AI-Driven Hyperspectral Image Classification using Low-Rank Representation and Spatial-Spectral Information
الموضوعات : سامانههای پردازشی و ارتباطی چندرسانهای هوشمند
1 - Assistant Professor, Department of Electrical Engineering, Khorm.C., Islamic Azad University, Khormoj, Iran
الکلمات المفتاحية: تصویر ابر طیفی, طبقه بندی, نمایش رتبه پایین, اطلاعات مکانی,
ملخص المقالة :
Hyperspectral image (HSI) classification is one of the most important processes on these images, which artificial intelligence (AI) techniques have recently achieved significant success in this process. Data representation using a low-dimensional subspace is critical in classification task of HSI. By employing the Low-Rank Representation (LLR) approach, low-dimensional representations from data can be effectively extracted. Since this method neglects local information, the extracted features are not sufficiently rich and informative for classification. This paper proposes a machine learning method for hyperspectral image classification, which involves employing the Structure of the Data Regularized LLR with Dictionary Learning (SDLRRDL) model. Our AI-based model, SDLRRDL, presents an approach for learning data structures through a low-rank and sparse representation. Also, to leverage structural data information, a penalty is added to the low-rank representation model. The method can create similar features for data of the same class by combining image class signature and spectral-spatial information. Moreover, image samples are represented through a linear combination of dictionary atoms. Rich and informative features are extracted through the trained dictionary utilizing a training data set that better matches the training content. Then, extracted features are classified using the support vector machine with high accuracy. Simulation results demonstrate that the proposed method has a superior classification accuracy compared to state-of-the-art methods on three popular HSI datasets. The proposed method improves the classification accuracy of the state-of-the-art methods more than 2.13, 0.2, and 0.6 percent on the Indian Pines, Pavia University, and Salinas datasets, respectively.
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