Introduction
The ERDAS IMAGINE system incorporates the functions of both image processing and GIS. These functions include importing, viewing, altering, and analyzing raster and vector data sets.This chapter is an introduction to raster data, including:
• remote sensing
• data storage formats
• different types of resolution
• radiometric correction
• geocoded data
• raster data in GIS
See "Vector Data" on page 41 for more information on vector data.
Image Data
In general terms, an image is a digital picture or representation of an object. Remotely sensed image data are digital representations of the Earth. Image data are stored in data files, also called image files, on magnetic tapes, computer disks, or other media. The data consist only of numbers. These representations form images when they are displayed on a screen or are output to hardcopy.Each number in an image file is a data file value. Data file values are sometimes referred to as pixels. The term pixel is abbreviated from picture element. A pixel is the smallest part of a picture (the area being scanned) with a single value. The data file value is the measured brightness value of the pixel at a specific wavelength.
Raster image data are laid out in a grid similar to the squares on a checkerboard. Each cell of the grid is represented by a pixel, also known as a grid cell.
In remotely sensed image data, each pixel represents an area of the Earth at a specific location. The data file value assigned to that pixel is the record of reflected radiation or emitted heat from the Earth’s surface at that location.
Data file values may also represent elevation, as in digital elevation models (DEMs).
The terms pixel and data file value are not interchangeable in ERDAS IMAGINE. Pixel is used as a broad term with many meanings, one of which is data file value. One pixel in a file may consist of many data file values. When an image is displayed or printed, other types of values are represented by a pixel.
See "Image Display" on page 145 for more information on how images are displayed.
Bands
Image data may include several bands of information. Each band is a set of data file values for a specific portion of the electromagnetic spectrum of reflected light or emitted heat (red, green, blue, near-infrared, near-infrared, thermal, and so forth) or some other user-defined information created by combining or enhancing the original bands, or creating new bands from other sources.ERDAS IMAGINE programs can handle an unlimited number of bands of image data in a single file.
Figure 1: Pixels and Bands in a Raster Image
See "Enhancement" on page 455 for more information on combining or enhancing bands of data.
Bands vs. Layers
In ERDAS IMAGINE, bands of data are occasionally referred to as layers. Once a band is imported into a GIS, it becomes a layer of information which can be processed in various ways. Additional layers can be created and added to the image file (.img extension) in ERDAS IMAGINE, such as layers created by combining existing layers.
Read more about image files in ERDAS IMAGINE Format (.img) on page 25.
1 pixel
3 bands
Layers vs. Viewer Layers
The Viewer permits several images to be layered, in which case each image (including a multiband image) may be a layer.
Numeral Types
The range and the type of numbers used in a raster layer determine how the layer is displayed and processed. For example, a layer of elevation data with values ranging from -51.257 to 553.401 would be treated differently from a layer using only two values to show land and water.
The data file values in raster layers generally fall into these categories:
• Nominal data file values are simply categorized and named. The actual value used for each category has no inherent meaning—it is simply a class value. An example of a nominal raster layer would be a thematic layer showing tree species.
• Ordinal data are similar to nominal data, except that the file values put the classes in a rank or order. For example, a layer with classes numbered and named:
1 - Good, 2 - Moderate, and 3 - Poor is an ordinal system.
• Interval data file values have an order, but the intervals between the values are also meaningful. Interval data measure some
characteristic, such as elevation or degrees Fahrenheit, which does not necessarily have an absolute zero. (The difference between two values in interval data is meaningful.)
• Ratio data measure a condition that has a natural zero, such as electromagnetic radiation (as in most remotely sensed data), rainfall, or slope.
Nominal and ordinal data lend themselves to applications in which categories, or themes, are used. Therefore, these layers are sometimes called categorical or thematic.
Likewise, interval and ratio layers are more likely to measure a condition, causing the file values to represent continuous gradations across the layer. Such layers are called continuous.
Coordinate Systems
The location of a pixel in a file or on a displayed or printed image is expressed using a coordinate system. In two-dimensional coordinate systems, locations are organized in a grid of columns and rows. Each location on the grid is expressed as a pair of coordinates known as X and Y. The X coordinate specifies the column of the grid, and the Y coordinate specifies the row. Image data organized into such a grid are known as raster data.There are two basic coordinate systems used in ERDAS IMAGINE:
• file coordinates—indicate the location of a pixel within the image (data file)
• map coordinates—indicate the location of a pixel in a map File Coordinates
File coordinates refer to the location of the pixels within the image (data) file. File coordinates for the pixel in the upper left corner of the image always begin at 0, 0.
Figure 2: Typical File Coordinates
Map Coordinates
Map coordinates may be expressed in one of a number of map coordinate or projection systems. The type of map coordinates used by a data file depends on the method used to create the file (remote sensing, scanning an existing map, and so forth). In ERDAS IMAGINE, a data file can be converted from one map coordinate system to another.
For more information on map coordinates and projection systems, see "Cartography" on page 211 or "Map Projections" on page 297. See "Rectification" on page 251 for more information on changing the map coordinate system of a data file.
Remote Sensing
Remote sensing is the acquisition of data about an object or scene by a sensor that is far from the object (Colwell, 1983). Aerial photography, satellite imagery, and radar are all forms of remotely sensed data.Usually, remotely sensed data refer to data of the Earth collected from sensors on satellites or aircraft. Most of the images used as input to the ERDAS IMAGINE system are remotely sensed. However, you are not limited to remotely sensed data.
rows (y) (3,1)
x,y
columns (x) 0
1 2 3
0 1 2 3 4
This section is a brief introduction to remote sensing. There are many books available for more detailed information, including Colwell, 1983, Swain and Davis, 1978; and Slater, 1980 (see
"Bibliography" on page 777).
Electromagnetic Radiation Spectrum
The sensors on remote sensing platforms usually record
electromagnetic radiation. Electromagnetic radiation (EMR) is energy transmitted through space in the form of electric and magnetic waves (Star and Estes, 1990). Remote sensors are made up of detectors that record specific wavelengths of the electromagnetic spectrum. The electromagnetic spectrum is the range of electromagnetic radiation extending from cosmic waves to radio waves ("Jensen, 1996").
All types of land cover (rock types, water bodies, and so forth) absorb a portion of the electromagnetic spectrum, giving a distinguishable signature of electromagnetic radiation. Armed with the knowledge of which wavelengths are absorbed by certain features and the intensity of the reflectance, you can analyze a remotely sensed image and make fairly accurate assumptions about the scene. Figure 3: illustrates the electromagnetic spectrum (Suits, 1983; Star and Estes, 1990).
Figure 3: Electromagnetic Spectrum
SWIR and LWIR
The near-infrared and middle-infrared regions of the electromagnetic spectrum are sometimes referred to as the short wave infrared region (SWIR). This is to distinguish this area from the thermal or far infrared region, which is often referred to as the long wave infrared region (LWIR). The SWIR is characterized by reflected radiation whereas the LWIR is characterized by emitted radiation.
micrometers μm (one millionth of a meter)
0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0