Texture Based Hyperspectral Image Classification
نویسندگان
چکیده
منابع مشابه
Texture Based Hyperspectral Image Classification
This research work presents a supervised classification framework for hyperspectral data that takes into account both spectral and spatial information. Texture analysis is performed to model spatial characteristics that provides additional information, which is used along with rich spectral measurements for better classification of hyperspectral imagery. The moment invariants of an image can de...
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A novel approach using volumetric texture and reduced-spectral features is presented for hyperspectral image classification. Using this approach, the volumetric textural features were extracted by volumetric gray-level co-occurrence matrices (VGLCM). The spectral features were extracted by minimum estimated abundance covariance (MEAC) and linear prediction (LP)-based band selection, and a semi-...
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The abundant information available in hyperspectral image has provided important opportunities for land-cover classification and recognition. However, “Curse of dimensionality” and small training sample set are two difficulties which hinder the improvement of computational efficiency and classification precision. In this paper, we present a co-training based method on hyperspectral image classi...
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Hyperspectral remote sensing imagery contains rich information on spectral and spatial distributions of distinct surface materials. Owing to its numerous and continuous spectral bands, hyperspectral data enables more accurate and reliable material classification than using panchromatic or multispectral imagery. However, high-dimensional spectral features and limited number of available training...
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ژورنال
عنوان ژورنال: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
سال: 2014
ISSN: 2194-9034
DOI: 10.5194/isprsarchives-xl-8-793-2014