Abstract: Interferometric hyperspectral image is a special kind of image source, which contains massive data and is difficult to transmit on a limited bandwidth channel. The traditional method is to compress the data and then encode the transmission. However, the compressed data is still very large, which brings great difficulties to the transmission and storage of data. Nevertheless, the compressed sensing technology can solve this problem well. Based on the original algorithm of compressed sensing, this paper proposes an adaptive threshold-based orthogonal matching pursuit algorithm (ATROMP) which is more suitable for interfering hyperspectral images. The algorithm first uses block processing and then selects the interference fringes. Because the vertical interference fringes have strong unidirectional characteristics, the total variation of the level is larger.Therefore, the interference fringes in the images are extracted from the horizontal total variation values for adaptive sampling. Then, an adaptive threshold is used in this paper to replace the quadratic selection in the ROMP algorithm. Using an adaptive threshold can not only ensure that the atomicity of each selected atom is sufficiently high, but also that multiple atoms can be properly selected each time to ensure sufficient number of cycles, to avoid the follow-up higher degree of atom missing. Compared with the traditional ROMP algorithm, a large amount of experimental data show that the sparse reconstruction ac- curacy of the method can be significantly improved.