The Classification Technology Research Based On Hyperspectral Image

Hyperspectral remote sensing is a frontier field in the development of remotesensing technology, and it obtains useful information from interesting objects by usingnarrow bands of electromagnetic waves. Hyperspectral remote sensing image is a majorbreakthrough in the field of remote sensing, and spectral resolution was greatlyimproved while retaining a high spatial resolution. As a result, it can be used to detectthe land-cover types which could not be detected with traditional panchromatic andmulti-spectral images. Compared with the traditional multi-spectral remote sensingimage, hyperspectral image (HSI) provides a large amount of information and highspectral resolution, and the ability in description and analysis of land-cover types hasbeen improved greatly, which makes it possible to process and analyze the spectralinformation of land-cover types more precisely. In many countries, hyperspectral remotesensing play an important role in the earth observation system, and it has become a newforce to the observation of earth’s land, ocean and atmosphere. Due to the highdimension of HSI, traditional classification methods have some limitations whendealing with HSI. How to accurately exploit the information from a large amount ofhyperspectral data for achieving high-precision classification, is still a serious problem.Based on the characteristics of HSI, this paper focuses on the research of HSIclassification. The main research contents are as follows:①After the preprocessing of HSI, atmospheric correction was carried out on HSI.The quadratic polynomial mode was selected for geometric correction, and the nearestneighbor interpolation method was used for resampling. Then the requirements foraccuracy are fully guaranteed, which lays a solid foundation for classification.②A RBF neural network method based on adaptive particle swarm optimization(PSO) was proposed for HSI classification. The neural network has the merits ofparallel processing, fuzzy recognition, and nonlinear mopping, which gives the benefitto the classification of HSI. However, it is very difficult to choose proper parameters.An adaptive PSO algorithm is used to optimize the parameters, and the RBF neuralnetwork model based on PSO is constructed for classification. Experimental resultsshow that the RBF neural network based on PSO has a better classification accuracy forHSI classification.③The support vector regression (SVR) with adaptive PSO is proposed for HSI classification. At first, the construction of kernel functions and the optimization ofmodel parameters are introduced. For the conditions of small number of data samplesand the complexity of model parameters, the CV estimation model is used. The adaptivePSO method is used to choose the parameters for the model of SVR in hyperspectralremote sensing classification, which is useful to solve the problem of insufficienttraining samples in HSI.④According to the sparse representation theory, a new method based on adaptivesparse representation (ASP) is proposed for HSI classification. In this method, thedictionary is constructed with training samples. The remainder of each iteration isclustered, and the center of clustering is considered as the atom of the new dictionary.Furthermore, the testing samples are regarded as the linear combination of trainingsamples in the redundant dictionary, which makes the dictionary adapt to sparserepresentation of samples more effectively. The validity of ASP is verified by theexperiments for HSI classification. The effectiveness of the proposed ASP method isverified on hyperspectral image data sets.