The analysis and interpretation of satellite images generally require the realization of a classification step. For this purpose, many methods over the year have been developed with good performances. But with the explosion of VHR images availability, these methods became more difficult to use. Recently, deep neural networks emerged as a method to address the VHR images classification which is a key point in remote sensing field. This work aims to evaluate the performance of fine-tuning pretrained convolutional neural networks (CNNs) on the classification of VHR imagery. The results are promising since they show better accuracy comparing to that of CNNs as features extractor.