Combining The Spectral and Spatial Signature of Information Classes using Artificial Neural Network Based Classifier for Remote Sensing of Spatially Heterogeneous Land-Use/Land Cover System in the Tropics
Joel C.Bandibas
Research center, Cavite State University,
4122 Indang, Cavite, Philipines
Tel :046-4150020; Fax: 046-4150012
E-mail: bandibas@gpu.srv.ualberta.ca
Abstract
This study focused on the extraction of the spectral signature of satellite images with high spatial frequency using a revolutionary computing methodology known as the artificial neural network (ANN) computing. This method models how the neurons in the human brain process data to identify complex and noisy patterns of information. An error back-propagation artificial neural network structure to model the spatial and spectral signatures of information classes in digital images was successfully developed.
The developed computing methodology was successfully applied in the mapping of the land-use/land cover types of Aurora province, Philippines. Using the artificial neural network, the combined information class signatures was successfully used to classify the artificial the Landsat TM multispectral satellite image of the study area. For comparison purpose, the image was also classified using the established maximum likelihood classifier (MLC).The high intra-class variability and spectral response similarities between information classes contributed to the low classification accuracy using the MLC. This problem was solved when the classification methodology using the designed artificial neural network was applied. Result showed that the artificial neural network gave a significantly higher overall classification accuracy (86 %) compared to the accuracy obtained (76%) using the MLC.
Introduction
Satellite images in tropical countries are generally characterized by their high spatial frequency. This attribute generally results to lower classification accuracy when the images are classified using the conventional spectral signature based classifiers. Recent studies suggest that spatial signatures of information classes in satellite images can be a good discriminatory attribute that can potentially increase the satellite image classification accuracy. Consequently, combining the spectral and spatial signatures of information classes in high spatial frequency satellite images can be the best method in classifying digital images with the aforementioned attribute.
Modeling the spatial signature of information classes satellite images is the bottleneck in the development of spatial signature based classifiers. Developing an algorithm to extract the spatial signature such as geometrical shapes, sizes and arrangement of pixel that directly relate to an information class in satellite images is very interpretation which basically utilizes our brain's capacity to identify complex and noisy patterns of information. Just recently, a new computing method that Simulates the way the neurons in our brain process information was developed known as the artificial neural network (ANN) computing. This can be used to extract the spatial signatures of information classes in satellite images.
Recent developments in the field of ANN computing have provided potential alternatives to the traditional techniques of pattern recognition (Khotanzad and Lu, 1991). In fact, some studies found the use of ANN for digital image calcification promising. Ming et al.(1994) used an ANN (radial base function ) in classifying spot images while Shoshany and Guedalia (1994) used an ANN (Kohenen Feature Map) in mapping vegetation using aerial photographs. On the other hand, Homosura and pallewatta (1994) used the ANN in detecting land cover change. However, ANN based classifier which extracts the spectral and spatial signatures of information classes in satellite images has yet to be developed.
This study aims to develop an accurate image classification method using ANN computing, with emphasis on the developments of an error back-propagation artificial neural network structure to model the spatial and spectral signatures of information classes in digital images. The resulting neural network was successfully applied for the mapping of the land-use/land cover types of the Landsat TM image in a portion of the Aurora province in the Philippines.
