Effective management of corn straw and stubble is critical in conservation tillage, as it impacts soil health and productivity. However, accurate classification of different types of straw cover has been hindered by their similar spectral and spatial characteristics and the low spatial resolution of hyperspectral satellite imagery. Moreover, traditional convolution neural network (CNN)-based methods, which rely on first-order statistics for feature extraction, often struggle to extract distinguishable features of highly similar objects effectively, thereby reducing classification accuracy. In this study, a second-order statistical-feature extraction algorithm based on CNN that uses fused multispectral and hyperspectral data was tested for its ability to classify types of straw cover. In the first step, coupled non-negative matrix factorization (CNMF) was used to fuse hyperspectral and multispectral images effectively, thereby enhancing the spatial resolution of the hyperspectral data. In this study, we integrated pointwise convolution (PWC), depthwise convolution (DWC), and a distance covariance matrix (DCM) to form a mixed convolution and DCM (MCDCM) network; we used this to extract and integrate deep spectral–spatial features of the hyperspectral images. Our experimental results show that the MCDCM network significantly improved classification accuracy compared to traditional methods, with accuracy rates for the different straw-cover types exceeding 90% and overall accuracy reaching 98.26%. The fused image also exhibited better preservation of feature edges and contours. The accurate identification of corn-straw-cover types achieved with the proposed MCDCM method is a major step in optimizing conservation-farming practices, improving soil fertility and farm productivity, and supporting sustainable ecological development.

中文翻译：

---

一种混合卷积和距离协方差矩阵网络，用于使用融合的高光谱和多光谱数据对玉米秸秆覆盖类型进行精细分类


有效管理玉米秸秆和残茬在保护性耕作中至关重要，因为它会影响土壤健康和生产力。然而，由于它们相似的光谱和空间特征以及高光谱卫星图像的低空间分辨率，阻碍了对不同类型秸秆覆盖物的准确分类。此外，传统的基于卷积神经网络 （CNN） 的方法依赖于一阶统计量进行特征提取，通常难以有效地提取高度相似对象的可区分特征，从而降低了分类准确性。在这项研究中，测试了一种基于 CNN 的二阶统计特征提取算法，该算法使用融合多光谱和高光谱数据，以评估其对秸秆覆盖类型进行分类的能力。第一步，采用耦合非负矩阵分解 （CNMF） 有效融合高光谱和多光谱图像，从而提高高光谱数据的空间分辨率。在本研究中，我们整合了逐点卷积 （PWC） 、深度卷积 （DWC） 和距离协方差矩阵 （DCM），形成了一个混合卷积和 DCM （MCDCM） 网络;我们用它来提取和整合高光谱图像的深度光谱-空间特征。实验结果表明，与传统方法相比，MCDCM 网络显著提高了分类精度，不同秸秆覆盖类型的分类准确率超过 90%，总体准确率达到 98.26%。融合图像还表现出对特征边缘和轮廓的更好保留。 使用所提出的 MCDCM 方法实现的玉米秸秆覆盖类型的准确识别是优化保护性农业实践、提高土壤肥力和农场生产力以及支持可持续生态发展的重要一步。