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Forestry
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Mulit-Spectral/Textural supervised classification - Land Cover Mapping with SPOT in Indonesia
After several tests, the best classification results are obtained by breaking down land cover units (main classes) into sub-classes. As the number of units becomes greater, so these units are spectrally resemblant. If they are broken down into subunits, spectral confusions are thus reduced. However, confusions are still present. Classification methods should therefore become progressively more accurate in order to handle a large number of classes.
This is consequently the objective of the supervised classification method presented in this paper. The method progresses through several stages. Each of these stages has been developed in order to achieve its purpose in the most efficient way possible.
Classification training
- Spectral training
The first step is training which is essential and often fastidious for supervised classification: the operator should intervene and act accordingly at each level of the training program. The results of classification depend on the accuracy of the samples. The difficulty in locating the samples is proportional to the number of classes. Indentifying a sample which features two subclasses is a frequent occurrence.
In all cases samples shall be representative, well spread out over the image and in sufficient number. Statistics have revealed that the number of samples should be between 4 and 15; above this number, class characteristics are more or less constant (Ducros-Gambart 1988).
The distribution of samples over the entire image is mandatory, due to atmospheric and climatic variations as well as to agricultural activities, which imply reflectance variations for one same land cover unit. In this case, a unit is broken down into subclasses, which can subsequently be grouped.
Sample checking is achieved through the acquisition of statistics in both graphic and tabular form. Certain corrections are automatic. Establishing class histogram threshold levels enables value extremes, originating from parasitc points within samples, to be eliminated.
A procedure compares the resemblance of a sample to its class (or subclass) through a divergence computation of the samples. This ensures that all the samples are correctly attributed to this class. If a sample is excessively uncharacteristic, it is either eliminated o affected to another subclass if it can characterize this subclass.
These processes enable the rigorous acquisition of spectral class characteristics while eliminating error as well as avoiding tedious processing and checking.
Class confusion matrices are calculated in order to identify interclass confusions in various combinations of spectral or synthetic channels (index of vegetation, of brightness etc.) Based on these matrices, subclasses may be grouped. Their number can therefore be reduced. Combinations of channels providing optimum results are also determined.
- Textural training.
The second stage, and second part of the training program, consists in detecting textural characteristics, which will enable confusions subsisting between classes subsequent to the first part to be eliminated.
The originality of the method consists in only applying textural discrimination in the classification for classes, which feature confusions; in general each class is only confused with a few classes. Confusion matrices enable the required classes to be determined.
Furthermore, it appears that each class can only be characterized by a reduced number of textural parameters. Only the textural factors, which determine a given class, will be used to identify this class without a systematic computation of all textural features. This not only reduces computation times but also improves results of the textural classification.
Among textural parameters characteristic of one class, certain are essential either for the description of class or for its separation in relation to other classes. These parameters are consequently sequenced, and intervene in the classification in a pre-established order as a function or their importance, defined in this stage (Swain) and Hauska, 1977).
Conventional textural parameters, i.e. homogeneity (variance), directivity (gradient), co-occurrence matrix (Haralick et al, 1973), Walsh-Hadamard transform, are computed for each sample, either from images transformed by the abovementioned operators or from spectral channels from which the features are extracted.
In order to identify the parameters which separate all the confused classes, and the order in which they intervee, a divergence computation enabling interclass distance to be measured is implemented. For each class pari, a distance is computed by applying the following formula:
iÎ I (set of textural parameters) and j, kÎC (set classes);
tji, tki: textural parameter value i, for classes j, k;
timin, timax: minimum, textural parameter values i:
tjiÎTj (set of textural parameters of class j)
di (j,k,): distance between classes j, k for textural parameter i;
d: divergence between classes j, k;
Selected parameter i is that which is at maximum distance di
As an example, if class j at 30% from these points
in class k and at 10% in class 1 (percentage given by the confusion
matrix), the researched parameter will be that which provides optimum
separation of theses classes. However, two parameters may be required:
one same parameter does not necessarily separate the three classes. The
parameter having priority is that which corresponds to the greatest
divergence (gastellu-Etchegorry and Ducros-Gambart, 1987).
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