Accurate Multi-Source Forest Species Mapping Using The Multiple Spectral-Spatial Classification Approach

This paper proposes an efficient methodology for combining multiple remotely sensed imagery, in order to increase the classification accuracy in complex forest species mapping tasks. The proposed scheme follows a decision fusion approach, whereby each image is first classified separately by means of a pixel-wise Fuzzy-Output Support Vector Machine (FO-SVM) classifier. Subsequently, the multiple results are fused according to the so-called multiple spectral-spatial classifier using the minimum spanning forest (MSSC-MSF) approach, which constitutes an effective post-regularization procedure for enhancing the result of a single pixel-based classification. For this purpose, the original MSSC-MSF has been extended in order to handle multiple classifications. In particular, the fuzzy outputs of the pixel-based classifiers are stacked and used to grow the MSF, whereas the markers are also determined considering both classifications. The proposed methodology has been tested on a challenging forest species mapping task in northern Greece, considering a multispectral (GeoEye) and a hyperspectral (CASI) image. The pixel-wise classifications resulted in overall accuracies (OA) of 68.71% for the GeoEye and 77.95% for the CASI images, respectively. Both of them are characterized by high levels of speckle noise. Applying the proposed multi-source MSSC-MSF fusion, the OA climbs to 90.86%, which is attributed both to the ability of MSSC-MSF to tackle the salt-and-pepper effect, as well as the fact that the fusion approach exploits the relative advantages of both information sources.