The issue of mixed pixels is a prevalent challenge in hyperspectral images (HSIs), largely due to imaging modalities and hardware limitations. Subpixel mapping (SPM) can address this issue by distinguishing between land cover classes at a subpixel scale. In recent years, deep convolutional neural networks have demonstrated their potential and effectiveness for SPM. However, during the SPM process, the unique strengths of features at different network levels are often neglected, resulting in insufficient interaction between features at varying levels. Consequently, the network cannot fully extract and utilize the information from higher and lower level features nor can it thoroughly understand and recognize the input images. This ultimately hampers the model’s SPM performance. In this study, we propose a deep spatial feedback refined network with multilevel feature fusion (SFRNet-MLFF) for SPM of hyperspectral remote sensing images. This innovative network incorporates a feedback mechanism and a multilevel feature fusion (MLFF) technique, both of which significantly enhance the interaction and aggregation of information across diverse features. Furthermore, throughout the entire network process, we apply solution space constraints to various spatial resolution contexts. These strategic implementations are expected to substantiate the network’s performance in SPM. The experimental results on three hyperspectral datasets demonstrate that SFRNet-MLFF produces results with more distinct feature outlines. These results are better equipped to indicate the precise location and distribution patterns of land cover classes in low-resolution images at higher spatial resolutions. Furthermore, SFRNet-MLFF significantly improves the overall accuracy (OA) compared to the state-of-the-art deep learning SPM networks (DLSPMNets).

中文翻译：

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用于高光谱图像子像素映射的具有多级特征融合的深度空间反馈细化网络


混合像素问题是高光谱图像 (HSI) 中的一个普遍挑战，很大程度上是由于成像模式和硬件限制造成的。子像素映射（SPM）可以通过区分子像素尺度的土地覆盖类别来解决这个问题。近年来，深度卷积神经网络已经证明了其在 SPM 方面的潜力和有效性。然而，在SPM过程中，不同网络层次特征的独特优势往往被忽视，导致不同层次特征之间的交互不足。因此，网络无法充分提取和利用来自高层和低层特征的信息，也无法彻底理解和识别输入图像。这最终会影响模型的 SPM 性能。在本研究中，我们提出了一种具有多级特征融合的深度空间反馈细化网络（SFRNet-MLFF），用于高光谱遥感图像的 SPM。这种创新网络结合了反馈机制和多级特征融合（MLFF）技术，这两者都显着增强了不同特征之间信息的交互和聚合。此外，在整个网络过程中，我们将解决方案空间约束应用于各种空间分辨率上下文。这些战略实施预计将证实网络在 SPM 方面的性能。三个高光谱数据集的实验结果表明，SFRNet-MLFF 产生的结果具有更清晰的特征轮廓。这些结果能够更好地指示较高空间分辨率的低分辨率图像中土地覆盖类别的精确位置和分布模式。 此外，与最先进的深度学习 SPM 网络 (DLSPMNets) 相比，SFRNet-MLFF 显着提高了整体准确性 (OA)。