This paper chose the typical salinization area in Kenli County of the Yellow River Delta as the study area, selected HJ-1A satellite HSI image at March 15, 2011 and TM image at March 22, 2011 as source of information, and pre-processed these data by image cropping, geometric correction and atmospheric correction. Spectral characteristics of main land use types including different degree of salinization lands, water and shoals were analyzed to find distinct bands for information extraction. Land use information extraction model was built by adopting the quantitative and qualitative rules combining the spectral characteristics and the content of soil salinity. Land salinization information was extracted via image classification using decision tree method. The remote sensing image interpretation accuracy was verified by land salinization degree, which was determined through soil salinity chemical analysis of soil sampling points. In addition, classification accuracy between the hyperspectral and multi-spectral images were analyzed and compared. The results showed that the overall image classification accuracy of HSI was 96.43%, Kappa coefficient was 95.59%; while the overall image classification accuracy of TM was 89.17%, Kappa coefficient was 86.74%. Therefore, compared to multi-spectral TM data, the hyperspectral imagery could be more accurate and efficient for land salinization information extraction. Also, the classification map showed that the soil salinity distinction degree of hyperspectral image was higher than that of multi-spectral image. This study explored the land salinization information extraction techniques from hyperspectral imagery, extracted the spatial distribution and area ratio information of different degree of salinization land, and provided decision-making basis for the scientific utilization and management of coastal salinization land resources in the Yellow River Delta.