Extraction Of Forest Types Based On Hyperion Data

Forest resources is the world's largest land ecological system, the important component of the national natural resources, the forestry construction foundation, the necessary basis of human survival and development. Developing forest resources survey to understand and grasp the forest resources situation and change information to improve the scientific and reasonable decision-making level of forestry development, management of forest resources has great significance. Compare with the traditional forest resources survey, RS image interpretation for its macroscopic, short cycle, dynamic and other advantages, is widely used in the investigation of the forest resources monitoring.Along with the development of remote sensing technology, we are able to get and use more and more information, hyperspectral remote sensing through the remote sensing method for more information is the inevitable development trend, as well as the frontier and hotspots. Hyperspectral remote sensing image combined imaging technology and subdividing spectrum technique in one, it make hyperspectral images in the application of classification technology has huge potential, and in imaging process, hyperspectral images can obtain the continuous spectrum of the features of information, and the traditional spectrum than remote sensing data, hyperspectral data is better to recognition and classification of all kinds of features, but the huge amount of data and data of high dimension make the data transmission and storage is limited, also give hyperspectral data processing method discussion bring quite a challenge.This paper applied Hyperion hyperspectral data, to the hunan zhuzhou YouXian HuangFeng bridge as the forest classification research area. For the bands of Hyperion data and the characteristics of large volumes of data, execute several data processes, uncalibration and suffer lunt images bands weed out, the conversion of DN value and the absolute value, bad line repair, calibration of Smile effect, FLAASH atmosphere effect correction, the remote sensing data pretreatment geometry correction:using mothod combinated with feature selection and feature extraction for hyperspectral data to reduction dimension, process will let data from a high dimensional space mapping to a low dimensional space, then respectively applied maximum likelihood and spectral Angle mapping method for the forest types of recognition and analysis. The main research conclusions are as follows:(1) After the band removed, conversion and correction processing of Hyperion data, hyperspectral data quantity is small, surface features curves tend to be more real vegetation spectral features, making the surface features easier to distinguish.(2) The correlation matrix shows the segmented principal component analysis of Hyperion data, the division of data sources, each the average correlation coefficient above0.9, that Hyperion data adjacent to the high correlation between bands.(3) Use the segmented principal component analysis and band index combining dimensionality reduction method, Hyperion data the original band of242is reduced to the large amount of information and the correlation weak13bands, on the one hand, segmented principal component analysis effectively inhibitglobal transformation leading to the possibility of local important spectral filter, on the other hand, the band index during band selection, taking into account the adaptive segmentation between paragraphs and subparagraphs in the band after the correlation between the effective lowerthe dimension of the hyperspectral data.(4) Sub-space division, before the band selection can remove larger band, and can reduce the amount of data calculation, so as to achieve high-dimensional remote sensing data to optimize the processing and efficient use of purpose.(5) Band index to select a subset of the band, select band maxima of the index rather than the maximum value as the drop the Wei Houbo piece set to compensate for the error of the original data in the segment.(6) Hyperion data in two ways based on the feature space maximum likelihood and spectral angle based on the spectral space mapping method for identification of forest types, classification error matrix:the maximum likelihood classification of the overall accuracy of85.23%, the spectral anglefill in the diagram method, the overall accuracy of88.94%for the high spectral, spectral angle mapping method targeted to identify superior to the traditional multi-spectral classification of various types of spectra.