Multi-sensor data fusion and feature extraction for forest applications

Abstract

The purpose of this study was to extract, evaluate and select different multi-frequency polarimetric SAR and multi-spectral optical features to demonstrate the benefit of multi-sensor data fusion for forest applications. Multi-frequency fully Polarimetric SAR data at P-, L- and C-band and multi-spectral Landsat TM data acquired over the Nezer forest in France were used for demonstration. The scene is composed of homogeneous fields of either bare soil or maritime pine trees of different ages, and the application was discriminating the bare soil, and the trees in terms of their ages. A total of twenty-six features; six from each of the three Polarimetric SAR datasets and eight from the optical dataset were extracted. Significant classification accuracy improvement (up to 12%) was achieved by fusing SAR and optical datasets. Therefore, attention should be given to the combined use of them whenever they are available. Five features were found to jointly preserve 98.5% of the classification information of the available set. In addition to retaining most of the valuable information, these few identified features were found useful to interpret the scene in terms of the different forest scattering mechanisms. Therefore, they can be reasonably expected to be used for other forest applications too.