The visible-shortwave infrared advanced hyperspectral imager (AHSI) loaded on the GF-5 satellite can acquire information about 330 spectral bands, which facilitates the derivation of land surface properties by deploying the hyperspectral observations of both atmosphere and land surface. However, cloud contamination in remotely sensed images often limits its application. To improve the availability of the GF-5 data, this study proposed a cloud detection approach that can be applied to various situations using the hyperspectral data proved by GF-5 AHSI. The apparent reflectance at the top of the atmosphere was firstly derived with the Level-1 product by the associated radiometric calibration coefficients and spectral response function. We found that the thick cloud pixels in the images can be effectively distinguished from other land cover types at the visible spectral region after the comparison of their apparent reflectance. The broadband apparent reflectance derived with the corresponding narrow bands was used to detect thick cloud pixels, which can eliminate the impact of noise associated with the narrow-band data. On this basis, the thick clouds can be screened out using simple detection thresholds. We then obtained the candidate thin cloud pixels using the cirrus cloud band. As thin cloud pixels were generally confused with high-albedo pixels, the distinction between these two features was studied by comparing the band ratios in various combinations. The thin cloud pixels were finally detected based on the optimal band combination and the corresponding threshold. Furthermore, we adopted the visual interpretation of cloud pixels to evaluate the performance of our algorithm using several GF-5 AHSI images. The cloud pixels can be well distinguished from clear sky pixels with an accuracy of over 91%, which indicates that our approach can be used to accurately detecting clouds for hyperspectral remote sensing images.