用3D卷积稀疏编码和单纯形体积最大化，提升高光谱解混精度。
Hyperspectral Unmixing with 3D Convolutional Sparse Coding and Projected Simplex Volume Maximization
- 基于3D稀疏编码块的网络，联合学习光谱与空间特征。
- 在真实与模拟数据上，信噪比10~30分贝下均优于现有方法。
- 适合高光谱图像分析、遥感解混等科研与工程场景。
高光谱解混（HSU）旨在将每个像素分解为组成端元并估计其丰度分数。本文提出一种基于算法展开的网络模型——3D卷积稀疏编码网络（3D-CSCNet），构建于3D CSC模型之上。不同于已有展开网络，3D-CSCNet在自编码器（AE）框架内设计，提出通过深度算法展开得到的3D CSC块（3D-CSCB）以求解3D CSC问题。对于高光谱图像（HSI），3D-CSCNet使用3D-CSCB估计丰度矩阵，利用3D CSC联合学习数据立方体中的光谱与空间关系。随后，丰度矩阵输入到自编码器解码器中重建原始图像，解码器权重被提取为端元矩阵。此外，我们提出投影单纯形体积最大化（PSVM）算法用于端元估计，并以此初始化3D-CSCNet解码器权重。在三个真实数据集和一个模拟数据集上，于三种不同信噪比（SNR）水平下的实验表明，3D-CSCNet显著优于现有先进方法。
原文摘要 · Abstract (English)
Hyperspectral unmixing (HSU) aims to separate each pixel into its constituent endmembers and estimate their corresponding abundance fractions. This work presents an algorithm-unrolling-based network for the HSU task, named the 3D Convolutional Sparse Coding Network (3D-CSCNet), built upon a 3D CSC model. Unlike existing unrolling-based networks, our 3D-CSCNet is designed within the powerful autoencoder (AE) framework. Specifically, to solve the 3D CSC problem, we propose a 3D CSC block (3D-CSCB) derived through deep algorithm unrolling. Given a hyperspectral image (HSI), 3D-CSCNet employs the 3D-CSCB to estimate the abundance matrix. The use of 3D CSC enables joint learning of spectral and spatial relationships in the 3D HSI data cube. The estimated abundance matrix is then passed to the AE decoder to reconstruct the HSI, and the decoder weights are extracted as the endmember matrix. Additionally, we propose a projected simplex volume maximization (PSVM) algorithm for endmember estimation, and the resulting endmembers are used to initialize the decoder weights of 3D-CSCNet. Extensive experiments on three real datasets and one simulated dataset with three different signal-to-noise ratio (SNR) levels demonstrate that our 3D-CSCNet outperforms state-of-the-art methods.
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