Hyperspectral remote sensing images are affected by different types of noise. In addition to typical random noise, nonperiodic partially deterministic disturbance patterns appear in the data. The strips usually found in images acquired by push-broom sensors, which are characterized by a high degree of spatial and spectral coherence. Many strips-reduction approaches such as histogram matching and moment matching have been developed. These methods assume that all sensor elements observe similar subscenes in a given image and adjust the distributions of values acquired by each sensor to some reference distribution by means of a histogram or moment matching, but this assumption usually is failure in many scenes which contain diverse materials. The formation of strips has close connection with the image formation process of push-broom imaging spectrometers. Many causes such as the uniformity of the pixels, the push-broom mode and the asymmetric width of thin slit at the entrance of imaging spectrometers can induce the strips in the images. Comparing with the dispersive spectrometers, interferometer spectrometers acquire the interference data, obtaining the spectrum by using the Fast Fourier Transformation (FFT). By analyzing the generating mechanism of strips in push-broom interferometer imaging spectrometers, we proposed an approach that corrects the strips using relative calibration factor directly computed from the acquired image. Once the relative calibration factor is determined, all the images acquired by the same imaging spectrometers can be corrected. So the methodology is an efficient one to reduce the strips. A formula is set up to describe the connection between gray values of pixels in images and relative calibration factor. The developed methodology is tested on data acquired by HJ-1A Hyperspectral Imaging Spectrometers, which is an interferometer spectrometer put into operation in 2008. The shortwave bands of HJ-1A HSI have severe strips. Results show excellent rejection of the noise with respect to the original HJ-1A HSI images, improving the removal in those scenes with diverse materials as well as being high efficient.