Monitoring the Earth using imaging spectrometers has necessitated more accurate analyses and new applications to remote sensing. A very high dimensional input space requires an exponentially large amount of data to adequately and reliably represent the classes in that space. On the other hand, with increase in the input dimensionality the hypothesis space grows exponentially, which makes the classification performance highly unreliable. Traditional classification algorithms Classification of hyperspectral images is challenging. New algorithms have to be developed for hyperspectral
data classification. The Spectral Angle Mapper (SAM) is a physically-based spectral classification that uses an ndimensional angle to match pixels to reference spectra. The algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra, treating them as vectors in a space with dimensionality equal to the number of bands. The key and difficulty is that we should artificial defining the threshold of SAM. The classification precision depends on the rationality of the threshold of SAM. In order to resolve this problem, this paper proposes a new
automatic classification model of remote sensing image using SAM combined with decision tree. It can automatic choose the appropriate threshold of SAM and improve the classify precision of SAM base on the analyze of field spectrum. The test area located in Heqing Yunnan was imaged by EO_1 Hyperion imaging spectrometer using 224
bands in visual and near infrared. The area included limestone areas, rock fields, soil and forests. The area was classified into four different vegetation and soil types. The results show that this method choose the appropriate threshold of SAM and eliminates the disturbance and influence of unwanted objects effectively, so as to improve the classification precision. Compared with the likelihood classification by field survey data, the classification precision of this model heightens 9.9%.
Satellite-based hyper-spectral imaging became a reality in November 2000 with the successful launch and operation of the Hyperion system on board the EO-1 platform. Hyperion is a pushbroom imager with 220 spectral bands in the 400-2500 nm wavelength range, a 30-meter pixel size and a 7.5 km swath. The objective of this research is to use the Hyperion image for deriving the Rocks/Minerals Information. This paper introduces a complete processing flow from raw Hyperion image to geological theme information map, including radiance calibration and correction, atmospheric correction and geometrical correction, feature extraction and Selection, and spectral mapping by multi-method on the base of referring to the USGS spectral library and ground radiometric measurement data. The study explored the utility of Hyperion data in alteration mineral mapping. Two Hyperion images of the BeiYa in the northwest of YunNan was acquired and evaluated for alteration zone mapping. The results show that the alteration zones in the study area can be identified from Hyperion data very efficiently. The mineralogical and lithologic information extracted from Hyperion data is largely consistent with the geological map and previous research results.
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