Detection and mapping crack patterns are key issues for structural assessment of concrete structures. The use of image processing for identification of pathologies has undergone major developments, since it is a noninvasive technique providing the precision and reliability required for the task. The authors have developed a method, named SurfCrete, to materials and damages classification on concrete structures, including mapping cracks. This is based on analysis of multi-spectral images, including visible and near infra-red (NIR) regions of the electromagnetic spectrum. Latest improvements include the use of hyperspectral image analysis for crack detection, based on image clustering. The drawbacks of the developed methods are the difficulties usually shown when dealing with surfaces presenting several damages and materials besides cracks, namely due to the presence of biological colonization, repairing mortars, delamination and efflorescence, among other anomalies commonly found on concrete structures. Furthermore, when surfaces are subjected to different light conditions, this also influences the accurate classification of cracks. In this paper, an evolution of the method previously developed, herein named SurfCrete-HSV, is presented. The new method is completely focused on classifying biological colonization based on the classification of HSV false colour images, being therefore more robust and reliable. These HSV images are built from hyperspectral images (wavelengths from 450 nm to 950 nm and 25 nm of bandwidth) by selecting three channels, one from NIR region and two from the visible region of electromagnetic spectrum. The HSV space allows isolating the colour in a single data dimension to enable a brightness free clustering. An image of a concrete specimen with simulation of biological colonization over a smooth surface is used from a database of hyperspectral images, to evaluate SurfCrete-HSV method. Results show that the SurfCrete-HSV method is reliable for detection of biological colonization on concrete surfaces. The best set of channels to use results from combining one from Near Infra-Red with Red and Blue regions of the electromagnetic spectrum, which reveals high accuracy values with acceptable recall.