【Objective】Hyperspectral remote sensing is advantageous for fine identification of features, but has the problem of large data volume, high correlations among bands, and low processing accuracy and efficiency. In remote sensing classification, it is not the case that the use of a greater number of narrow bands ensures better classification perfor-mance. Therefore, the primary objective of the current work was to determine an appropriate bandwidth of Hyperion images by exploring the reduction of hyperspectral data, from the perspective of spectra.【Method】In this study, the preprocessing procedures for Hyperion images principally included the removal of uncalibrated and water vapor affected bands, eliminating the bad lines, stripes and smile effects, radiometric calibration and atmospheric correction. After this processing, 161 bands were retained to implement further geometric correction. Fourteen land use classes or schemes in the experimental area were selected according to field sample plot inventories, and a 7-scale Sym 3 wavelet decomposition was carried out on the average spectra of the 14 land use types. The spectral bands suitable for each type of recognition were mainly concentrated around 40 and 80 nm because of the variance and entropy of the wavelet detail coefficients. The narrow bands identified in the different spectral intervals were fused using the wavelet function to classify, by implementation, the support vector machines(SVM)algorithm.【Result】It was concluded that the appropriate spectral resolution to identify nine types of ground objects, such as Pinus syluestriformis forest, Larix gmelinii forest, Pinus sylvestris forest, mixed conifers forest, mixed broadleaf forest, fire burned area, water, arable land, and unused land, is 40 nm and the suitable spectral resolution for the remaining five kinds of terrain recognitions is 80 nm. The number of bands in different spectral intervals was greatly reduced. Further, the final classification accuracy was over 81%. 【Conclusion】The combination of wavelet analysis and SVM was helpful to solve the “dimensional disaster” issue with hyperspectral data, which indicates that the proposed method is feasible.
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