In the face of the increasingly abundant airborne and spaceborne hyperspectral sensors and the accompanying increase in hyperspectral data, the problems of excessive data volume and band redundancy have always been the major difficulties in hyperspectral image processing and interpretation. At the same time, the use of hyperspectral remote sensing technology to reveal camouflaged targets has always been the key point of modern remote sensing application technology research. In detecting massive spectral data of ground objects and redundant spectral information, the design of appropriate data dimensionality reduction technology plays a vital role. The band selection method among the main methods of dimensionality reduction processing can not only reduce the spectral information of the image data without distortion but also accurately distinguish the camouflaged target and its background based on it. Today, hyperspectral technology is an important technical means for military applications, and it is also a research hotspot for many scholars at home and abroad.It is a commonly used research method to use various indicators to calculate the different performances between the bands and to select the most representative bands according to their parameters for feature identification or classification to test the pros and cons of the method. However, few experimental studies still exist on specific band selection methods for special features, such as vegetation camouflage targets. In the study, green steel plates, green camouflage nets, and green fake turf were selected and placed in a background environment containing healthy green vegetation, wet bare ground, and dry, bare ground. Band selection and classification experiments were carried out by simulating camouflage targets and background objects in the real environment. First, analyze the spectral curve, and select a significant feature band. Secondly, the band screening is performed based on the sub-space divided according to the phase relationship between the band. The visual model is then established according to the image brightness of the property target. Finally obtains a band selection collection with relative in dependence and optimal recognition. Next, using support vector machine classifier and Mahalanobis distance classifier, the proposed algorithm is compared with two common algorithms in band selection results and full band combination for classification experiments. The experiment shows that the band selection results of the proposed method are better than those of the common algorithms in band selection results and full band, and the classification accuracy and speed are improved. Among them, compared to using full band classification, the overall classification accuracy of the two types of classifiers has been improved by 4.559 2% and 2.364 8%, respectively. The Kappa coefficient has been improved by 0.059 4 and 0.031 2, and the classification time has been reduced by 6.83 seconds. Experiments show that this method can effectively classify vegetation camouflage targets and background objects and has great practical application value.