With the advent of aerial image datasets, dense stereo matching has gained tremendous progress. This work analyses dense stereo correspondence analysis on aerial images using different techniques. Traditional methods, optimization-based methods, and learning-based methods have been implemented and compared here for aerial images. For traditional methods, the architecture of Stereo SGBM is chosen while using different cost functions to get an understanding of their performance on aerial datasets. Analysis of most of the methods in standard datasets has shown good performance; however, in the case of the aerial datasets, not much benchmarking is available. Quantitative and qualitative analysis of different disparity estimation techniques has been carried out over the stereo aerial datasets. Using existing pre-trained models, recent learning-based architectures have also been tested on stereo pairs along with different cost functions in SGBM. The evaluation of obtained depth maps has been carried out using different quantitative metrics such as MSE, BMP, and SSIM. Through the analysis, the author summarizes the performances of different methods and provides a way forward for disparity estimation techniques in the future.