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- W2766336871 abstract "Classification is an important and widely used technique for remotely sensed hyperspectral data interpretation. Although most techniques developed for classification assume that the spectral signatures provided by an imaging spectrometer can be interpreted as a unique and continuous signal, in practice this signal may be obtained after the combined individual responses from several different spectrometers. For instance, the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system is in fact formed by four different spectrometers, covering the nominal spectral ranges of 400–700 nm, 700–1300 nm, 1300–1900 nm, and 1900–2500 nm, respectively. In this work, we propose a new classification strategy which takes into account the physical design of the imaging spectrometer system for partitioning the spectral bands collected by each spectrometer, and resampling them into different groups or partitions. The final classification result is obtained as a combination of the results obtained from each individual partition. This concept is illustrated in this work using AVIRIS data, and our experimental results indicate that the proposed strategy provides advantages in terms of classification accuracy, in particular, when very limited training samples are available." @default.
- W2766336871 created "2017-11-10" @default.
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- W2766336871 date "2014-06-01" @default.
- W2766336871 modified "2023-09-27" @default.
- W2766336871 title "Spectrometer-driven spectral partitioning for hyperspectral image classification" @default.
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- W2766336871 doi "https://doi.org/10.1109/whispers.2014.8077558" @default.
- W2766336871 hasPublicationYear "2014" @default.
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