Nowadays, remote sensing is an essential science in French Polynesia because of its extended territory and the remoteness of its 120 islands. There is a strong need to study the vegetation cover and its evolution (biodiversity threat, invasive species, etc.). A growing satellite images database has been acquired throughout, giving access to very high resolution optical images such as Quickbird data. These data allow accessing the vegetation canopy spectral and contextual information, texture classification has proved to be an efficient tool to map the complex vegetation found in tropical regions. The main goal of this paper is to propose an optimized SVM multispectral-texture classification method for tropical vegetation mapping. One of the texture computation drawbacks is the window treatment size, which is related to the largest texture element size. In complex tropical vegetation cover, this parameter leads to very small ground truth learning database, inducing a significant degradation of the classifications accuracy. We propose to increase the thumbnail numbers using an under-sampling method, optimizing the size and the number of the thumbnails. The other drawback is the high dimensionality of the problem when dealing with multispectral textures. We thus propose to rank and select the most pertinent textures attributes in order to reduce the dimensionality without reducing the classification accuracy. We first introduce the study context, before exposing preliminary studies on tuning the SVM learning method. The adapted method is then accurately exposed and the interesting experimental results as well as a sample of applications are presented before to conclude.