Decision fusion for classifying hyperspectral imagery with high spatial resolution

Abstract

Hyperspectral imaging is a relatively new technology in remote sensing. It deploys hundreds of spectral bands to collect image data for the same area. The high spectral resolution offers the potential of more accurate land-cover classification than when using instruments with rough spectral resolution, such as multispectral imaging sensors. However, the classification problem is challenging because of intrinsic intraclass variations (samples in the same class may have different spectral signatures). If a hyperspectral image also has high spatial resolution, the problem becomesmore serious since intraclass variations exist in both the spectral and spatial domains. As the accuracy of individual classifiers cannot be improved beyond given hard limits, many studies have been undertaken to develop and analyze combinations of results from different classifiers. The general aim is to obtain better results than can be derived from using classifiers individually, one at a time.1, 2 Unlike feature-level fusion, which extracts and combines features to improve performance, decision-level fusion combines the results from individual classifiers for final decision. Most decisionfusion approaches focus on supervised classifiers as base learners: all classifiers need training, so the results can only be as good as the training data. To avoid the possible negative influence from the limited quality of this data, we propose a method that combines supervised and unsupervised classifiers. In general, supervised feature recognition provides better classification than its unsupervised alternative. In addition to training-data limitations, using supervised classifiers may result in overclassification for some homogeneous areas. An unsupervised classifier, although possibly less powerful, generally recognizes such spectrally homogeneous areas fairly well. Thus, Figure 1. Test 1 image.