Aircraft contrail detection remains crucial for maintaining airspace safety and addressing the greenhouse effects caused by the aviation industry. Existing methods for detecting aircraft contrails primarily relied on the radiance or temperature differences between specific channels in multispectral images. But they did not fully exploit the potential of spectral features. The advancement of satellite-borne hyperspectral imaging technology has provided a new data foundation for aircraft contrail detection. However， methods that rely solely on either the spatial or spectral dimension of the image are unlikely to achieve satisfactory results in the task of aircraft contrail detection using satellite-based hyperspectral imagery. Therefore， a detection algorithm for potential aircraft contrails was explored using shortwave infrared hyperspectral images from the GF-5 AHSI. A spatial-spectral feature extraction method was proposed， which utilized the complementary nature of spatial and spectral information in hyperspectral images. The method achieved an accuracy of over 97% and a false alarm rate of less than 2% on GF-5 hyperspectral image data. It not only provides an innovative technical approach for aircraft contrail detection， but also offers valuable insights for future researchers and promotes further development of hyperspectral imaging in practical applications.