Unsupervised classification of remote sensing imagery using multi-sensor data fusion

Abstract

Remotely sensed imagery accounts for sensor specific information. The following paper deals with making use of data from multiple sources with similar temporal resolution to improve classification accuracy. This was done by clustering five masks or samples of 100 × 100 pixels selected randomly from multispectral data from Landsat TM and evaluation of cluster quality to find the number of naturally occurring clusters. This was followed by clustering the entire study area Landsat TM data using k-means algorithm and evaluation of the resulting cluster quality using silhouette coefficient to identify loosely classified pixels and mean silhouette value (threshold of the scene). Hyper-spectral data from Hyperion was used for only the loosely classified pixels identified above and was clustered using the k-means algorithm. Finally, soft decision level fusion method was applied to the clustering output from HS data with good quality clusters (clusters with silhouette coefficient above the mean) from the multi-spectral imagery to produce final classification maps. In the fused imagery, the overall Classification accuracy and Kappa Statistics increased significantly as compared to the multispectral imagery. Cluster validity indices like Silhouette coefficient is used to evaluate cluster quality and predict naturally occurring clusters. The decision level fusion of selective data from multiple sources has exhibited better classification results at reduced computational overheads.