Multispectral pattern recognition
Encyclopedia
Multispectral remote sensing is the collection and analysis of reflected, emitted, or back-scattered energy from an object or an area of interest in multiple bands of regions of the electromagnetic spectrum
Electromagnetic spectrum
The electromagnetic spectrum is the range of all possible frequencies of electromagnetic radiation. The "electromagnetic spectrum" of an object is the characteristic distribution of electromagnetic radiation emitted or absorbed by that particular object....

 (Jensen, 2005). Subcategories of multispectral remote sensing include hyperspectral, in which hundreds of bands are collected and analyzed, and ultraspectral remote sensing where many hundreds of bands are used (Logicon, 1997). The main purpose of multispectral imaging is the potential to classify the image using multispectral classification. This is a much faster method of image analysis than is possible by human interpretation.

The ISODATA algorithm used for Multispectral pattern recognition was developed by Geoffrey H. Ball and David J. Hall, working in the Stanford Research Institute in Meleno Park. They published their findings in a technical report entitled: ISODATA, a novel method of data analysis and pattern classification (Stanford Research Institute, 1965.) ISODATA is defined in the abstract as: 'a novel method of data analysis and pattern classification, is described in verbal and pictorial terms, in terms of a two-dimensional example, and by giving the mathematical calculations that the method uses. The technique clusters many-variable data around points in the data's original high- dimensional space and by doing so provides a useful description of the data.' (1965, pp v.)ISODATA was developed to facilitate the modelling and tracking of weather patterns.

Multispectral remote sensing systems using ISODATA

Remote sensing systems gather data via instruments typically carried on satellites in orbit around the Earth. The remote sensing scanner detects the energy that radiates from the object or area of interest. This energy is recorded as an analog electrical signal and converted into a digital value though an A-to-D conversion. There are several multispectral remote sensing systems that can be categorized in the following way:

Multispectral Imaging using discrete detectors and scanning mirrors

  • Landsat Multispectral Scanner (MSS
    Multispectral Scanner
    The Multispectral Scanner is one of the Earth observing sensors introduced in the Landsat program. A Multispectral Scanner was placed aboard each of the first five Landsat satellites.-MSS Technical Specifications:...

    )
  • Landsat Thematic Mapper (TM
    Thematic Mapper
    One of the Earth observing sensors introduced in the Landsat program. A Thematic Mapper was first placed aboard Landsat 4 , and one is still operational aboard Landsat 5. TM sensors feature seven bands of image data most of which have 30 metre spatial resolution...

    )
  • NOAA Geostationary Operational Environmental Satellite (GOES
    Goes
    Goes is a municipality and a city in the southwestern Netherlands in Zuid-Beveland, in the province Zeeland. The city of Goes has approximately 27,000 residents.-History of Goes:...

    )
  • NOAA Advanced Very High Resolution Radiometer (AVHRR)
  • NASA and ORBIMAGE, Inc., Sea-viewing Wide field-of-view Sensor (SeaWiFS
    SeaWiFS
    SeaWiFS stands for Sea-viewing Wide Field-of-view Sensor. It was the only scientific instrument on GeoEye's OrbView-2 satellite, and was a follow-on experiment to the Coastal Zone Color Scanner on Nimbus 7...

    )
  • Daedalus, Inc., Aircraft Multispectral Scanner (AMS)
  • NASA Airborne Terrestrial Applications Sensor (ATLAS)

Multispectral Imaging Using Linear Arrays

  • SPOT
    SPOT (satellites)
    SPOT is a high-resolution, optical imaging Earth observation satellite system operating from space. It is run by Spot Image based in Toulouse, France...

     1, 2, and 3 High Resolution Visible (HRV) sensors and Spot 4 and 5 High Resolution Visible Infrared (HRVIR) and vegetation sensor
  • Indian Remote Sensing System (IRS
    Indian Remote Sensing satellite
    Indian Remote Sensing satellites are a series of Earth Observation satellites, built, launched and maintained by Indian Space Research Organisation...

    ) Linear Imaging Self-scanning Sensor (LISS)
  • Space Imaging, Inc. (IKONOS
    IKONOS
    IKONOS is a commercial earth observation satellite, and was the first to collect publicly available high-resolution imagery at 1- and 4-meter resolution. It offers multispectral and panchromatic imagery. The IKONOS launch was called by John E. Pike “one of the most significant developments in...

