高光谱成像
计算机科学
杠杆(统计)
人工智能
模式识别(心理学)
词典学习
稀疏逼近
张量(固有定义)
代表(政治)
一般化
聚类分析
秩(图论)
稀疏矩阵
特征学习
数学
政治学
纯数学
法学
高斯分布
量子力学
数学分析
物理
组合数学
政治
作者
Zhaohui Xue,Xiangyu Nie,Mengxue Zhang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:60: 1-19
标识
DOI:10.1109/tgrs.2022.3223792
摘要
Low-rank and sparse representation (LRSR) has gained popularity in hyperspectral image (HSI) classification. However, existing LRSR models usually treat HSI as a two-dimensional matrix, which may destroy the original 3D intrinsic structure of HSI. Moreover, the dictionary consisting of only training samples lacks completeness and may be suboptimal for representation. To overcome the above issues, we propose an incremental dictionary learning-driven tensor low-rank and sparse representation (TLRSR-IDL) model for HSI classification. First, we represent HSI as a third-order tensor to retain its original 3D intrinsic structure by using the TLRSR model, which also combines both sparsity and low rankness to maintain global and local data structures. Second, we design an optimal reconstruction within regularized neighborhood (ORRN) method to exploit spectral-spatial information by avoiding the interference of heterogeneous samples in the neighborhood. Finally, an incremental dictionary learning (IDL) scheme is designed to iteratively introduce augmented samples into the dictionary, and the final classification map is produced by feeding back the last round of the incremental dictionary into the TLRSR-IDL model. The main innovative contribution lies in that the proposed IDL scheme can leverage supervised and unsupervised information, which greatly enhances traditional LRSR and TLRSR models. Experimental results based on three popular hyperspectral datasets demonstrate that the proposed method outperforms other related counterparts in terms of classification accuracy and generalization performance, with OA improvements of 0.97%-16.83%, 1.25%-6.89%, and 0.85%-6.67% for Indian Pines, Pavia University, and Salinas, respectively.
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