Hyperspectral image refers to a three-dimensional data cube image containing both spatial shape information and spectral reflectance information of ground objects. In recent years, with the rapid development of land-based platforms and near ground flight carriers, the production and application of land-based hyperspectral imaging equipment are gradually mature. Compared with hyperspectral images under remote sensing imaging conditions, hyperspectral images under land-based imaging conditions have higher spatial resolution and temporal resolution, saving a lot of time and use costs. The land-based hyperspectral imaging technology provides a solution to the problem of "similar shape is the same object" existing in the current two-dimensional space target detection. Therefore, a method of target location and recognition based on joint spatial and spectral information is proposed in this paper. This paper first introduces the basic principles of space target detection and spectral target detection, then establishes the spatial dataset of land-based hyperspectral images, and carries out experiments to verify the proposed methods. Use the ground imaging spectrometer to obtain the hyperspectral image data of the area to be measured, and then use the spatial target detection model to frame the specific target; The similarity test between the specific target set out in the frame and the prior spectral information shows that the matching degree between the pixel in the target area to be measured and the target spectrum is 88.48%, and the matching degree between the pixel in the non target area and the target spectrum is only 3.12%, thus successfully completing the target positioning and recognition task. The experiment shows that the method of specific target location and recognition based on spatial and spectral information can solve the problems of simple spectral dimension detection and simple spatial dimension detection at the same time, which is of great significance in specific target location, identification of true and false targets, and provides a new idea for specific target location and recognition in the future.
The spectral information data of ground objects refers to the relationship between spectral reflectance and wavelength. At present, the field imaging spectrometer is mainly used to obtain the image and spectral information of objects at the same time. However, the spectral reflectance of the same object in different directions is different, which seriously affects the accuracy of subsequent classification and target detection based on spectral data. In order to solve this problem, a method of spectral data expansion of ground objects based on semi empirical kernel driven model is proposed in this paper. A small amount of spectral data of ground objects under the condition of known directions are substituted into the model, and the spectral data under the condition of other arbitrary directions are inverted, which not only reduces the cost of sample collection, but also expands the spectral data of ground objects. Experiments prove the effectiveness of this spectral data expansion method and use the expanded spectral data as a priori sample for ground object classification. Compared with the classification method based on a small number of original spectral samples, the experiments show that this method can effectively improve the accuracy of ground object classification.
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