Mulit-Spectral/Textural supervised classification - Land Cover Mapping with SPOT in Indonesia
Gastellu-Etchegorry
J.P.SCOT Conseil 18, Avenue Edouard Belin
31055-Toulouse Cedex France
Ducros-Gambart
D.C.E.S.R Paul Sabatier University
21029-Toulouse Cedex France
Abstract:
The capability of SPOT combined with a specifically designed classifier was investigated for computer assisted land cover/use mapping in Indonesia. Atmospheric conditions and the small size, complexity and dynamic nature of local agro-forest systems both confuse spectral analyses. In this context, conventional classifiers are inadequate. This led to the development of an original supervised classification method for discriminating between the large numbers of classes (or subclasses) that are apparent in high-resolution satellite images. Several multi-spectral/temporal classifications are initially processed with various combinations of multi-spectral/temporal channels. Then, results are fusioned with a class priority system but information about spectral confusions is preserved. These confusions are further solved by applying texture features to classes that are confused. These confusions are further solved by applying texture features to classes that are confused. The selection of these features, occasionally correlated, is difficult and subtle. A technique was developed for selecting the optimum feature for each class. But textural confusions appear, e. g in heterogeneous and interface zones; they are solved by a Specifically designed process. The final result is improved confusion matrices enable class improvement to be identified, which is considered as being from 10 to 50% depending on the particular class.
Introduction
Both the small size, complexity and dynamic nature of Indonesian agro-forest systems (Malingreau and Christiani, 1981), especially in Java, create problems for inventorying and monitoring. Studies have already been conducted by the authors for determining spectral and spatial characteristics of these systems (Gastellu-Etchegorry and Ducros-Gambart, 1989).
Two methods were used by the authors for assessing the atmospheric influence foe determining spectral characteristics; i.e. the dark ground feature calibration method (Sabins, 1978) and a method derived from piech and Wlaker (1974), based on the statistical analysis of landscape units which are partly in shadow. Several SPOT scenes of Central Java were considered. Results concerning three study areas of SPOT scene (193, 365) are listed in table 1. Two major points must be emphasized:
- Atmospheric upwelling radiances have very large
values. Compared to the total measured radiance, they represent between
30% and 80% for channel XS1, between 20% and 70% for channel XS2 and
between 15% and 45% for channels XS3.
- Atmospheric radiances are characterized by an spatial important heterogeneity; which means that identical Earth features within the same SPOT image may have quite different spectral characteristics.
Table 1:
(a)atmospheric influence
| Study areas |
SPOT scene |
XS1 |
XS2 |
XS3 |
| Study area 1 |
( 293, 362) |
52 |
37 |
34 |
| Study area 2 |
43 |
31 |
32 |
| Study area 3 |
35 |
27 |
23 |
(b)Mean radiometric values of Earth feature
| |
XS1 |
XS2 |
XS3 |
Mean radiometric values
Within the SPOT scen |
63 |
58 |
68 |
Another major limitation for determining spectral characteristics of land cover units is due to the fact that there is no direct relationship between lands covers unit and spectral calluses. For example, a land cover unit corresponds to several spectral (Sub) classes, whereas a spectral (sub) class may correspond to several land cover units. This is particularly disturbing for classification processes. This aspect is of special importance with high-resolution satellites. Indeed, the number of land cover units which can be discriminated in a multispectral or multitemporal image is undoubtedly greater than with low-resolution satellites. Indeed, the number of land cover units which can be discriminated in a multispectral or multitemporal image is undoubtedly greater than with low-resolution satellites. Numerous tests have revealed the possibility of identifying twenty to thirty spectral classes for this type of
Image (Gastellu-Etchegorry and Ducros-Gambart, 1989) In reality, these classes correspond to land cover sub-units; e. g. the forest class can be broken down into subclasses of varying degrees of density.