    )
  • Digital Globe, Inc. (QuickBird
    QuickBird
    QuickBird is a high-resolution commercial earth observation satellite, owned by DigitalGlobe and launched in 2001 as the first satellite in a constellation of three scheduled to be in orbit by 2008...

    )
  • ORBIMAGE, Inc. (OrbView-3)
  • ImageSat International, Inc. (EROS A1
    EROS (satellite)
    Earth Resources Observation Satellite is a series of Israeli commercial Earth observation satellites, designed and manufactured by Israel Aircraft Industries , with optical payload supplied by El-Op. The satellites are owned and operated by ImageSat International, another Israeli company, with...

    )
  • NASA Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER
    Advanced Spaceborne Thermal Emission and Reflection Radiometer
    ASTER is a Japanese sensor which is one of five remote sensory devices on board the Terra satellite launched into Earth orbit by NASA in 1999...

    )
  • NASA Terra Multiangle Imaging Spectroradiometer (MISR
    MISR
    The Multi-angle Imaging SpectroRadiometer is a scientific instrument on the Terra satellite launched by NASA on December 18, 1999. This device is designed to measure the intensity of solar radiation reflected by the Earth system in various directions and spectral bands; it became operational in...

    )

Imaging Spectrometry Using Linear and Area Arrays

  • NASA Jet Propulsion Laboratory Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)
  • Compact Airborne Spectrographic Imager 3 (CASI 3)
  • NASA Terra Moderate Resolution Imaging Spectrometer (MODIS
    MODIS
    The Moderate-resolution Imaging Spectroradiometer is a payload scientific instrument launched into Earth orbit by NASA in 1999 on board the Terra Satellite, and in 2002 on board the Aqua satellite...

    )
  • NASA Earth Observer (EO-1
    Earth Observing-1
    The Earth Observing-1 Mission is part of NASA's New Millennium Program , to develop and validate a number of instrument and spacecraft bus breakthrough technologies designed to enable the development of future earth imaging observatories that will have a significant increase in performance while...

    ) Advanced Land Imager (ALI), Hyperion, and LEISA Atmospheric Corrector (LAC)

Satellite Analog and Digital Photographic Systems

  • Russian SPIN-2 TK-350, and KVR-1000
  • NASA Space Shuttle
    Space Shuttle
    The Space Shuttle was a manned orbital rocket and spacecraft system operated by NASA on 135 missions from 1981 to 2011. The system combined rocket launch, orbital spacecraft, and re-entry spaceplane with modular add-ons...

     and International Space Station
    International Space Station
    The International Space Station is a habitable, artificial satellite in low Earth orbit. The ISS follows the Salyut, Almaz, Cosmos, Skylab, and Mir space stations, as the 11th space station launched, not including the Genesis I and II prototypes...

     Imagery

Multispectral classification methods

A variety of methods can be used for the multispectral classification of images:
  • Algorithms based on parametric and nonparametric statistics that use ratio-and interval-scaled data and nonmetric methods that can also incorporate nominal scale data (Duda et al., 2001),
  • Supervised or unsupervised classification logic,
  • Hard or soft (fuzzy) set classification logic to create hard or fuzzy thematic output products,
  • Per-pixel or object-oriented classification logic, and
  • Hybrid approaches

Supervised classification

In this classification method, the identity and location of some of the land-cover types are obtained beforehand from a combination of fieldwork, interpretation of aerial photography, map analysis, and personal experience. The analyst would locate sites that have similar characteristics to the known land-cover types. These areas are known as training sites because the known characteristics of these sites are used to train the classification algorithm for eventual land-cover mapping of the remainder of the image. Multivariate statistical parameters (means, standard deviations, covariance matrices, correlation matrices, etc) are calculated for each training site. All pixels inside and outside of the training sites are evaluated and allocated to the class with the more similar characteristics.

Classification scheme

The first step in the supervised classification method is to identify the land-cover and land-use classes to be used. Land-cover refers to the type of material present on the site (e.g. water, crops, forest, wet land, asphalt, and concrete). Land-use refers to the modifications made by people to the land cover (e.g. agriculture, commerce, settlement). All classes should be selected and defined carefully to properly classify remotely sensed data into the correct land-use and/or land-cover information. To achieve this purpose, it is necessary to use a classification system that contains taxonomically correct definitions of classes. If a hard classification is desired, the following classes should be used:
  • Mutually exclusive: there is not any taxonomic overlap of any classes (i.e., rain forest and evergreen forest are distinct classes).
  • Exhaustive: all land-covers in the area have been included.
  • Hierarchical: sub-level classes (e.g., single-family residential, multiple-family residential) are created, allowing that these classes can be included in a higher category (e.g., residential).


