7 elements of remote sensing process 1.Energy Source (A) 2.Radiation & Atmosphere (B) 3.Interaction with Targets (C) 4.Recording of Energy by Sensor (D) 5.Transmission & Reception (E) 6.Interpretation and Analysis (F) 7.Application (G)

Extract meaningful information from imagery 6. Interpretation and Analysis (F) - the processed image is interpreted, visually and/or digitally, to extract information about the target which was illuminated.

4.3 Digital Image Processing Common image processing image analysis functions: A. Preprocessing B. Image Enhancement C. Image Transformation D. Image Classification and Analysis

Background DIP - manipulation & interpretation of images Began in 1960’s Landsat 1 launched Access to low cost, efficient computers Access to imagery

Digital spatial image - made up of a grid of cells, each containing a value or measurement and representing an area of the Earth’s surface. Pixel

Digital Number (DN) - value stored within a pixel of an image, represents amount of light reflected back to sensor. digital format – images are represented in a computer as arrays of pixels.

Multispectral images - multiple layers representing different parts of the EMS.

4.2 Elements of Visual Interpretation Identifying targets –Based on how they reflect/emit radiation Based on; –Visual elements – tone, shape, pattern, texture, shadow, association.

4.3 Digital Image Processing Common image processing image analysis functions: A. Preprocessing B. Image Enhancement C. Image Transformation D. Image Classification and Analysis

1. Pre-Processing (Image Rectification) Initial processing of raw data prior for analysis Correct for distortion due to characteristics of imaging system & imaging conditions.

1. Pre-Processing (Image Rectification) Procedures include: a. geometric correction - correct for geometric distortion due to Earth's rotation, curvature, platform motion, relief displacement, (such as oblique viewing). b. radiometric correction - correct for uneven sensor response over image, random noise, atmosphere. c. geo-referencing - ground control points (GCP's) used to register image to a precise map.

2. Image Enhancement Solely to improve appearance of imagery. Increasing visual distinction Un-enhanced images usually appear very dark - little contrast - difficult to visually interpret. Various procedures applied to image data in order to more effectively display data for visual interpretation.

2. Image Enhancement A. Contrast stretching –Histograms –Increase tonal distinction B. Spatial filtering –Enhance/suppress features

A. Contrast stretching Radiometric enhancement - manipulate brightness and contrast of pixels to amplify differences between features. Changes made to pixels without consideration of values of surrounding pixels. –adjust brightness and contrast controls –apply preset contrast stretches –manually adjusting image histograms

A. Contrast stretching Radiometric Enhancement Not all values will be used or spread out to fill the entire range of 256 values. Need to manipulate the relative brightness and contrast of the pixels to amplify the differences between features.

Lanier.img (4-3-2) Swipe

Computers - ideal for manipulating and analyzing large continuous data sets displayed as grayscale. Used to distinguish between slight spectral variations and enhance them.

Landsat 7 image with no contrast stretching - histogram for the near infrared band. Some features, like agricultural areas, can be distinguished.

Applying a histogram stretch produces a simple classification of urban, agricultural, and mixed use areas.

A. Contrast stretching Radiometric resolution Dynamic range or number of possible data values (Digital numbers) in each band of the image. The range of DN’s is usually referred to by the number of bits into which the recorded energy is divided. 2 8 = 256 is most common

0 = black 255 = white

A sensor measures the electromagnetic energy within its range. Total intensity of the energy from zero to the maximum is broken down into 256 brightness values for 8-bit data.

A. Contrast stretching Linear grey-level stretching Lower threshold value is chosen so that all pixel values below threshold are mapped to zero. Upper threshold value is chosen so that all pixel values above threshold are mapped to 255. All other pixel values are linearly interpolated to lie between 0 and 255. –Lower and upper thresholds are usually chosen to be values close to the minimum and maximum pixel values of the image.

Two types of histogram stretches

Landsat TM image - Olympic Pennisula, NW Washington Vis red band