ADS-B signal plays a very important role in the area of communication in aviation, and it is of great significance for the detection and analysis of ADS-B signal in aviation communication. However, it is so much difficult to directly decode ADS-B signal with noise or interference. In order to better capture information contained in ADS-B signal, this paper proposed a method called EASTR. Firstly, ADS-B signal is de-noised by non-causal dilated convolutions and a residual network which are utilized to enhance the original noisy ADS-B signal to reduce noise. Then, the de-noised signal is transformed into Contour Stellar image and then a multi-level perceptron is leveraged for classification and recognition. In this paper, 5000 ADS-B signal data from different aircrafts are collected. With this dataset, EASTR is compared with multiple baselines under different SNRS and the experimental results show that EASTR outperform all baselines in all cases. The significance of signal enhancement module is also analyzed with ablation experiments.