AI 中文总结
针对SAR自动目标识别中深度学习方法可解释性低及现有DNMF方法存在的问题，提出G-DNMF方法，通过拉格朗日乘数法推导更新规则，摒弃逐层分解策略，提升多层特征提取能力，实验验证其性能优于现有算法。
AI 中文摘要
深度非负矩阵分解（DNMF）技术旨在解决基于深度学习的方法在从合成孔径雷达（SAR）目标样本中提取多层特征时可解释性低的问题。然而，现有DNMF方法采用逐层分解策略，易导致误差累积和局部最优。本文提出广义深度非负矩阵分解（G-DNMF）方法应对SAR自动目标识别挑战。G-DNMF追求全局最优，用拉格朗日乘数法推导参数更新规则，表明基于编码矩阵和混合矩阵的DNMF方法是其特例，具有通用性。该方法摒弃逐层分解策略，有效降低局部最优风险并消除误差累积，提升多层特征提取能力。实验结果通过展示G-DNMF提取的特征图像和重建原始图像，验证了其对多层特征的纯加法理解及可解释性。基于MSTAR和OpenSARship数据集的实验表明，G-DNMF在稳定性和识别性能上优于现有DNMF算法及其衍生算法。
英文摘要
The deep nonnegative matrix factorization (DNMF) technique is proposed to address the low interpretability of deep learning-based methods in extracting multilayer features from synthetic aperture radar (SAR) target samples. However, existing DNMF methods employ a layer-by-layer decomposition strategy, which is prone to causing error accumulation and local optimum, thereby hindering a consistent improvement in recognition accuracy as the number of layer increases. In this paper, a robust multilayer feature extraction method, termed generalized deep non-negative matrix factorization (G-DNMF), is proposed to address the above challenges in SAR automatic target recognition (ATR). The G-DNMF aims global optimality and derives the update rules for each parameter using lagrangian multiplier method. The new update formula indicates that both the DNMF method based on the encoding matrix and the mixing matrix are special cases of the proposed method, theoretically demonstrating the universality of proposed method. In general, the proposed method discards the layer-by-layer decomposition strategy, thereby effectively mitigating the risk of local optima and eliminating error accumulation, leading to a significant improvement in DNMF's multi-layer feature extraction capability. The experimental results, by presenting the feature images extracted from each layer by G-DNMF and the reconstructed original images, verified the proposed method's pure additive understanding of multi-layer features and demonstrated its interpretability. The experimental results based on MSTAR and OpenSARship datasets show that G-DNMF outperforms existing DNMF algorithms and their derivatives in terms of stability and recognition performance.
