Hippolyte Tapamo ; Adamou Mfopou ; Blaise Ngonmang ; Pierre Couteron ; Olivier Monga - Linear vs non-linear learning methods A comparative study for forest above ground biomass, estimation from texture analysis of satellite images

arima:1982 - Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées, September 15, 2014, Volume 18, 2014 - https://doi.org/10.46298/arima.1982
Linear vs non-linear learning methods A comparative study for forest above ground biomass, estimation from texture analysis of satellite images

Authors: Hippolyte Tapamo ; Adamou Mfopou ; Blaise Ngonmang ; Pierre Couteron ; Olivier Monga

The aboveground biomass estimation is an important question in the scope of Reducing Emission from Deforestation and Forest Degradation (REDD framework of the UNCCC). It is particularly challenging for tropical countries because of the scarcity of accurate ground forest inventory data and of the complexity of the forests. Satellite-borne remote sensing can help solve this problem considering the increasing availability of optical very high spatial resolution images that provide information on the forest structure via texture analysis of the canopy grain. For example, the FOTO (FOurier Texture Ordination) proved relevant for forest biomass prediction in several tropical regions. It uses PCA and linear regression and, in this paper, we suggest applying classification methods such as k-NN (k-nearest neighbors), SVM (support vector machines) and Random Forests to texture descriptors extracted from images via Fourier spectra. Experiments have been carried out on simulated images produced by the software DART (Discrete Anisotropic Radiative Transfer) in reference to information (3D stand mockups) from forests of DRC (Democratic Republic of Congo), CAR (Central African Republic) and Congo. On this basis, we show that some classification techniques may yield a gain in prediction accuracy of 18 to 20%


Volume: Volume 18, 2014
Published on: September 15, 2014
Submitted on: February 25, 2014
Keywords: Aboveground biomass, estimation, supervised learning, regression, support vector machines, random forests, k-nearest neighbor.,[INFO] Computer Science [cs],[MATH] Mathematics [math]


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