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Forestry / Vegetation
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Classification of Multi-sensor Data using a combination of Image Analysis Techniques
Gerrit Huunemna, Lucas Broekema
International Institute for Aerospace Survey and Earth Sciences (ITC)
Department of Geoinformatics
P.O. Box 6, 7500 AA Enschede, The Netherlands
Tel. +31-53-4874489, Fax. +31-53-4874335
E-mail: HUURNEMAN@ITC.NL
Abstract
Most image analysis techniques have both strong and weak aspects. Training a complex neural network of image classification, for example, is a time-consuming process. Using a conventional classifier (e.g. maximum likelihood) in combination with a neural network classifier can reduce processing time while, at the same time, reinforcing the performances of both classifiers..
This paper deals with the classification of an area where the land use is mainly agriculture, with optical and microwave data as input. The optical data is acquired by the SPOT satellite in multi-spectral mode and the microwave data is acquired by the ERS-1 and ERS-2 satellites in single look complex mode. in the first step of the classification, the maximum likelihood classifier is used; the classes that are classified satisfactorily are masked out and the remaining data is classified within a reduced neural network. the results of the classifications of several data combinations are monitored. The influence of the mode in which the microwave data is used in these combinations is especially highlighted (intensity images versus coherence maps).
The coherence map is created based on the correlation of two complex SAR data sets. Indication the level of (de) correlation, this map gives information about the relation between groundcover and temporal changes. For classes that cannot be separated with optical data alone an investigation is made into the use of such (de) correlation as an additional layer.
Introduction
With the launch of every new sensor system for the acquisition of data in the optical and /or the microwave part of the spectrum, ad the development of new sophisticated processing and analyzing techniques lead very often to an exponential increase in processing time. For the recognition and classification of several objects, e.g. a number of crop types. On data source and a powerful classifier such as the maximum likelihood, give acceptable results; therefore, the use of complex algorithms and multi-source datasets is not required.
Our research is aimed at optimal use of : neural network classifier in combination with a maximum likelihood classifier. In the firs pass of the classifier. In the first pass of the classification, the can be classified with acceptable accuracy and reliability by means of the maximum likelihood by means of the maximum likelihood classifier will be excluded from the second pass. The selection of the classes that are thus successfully classified is controlled by means of a threshold. The value of the threshold will be application dependent. In the second pass a neural network classifier is activated for all classes that were not satisfactorily treated in the first pass.
The data sources that are used in the classification are a SPOT-XS and a tandem pair of ERS-1 and ERS-2 SAR data. From the two radar images, which are in the Single Look Complex (SLC) format, a coherence map is created. This map reflects locally the amount of correlation between the two amount of correlation between the two images. The two radar datasets are acquired with a time difference of 24 hours (tandem mode). However, the same object or distributed target on these two radar images will cause a different pattern of intensities due to the appearance of multiplicative noise (speckle). A comparison of the difference of these two images will, therefore, show a large variance. The speckle is the result of the variation in the overall phase and the overall magnitude of cell with equal cover type.
It the two images would be taken from the same position and at the same moment, then the two images would be identical. Differences appear due to changes over time and/or the difference position of the sensors. The time dependent changes are more or less characteristic for the type of land cover and are, as such, a worthwhile feature in the classification process.
2. Data Description
The images were taken of an area in the central part of The Netherlands. It consists of a relatively new polder with large agricultural fields, a wetland put under agricultural fields, a wetland put under agriculture with some Redlands remaining; furthermore an are with small agricultural fields on the "old" land and parcels covered with forest. The old and the new land are separated by a take. In this area there are no relief differences; it is very flat terrain.
In order to classify the images samples supposed to be representatives for the various types of land cover were taken, and, at the same time, another independent set of samples was acquired to check the accuracy and reliability of the classifications. Field data was acquired of the images.
The eight classes that are selected did not change between the acquisition data and the field check.
The following classes were sampled :
- Bare soil (BS),
- Sugar bet (SB),
- Stubble (S),
- Forest (F),
- Maize (M)
- Grass (G),
- Water (W),
- Reed (R).
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