Hyperspectral imaging is an important source of land cover information by virtue of its spectral richness. However, this type of imagery is typically known by its coarse spatial resolution, that is a limiting factor for end-users. Although spectral unmixing techniques can provide subpixellic information by means of abundance fractions for each class in mixed pixels, the spatial distribution of these classes within each pixel is still unknown. Sub-pixel mapping techniques address the above mentioned problem. Nevertheless, the traditional sub-pixel mapping algorithms based on spatial dependence assumptions cannot solve these problems efficiently. Spatial regularization methods have recently been proposed in a way that they can treat each abundances map separately and do not consider spatial correlation between classes. In order to improve sub-pixel mapping accuracy and, consequently, enhance hyperspectral image classification, we propose a sub-pixel mapping method based on isotropic total variation minimization within and between pixels for different classes simultaneously. Experimental results with synthetic data sets show the attributes of using total variation as a prior model, which leads to improve sub-pixel mapping of different classes together.