Synergistic use of Landsat TM and SPOT5 imagery for object-based forest classification

Abstract

Abstract.  This study evaluated the synergistic use of Landsat5 TM and SPOT5 images for improving forest classification using an object-based image analysis approach. Three image segmentation schemes were examined: (1) segmentation based on both SPOT5 and Landsat5 TM; (2) segmentation based solely on SPOT5; and (3) segmentation based solely on Landsat5 TM. The optimal scale parameters based on TM/SPOT5 and SPOT5 were determined by measuring the topological similarity between segmented objects and reference objects at 10 different scales. Mean and standard deviation of the pixels within each object in each input layer were the classification metrics. Nearest neighbor classifier was performed for the three segmentation schemes. The results showed that (1) the optimal scales of TM/SPOT5, SPOT5, and TM were 70, 100, and 0.8, respectively and (2) classification results with medium spatial resolution images were not desirable, with overall accuracy of only 72.35%, while synergistic use of Landsat5 TM and SPOT5 greatly improved forest classification accuracy, with overall accuracy of 82.94%.