کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4969814 1449984 2017 36 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A novel spectral-spatial co-training algorithm for the transductive classification of hyperspectral imagery data
ترجمه فارسی عنوان
یک الگوریتم جدید همکاری آموزشی طیفی-فضایی جدید برای طبقه بندی پرتوودرایز داده های تصویر هیپرشکرال
کلمات کلیدی
طبقه بندی تصویر فوق العاده یادگیری انتقالی استنتاج جمعی، همکاری آموزشی، اطلاعات فضایی طیفی،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
The automatic classificationr of hyperspectral data is made complex by several factors, such as the high cost of true sample labeling coupled with the high number of spectral bands, as well as the spatial correlation of the spectral signature. In this paper, a transductive collective classifier is proposed for dealing with all these factors in hyperspectral image classification. The transductive inference paradigm allows us to reduce the inference error for the given set of unlabeled data, as sparsely labeled pixels are learned by accounting for both labeled and unlabeled information. The collective inference paradigm allows us to manage the spatial correlation between spectral responses of neighboring pixels, as interacting pixels are labeled simultaneously. In particular, the innovative contribution of this study includes: (1) the design of an application-specific co-training schema to use both spectral information and spatial information, iteratively extracted at the object (set of pixels) level via collective inference; (2) the formulation of a spatial-aware example selection schema that accounts for the spatial correlation of predicted labels to augment training sets during iterative learning and (3) the investigation of a diversity class criterion that allows us to speed-up co-training classification. Experimental results validate the accuracy and efficiency of the proposed spectral-spatial, collective, co-training strategy.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 63, March 2017, Pages 229-245
نویسندگان
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