Image classification in the field of remote sensing refers to the assignment of land cover categories (or classes) to image pixels. For instance, land cover data collections and imagery can be classified into urban, agriculture, forest, and other classes for the sake of further analysis and processing.
Remote Sensing: Image Classification Techniques
Typically, professionals in GIS remote sensing work with three types of image classification techniques; these are:
- Unsupervised remote sensing image classification
- Supervised remote sensing image classification
- Image analysis based on objects.
Out of these, supervised and unsupervised image classification techniques are the most commonly used of the three.
Unsupervised Classification
In unsupervised classification, pixels are grouped into ‘clusters’ on the basis of their properties. K-means and ISODATA are among the popular image clustering algorithms used by GIS data analysts for creating land cover maps in this basic technique of image classification. Once a clustering algorithm is selected, the number of groups to be generated has to be identified. In the next step, every individual unclassified cluster is identified with land cover classes. As samples are not necessary for unsupervised classification, this technique serves as an easy means of segmenting and understanding images.
Supervised Classification
Supervised classification requires the selection of representative samples for individual land cover classes. Thereafter, software like IKONOS makes use of ‘training sites’ to apply them to the images in the reckoning. In this technique of remote sensing image classification, spectral signature described in the training set are used trained GIS experts to deliver accurate and detailed results. The most commonly used supervised classification algorithms are minimum-distance classification and maximum likelihood.
Application of Unsupervised and Supervised Classification Methods
Both these types of classification methods necessitate requisite degree of knowledge in the areas of interest. The most important being:
- The degree of class details needed.
- The quality of geo-spectral data wherein the classification algorithm will be applied.
Difference Between Unsupervised and Supervised Classification
In the case of unsupervised classification technique, the analyst designates labels and combine classes after ascertaining useful facts and information about classes such as agricultural, water, forest, etc. Because of the presence of mixed land cover classes, the assignment of geo-spectral clusters becomes a difficult task for GIS experts. Therefore, unsupervised classification is mainly used for the quick assignment of labels to simpler, less complex, and broadly defined land cover classes. These classes include vegetation/non-vegetation, water, forested/non-forested, and other related classes. In other words, unsupervised classification is responsible for reducing analyst bias.
On the other hand, supervised classification permits the fine tuning of information classes by analysts; these may be linked to finer subcategories including level classes. In this image classification technique, high-accuracy GPS devices are used for collecting training data in the field. By using the supervised approach, GIS analysts can zoom in on any area, decipher the problem minutely, and use more accurate data to train classification algorithms. In comparison to unsupervised data, the usage of training data in supervised classification yields more accurate results. This is because of the presence of reduced mixed pixels in the data collected through the supervised approach.
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