Information Department, Hohai University, Nanjing, 211100, China.
Department of Oncology, Drum Tower Hospital, School of Medicine, Nanjing University, Nanjing, 211108, China.
Comput Biol Med. 2023 Dec;167:107568. doi: 10.1016/j.compbiomed.2023.107568. Epub 2023 Oct 21.
Microscopic hyperspectral images has the advantage of containing rich spatial and spectral information. However, the large number of spectral bands provides a significant amount of spectral features, but also leads to data redundancy and noise, which seriously affect the recognition and classification performance of the images, as well as increasing the requirements for computation and storage. To address this issue, we propose a dimensionality reduction algorithm named enhanced discriminant local constraint preserving projection (EDLCPP). Specifically, the global spectral attention mechanism focuses on important bands, the high discriminability sample selection module measures the discriminability of samples using a modified average neighborhood margin, the graph construction module preserves the local geometric relationship and discriminant information, and the graph embedding module embeds the constructed graphs into a low-dimensional space to obtain the projection matrices. Experimental results on eight cholangiocarcinoma (CCA) hyperspectral images, Bloodcell1-3, and Bloodcell2-2 datasets have demonstrated the effectiveness of the proposed method.
显微高光谱图像具有包含丰富的空间和光谱信息的优点。然而，大量的光谱波段提供了大量的光谱特征，但也导致了数据冗余和噪声，这严重影响了图像的识别和分类性能，同时也增加了计算和存储的要求。针对这个问题，我们提出了一种名为增强判别局部约束保持投影（EDLCPP）的降维算法。具体来说，全局光谱注意力机制关注重要的波段，高判别样本选择模块使用改进的平均邻域边缘来衡量样本的判别能力，图构建模块保持局部几何关系和判别信息，图嵌入模块将构建的图嵌入到低维空间中以获得投影矩阵。在八个胆管癌（CCA）高光谱图像、Bloodcell1-3 和 Bloodcell2-2 数据集上的实验结果表明了该方法的有效性。
