In recent years, it was a difficult task to classify a huge set of data due to the increasing population in urban places. As of now, satellite hyperspectral image provides information but this is not sufficient to classify data in urban areas. To develop the urban areas, accurate and timely information is necessary for the government. Hence, airborne hyperspectral data provides sufficient information for urban planning and disaster management. This paper, focuses on the following objectives: (i) To improve the classification accuracy in bigdata images (ii) To reduce the mixed pixels in residential buildings that are surrounded by small trees (iii) To bring down similar pixels of roads and parking lots. In this paper, 15 different classes were classified which are important for the growth in urban areas. The SVM classifier has more accuracy and better kappa coefficient compared with Neural Network (NN) and K-Means clustering. The Overall Accuracy (OA) has improved by 23.3.