| abstract |
The invention discloses a semantic hyperspectral image classification method based on local and non-local multi-features. It mainly solves the problems of low accuracy rate, poor robustness and weak spatial consistency in hyperspectral image classification. The steps include: inputting an image, extracting various features of the image; dividing the data set into a training set and a test set; probabilistic support vector machine mapping various features of all samples into corresponding semantic representations; constructing local and non-local neighbor sets; Construct a denoising Markov field model, perform semantic fusion and denoising processing; iteratively optimize the semantic representation; use the semantic representation to obtain the categories of all samples, and complete the accurate classification of hyperspectral images. The present invention adopts multi-feature fusion, and fully excavates and utilizes the spatial information existing in the image. In the case of small samples, it obtains very high classification accuracy, and has good robustness and spatial consistency. It is used for Military detection, map drawing, vegetation survey, mineral detection, etc. |