Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification
المؤلفون المشاركون
Tang, Yidong
Huang, Shucai
Xue, Aijun
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-06-09
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
The sparse representation based classifier (SRC) and its kernel version (KSRC) have been employed for hyperspectral image (HSI) classification.
However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given.
Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH) model is established in this paper.
In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary.
The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel.
Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required.
Furthermore, the kernel method is employed to improve the interclass separability.
In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP) algorithm.
Then the query pixel can be labeled by the characteristics of the coding vectors.
Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification.
It enhances the discrimination and hence improves the performance.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Tang, Yidong& Huang, Shucai& Xue, Aijun. 2016. Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1112030
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Tang, Yidong…[et al.]. Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification. Mathematical Problems in Engineering No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1112030
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Tang, Yidong& Huang, Shucai& Xue, Aijun. Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1112030
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1112030
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر