The invention discloses a spectral imaging method based on self-adaptive coupling observation and non-linear compressed learning to mainly solve the problem that the prior art cannot ensure that matrix observation is not related to a dictionary obtained by learning. The method is realized by the following steps: 1, projecting signals in an original space to a feature space; 2, conducting dictionary learning in the feature space through a KPCA method and solving a sparse dictionary; 3, conducting random initialization on an observation matrix, conducting iteration training on a perception matrix formed by a product of the sparse dictionary and the initialized observation matrix, and obtaining an observation matrix that has undergone coupling optimization; 4, realizing non-linear compressed perception spectral imaging through a nucleus compressed perception method; and 5, restoring original signals through a pre-image method. The result of an experiment shows that in a condition of different sampling rates, the spectral imaging method, compared with a conventional method taking a Gaussian random matrix as an observation matrix, has a great reconstitution effect and can be used for obtaining low-cost and high-quality high spectral images.