15 October 2015 Accurate multi-source forest species mapping using the multiple spectral–spatial classification approach
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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 hyper-spectral (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.
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Dimitris Stavrakoudis, Dimitris Stavrakoudis, Ioannis Gitas, Ioannis Gitas, Christos Karydas, Christos Karydas, Polychronis Kolokoussis, Polychronis Kolokoussis, Vassilia Karathanassi, Vassilia Karathanassi, } "Accurate multi-source forest species mapping using the multiple spectral–spatial classification approach", Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 964324 (15 October 2015); doi: 10.1117/12.2194663; https://doi.org/10.1117/12.2194663

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