightness level. An image histogram is a

graphical representation

of the brightness values that comprise an image. The

brightness values (i.e. 0-255)

are displayed along the

x axis

of the graph. The

frequency of occurrence

image is shown on the

y-axis.

of each of these values in the 8-bit image (0 - 255 brightness levels) Image Histogram x-axis = 0 to 255 y-axis = number of pixels

Class Activity

• http://www.fas.org/irp/imint/docs/rst/Sect1/ Sect1_1.html#1-2

TM Band 3 Image of Morro Bay, California

•

Image Enhancement

Contrast Stretching :

Quite often the useful data in a digital image populates only a small portion of the available range of digital values (commonly 8 bits or 256 levels). Contrast enhancement involves changing the original values so that more of the available range is used, this then increases the contrast between features and their backgrounds. There are several types of

contrast enhancements

which can be subdivided into

Linear

and

Non Linear procedures

.

•

Image Enhancement

Linear Contrast Stretch

full range.

:

This involves identifying lower and upper bounds from the histogram (usually the minimum and maximum brightness values in the image) and applying a transformation to stretch this range to fill the •

Equalized Contrast Stretch

: This stretch assigns more display values (range) to the frequently occurring portions of the histogram. In this way, the detail in these areas will be better enhanced relative to those areas of the original histogram where values occur less frequently.

Linear Stretch Example

:

Before Linear Stretch After Linear Stretch

The linear contrast stretch enhances the contrast in the image with

light toned areas appearing lighte

r and

dark areas appearing darker

, making visual interpretation much easier. This example illustrates the increase in contrast in an image before (left) and after (right) a linear contrast stretch.

Related to your activity last time -Is this stretching?

Spatial Filtering

• Spatial filters are designed to

highlight or suppress features

in an image based on their

spatial frequency

. The spatial frequency is related to the

textural characteristics

of an image. Rapid variations in brightness levels

('roughness')

reflect a

high spatial frequency

;

'smooth' areas

with little variation in brightness level or tone are characterized by a

low spatial frequency

characteristics. . Spatial filters are used to suppress 'noise' in an image, or to highlight specific image • •

Low-pass Filters High-pass Filters

•

Directional Filters

• etc

Spatial Filtering

•

Low-pass Filters: These are used to emphasize large homogenous areas of similar tone and reduce the smaller detail. Low frequency areas are retained in the image resulting in a smoother appearance to the image.

Linear Stretched Image Low-pass Filter Image

Spatial Filtering

•

High-pass Filters:

allow high frequency areas to pass with the resulting image having greater detail resulting in a sharpened image Hi-pass Filter Linear Contrast Stretch

Spatial Filtering

•

Directional Filters

:are designed to

enhance linear features

such as roads, streams, faults, etc.The filters can be designed to enhance features which are

oriented in specific directions

, making these useful for r

adar imagery

and for

geological applications.

Directional filters are also known as edge detection filters. Edge Detection Lakes & Streams Edge Detection Fractures & Shoreline

Image Ratios

• It is possible to

divide the digital numbers

of one image band by those of another image band to create a third image. Ratio images may be used to remove the influence of light and shadow on a ridge due to the sun angle. It is also possible to

calculate certain indices

which can enhance vegetation or geology

Sensor Image Ratio Landsat TM Bands 3/2 Landsat TM Bands 4/3 Landsat TM Bands 7/5 EM Spectrum red/green PhotoIR/red SWIR/NIR Application Soils Biomass Clay Minerals/Rock Alteration

For example: Normalized Difference Vegetation Index (NDVI):

a commonly use vegetation index which uses the red and infrared bands of the EM spectrum.

Image Ratio example: NDVI

NDVI image of Canada.

Green/Yellow/Brown represent decreasing magnitude of the vegetation index.

Principle Components Analysis

• Different bands in

multispectral images

like those from Landsat TM have

similar visual appearances

since reflectances for the same surface cover types are almost equal. Principle Components Analysis is a

statistical procedure

designed to

reduce the data redundancy

and put as much information from the image bands into fewest number of components. The intent of the procedure is to produce an image which is easier to interpret than the original.

Data Visualization

Contrast enhancement or stretch reassigns the DN range that corresponds to the 256 gray shades Top row of images are ETM+ data with no enhancement and bottom row consists of linear contrast stretches of the image DNs to the full 0-255 gray shades

Data Visualization

Ability to quickly discern features is improved by using 3-band color mixes Image below assigns blue to band 2, green to band 4, and red to band 7 Vegetation is green Surface water is blue Playa is gray and white (Playas are dry lakebeds)

Color display

Rely on display hardware to convert between DN and gray levels  Digital Numbers (DNs) are image data  Grey Levels (GLs) are numerical display values  brightness, contrast and colors  Look-Up Tables (LUTs) map DNs —> GLs and change image Actual displayed colors depend on the color response characteristics of the display system

Data Visualization

Changing the color assignment to red, green, and blue does not alter the surface material only appearance of the image All images below show only combinations of bands 2, 4, and 7 of ETM+

Data Visualization

Other band combinations of the same data set bring out different features (or in some cases lack there of) All images below show only combinations of bands 2, 4, and 7 of ETM+

Video: Wonder How Hubble Color Images are made?

• http://hubblesite.org/gallery/behind_the_pi ctures/ “Images must be woven together from the incoming data from the cameras, cleaned up and given colors that bring out features that eyes would otherwise miss.

File formats

File formats play an important role in that many are automatically recognized in image processing packages  Makes life very easy  Raw data typically have no header information  header (http://remotesensing.org/geotiff/geotiff.html)  GeoTIFF is a variant of TIFF that includes geolocation information in HDF or Hierarchical Data Format (http://hdf.ncsa.uiuc.edu/) is a self documenting format  All metadata needed to read image file contained within the image file   Allows for variable length subfiles     First developed for web sites in the 1980s EOS-HDF is NASA version (http://hdf.ncsa.uiuc.edu/hdfeos.html) NITF National Imagery Transmission Format (http://remotesensing.org/gdal/frmt_nitf.html) Department of Defense

Data processing levels

Recently, operational processing of remote sensing data has led to multiple processing levels      “Standard” types of preprocessing Radiometric calibration Geometric calibration Noise removal Formatting     Generic description Level 0: raw, unprocessed sensor data Level 1: radiometric (1R or 1B) or geometric processing (1G) Level 2: derived product, e.g. vegetation index