Influence of image fusion approaches on classification accuracy: a case study

While many studies in the field of image fusion of remotely sensed data aim towards deriving new algorithms for visual enhancement, there is little research on the influence of image fusion on other applications. One major application in earth science is land cover mapping. The concept of sensors with multiple spatial resolutions provides a potential for image fusion. It minimises errors of geometric alignment and atmospheric or temporal changes.

This study focuses on the influence of image fusion on spectral classification algorithms and their accuracy. A Landsat 7 ETM+ image was used, where six multispectral bands (30 m) were fused with the corresponding 15 m panchromatic channel. The fusion methods comprise rather common techniques like Brovey, hue‐saturation‐value transform, and principal component analysis, and more complex approaches, including adaptive image fusion, multisensor multiresolution image fusion technique, and wavelet transformation. Image classification was performed with supervised methods, e.g. maximum likelihood classifier, object‐based classification, and support vector machines. The classification was assessed with test samples, a clump analysis, and techniques accounting for classification errors along land cover boundaries. It was found that the adaptive image fusion approach shows best results with low noise content. It resulted in a major improvement when compared with the reference, especially along object edges. Acceptable results were achieved by wavelet, multisensor multiresolution image fusion, and principal component analysis. Brovey and hue‐saturation‐value image fusion performed poorly and cannot be recommended for classification of fused imagery.