Multispectral pattern recognition
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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 (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.
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 (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.
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.
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) algorithm used for Multispectral pattern recognition was developed by Geoffrey H. Ball and David J. Hall at Stanford Research Institute. The ISODATA algorithm is a modification of the k-means clustering algorithm, with added heuristic rules based on experimentation. In outlines: INPUT. dataset, user specified configuration values. Initialize cluster points for k-means algorithm randomly. DO UNTIL. termination conditions are satisfied Run a few iterations of the k-means algorithm. Split a cluster point into two if the standard deviation of the points in the cluster is too high. Merge two cluster points into one if the distance between their mean is too low. Delete a cluster point if it contains too few data points. Delete data points that are too distant from its cluster point. Check heuristic conditions for termination. RETURN. clusters found There are many possible heuristic conditions for termination, depending on the implementation.
References
Ball, Geoffrey H., Hall, David J. (1965) Isodata: a method of data analysis and pattern classification, Stanford Research Institute, Menlo Park, United States. Office of Naval Research. Information Sciences Branch Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern Classification. New York: John Wiley & Sons. Jensen, J. R. (2005). Introductory Digital Image Processing: A Remote Sensing Perspective. Upper Saddle River : Pearson Prentice Hall. Belokon, W. F. et al. (1997). Multispectral Imagery Reference Guide. Fairfax: Logicon Geodynamics, Inc.
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