Some examples of hard classification schemes are:
  • American Planning Association Land-Based Classification System
  • United States Geological Survey Land-use/Land-cover Classification System for Use with Remote Sensor Data
  • U.S. Department of the Interior Fish and Wildlife Service
  • U.S. National Vegetation and Classification System
  • International Geosphere-Biosphere Program IGBP Land Cover Classification System

Training sites

Once the classification scheme is adopted, the image analyst may select training sites in the image that are representative of the land-cover or land-use of interest. If the environment where the data was collected is relatively homogeneous, the training data can be used. If different conditions are found in the site, it would not be possible to extend the remote sensing training data to the site. To solve this problem, a geographical stratification should be done during the preliminary stages of the project. All differences should be recorded (e.g. soil type, water turbidity, crop species, etc). These differences should be recorded on the imagery and the selection training sites made based on the geographical stratification of this data. The final classification map would be a composite of the individual stratum classifications.

After the data are organized in different training sites, a measurement vector is created. This vector would contain the brightness values for each pixel
Pixel
In digital imaging, a pixel, or pel, is a single point in a raster image, or the smallest addressable screen element in a display device; it is the smallest unit of picture that can be represented or controlled....

 in each band in each training class. The mean
Arithmetic mean
In mathematics and statistics, the arithmetic mean, often referred to as simply the mean or average when the context is clear, is a method to derive the central tendency of a sample space...

, standard deviation
Standard deviation
Standard deviation is a widely used measure of variability or diversity used in statistics and probability theory. It shows how much variation or "dispersion" there is from the average...

, variance-covariance matrix, and correlation matrix are calculated from the measurement vectors.

Once the statistics from each training site are determined, the most effective bands for each class should be selected. The objective of this discrimination is to eliminate the bands that can provide redundant information. Graphical and statistical methods can be used to achieve this objective. Some of the graphic methods are:
  • Bar graph spectral plots
  • Cospectral mean vector plots
  • Feature space plots
  • Cospectral parallelepiped or ellipse plots

Classification algorithm

The last step in supervised classification is selecting an appropriate algorithm. The choice of a specific algorithm depends on the input data and the desired output. Parametric algorithms are based on the fact that the data is normally distributed. If the data is not normally distributed, nonparametric algorithms should be used. The more common nonparametric algorithms are:
  • One-dimensional density slicing
  • Parallelipiped
  • Minimum distance
  • Nearest-neighbor
  • Neural network and expert system analysis

Unsupervised classification

Unsupervised classification (also known as clustering) is a method of partitioning remote sensor image data in multispectral feature space and extracting land-cover information. Unsupervised classification require less input information from the analyst compared to supervised classification because clustering does not require training data. This process consists in a series of numerical operations to search for the spectral properties of pixels. From this process, a map with m spectral classes is obtained. Using the map, the analyst tries to assign or transform the spectral classes into thematic information of interest (i.e. forest, agriculture, urban).
This process may not be easy because some spectral clusters represent mixed classes of surface materials and may not be useful. The analyst has to understand the spectral characteristics of the terrain to be able to label clusters as a specific information class. There are hundreds of clustering algorithms. Two of the most conceptually simple algorithms are the chain method and the ISODATA method.

Chain method

The algorithm used in this method operates in a two-pass mode (it passes through the multispectral dataset two times. In the first pass, the program reads through the dataset and sequentially builds clusters (groups of points in spectral space). Once the program reads though the dataset, a mean vector is associated to each cluster. In the second pass, a minimum distance to means classification algorithm is applied to the dataset, pixel by pixel. Then, each pixel is assigned to one of the mean vectors created in the first step.....

ISODATA method

The Iterative Self-Organizing Data Analysis Technique (ISODATA) method used a set of rule-of-thumb procedures that have incorporated into an iterative classification algorithm. Many of the steps used in the algorithm are based on the experience obtained through experimentation. The ISODATA algorithm is a modification of the k-means clustering algorithm. This algorithm includes the merging of clusters if their separation distance in multispectral feature space is less than a user-specified value and the rules for splitting a single cluster into two clusters. This method makes a large number of passes through the dataset until specified results are obtained.
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