With the increase in natural disasters, there is a need for better management and planning of post-disaster relief and rescue efforts to minimize loss of lives and property. An Unmanned Aerial Vehicle (UAV)-based system offers the advantage of mobility and a customized flight path that could be utilized to survey areas affected by a disaster. However, the images acquired by UAV must be analysed rapidly with minimum user intervention. In this context, the present work compares the performance of traditional handcrafted feature-based classifiers with that of deep learning methods for classifying images as flooded/non-flooded. The pixels corresponding to water in the UAV aerial image exhibit a characteristic texture as compared to roads, greenery etc. This motivated the use of handcrafted texture features (gray-level co-occurrence matrix (GLCM), local binary patterns (LBP)), which were then used to train a Support Vector Machine (SVM) classifier. Besides, Supervised (ResNet18) and Self-Supervised (Sim-CLR) deep learning methods are also studied for classifying UAV aerial images as flooded/non-flooded. The traditional and deep learning methods are compared on FloodNet dataset containing images acquired after hurricane Harvey. An F1 score of 0.84 for flooded class was obtained with the LBP texture classifier, compared to 0.87 using the self-supervised deep learning method. This result demonstrates that a hand-crafted texture-based classifier performs competitively with deep learning methods. Therefore, a traditional texture classifier could be preferred over deep learning methods for a rapid post-flood scene understanding in UAV aerial images.