This work proposes a new method to treat spatial and spectral information interactively. The method extracts spatial features, ie, variogram, gray‐level co‐occurrence matrix (GLCM), histograms of oriented gradients (HOG), and local binary pattern (LBP) features, from each wavelength image of hypercube and principal component analysis (PCA) is applied on this spatial feature matrix to identify wavelength‐dependent variation in spatial patterns. Resultant image is obtained by projecting the score values to the original data. Three datasets, including a synthetic hyperspectral image (Dataset 1), a set of real hyperspectral images of salmon fillets (Dataset 2), and remote‐sensing images (Dataset 3), were utilized to evaluate the performance of the proposed method. Results from Dataset 1 showed that the spatial‐spectral methods had the potential of reducing baseline offset noise. Dataset 2 revealed that spatial‐spectral methods can alleviate noisy pixels with strong signal and reduce shadow effects. In addition, substantial improvements were obtained in case of classification between white stripe and red muscle pixels by using the HOG‐based approach with correct classification rate (CCR) of 0.97 compared with the models directly built from raw and standard normal variate (SNV) preprocessed spectra (CCR = 0.94). Samson image of Dataset 3 suggested the flexibility and effectiveness of the proposed method by improving CCR of 0.96 using conventional PCA on SNV pretreated spectra to 0.98 using GLCM‐based approach on SNV preprocessed spectra. Overall, experimental results demonstrated that the spatial‐spectral methods can improve the results found by using the spectral information alone because of the spatial information provided.

中文翻译：

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基于纹理特征结合PCA的高光谱图像信息提取空间光谱分析方法

这项工作提出了一种交互式处理空间和光谱信息的新方法。该方法从超立方体和主成分分析 (PCA) 的每个波长图像中提取空间特征，即变异函数、灰度共生矩阵 (GLCM)、定向梯度直方图 (HOG) 和局部二值模式 (LBP) 特征。 ) 应用于此空间特征矩阵以识别空间模式中与波长相关的变化。通过将得分值投影到原始数据上获得结果图像。三个数据集，包括合成高光谱图像（数据集 1）、一组真实的鲑鱼鱼片高光谱图像（数据集 2）和遥感图像（数据集 3），被用来评估所提出方法的性能。数据集 1 的结果表明空间光谱方法具有降低基线偏移噪声的潜力。数据集 2 显示空间光谱方法可以减轻具有强信号的噪声像素并减少阴影效应。此外，与直接从原始和标准正态变量 (SNV) 预处理建立的模型相比，使用基于 HOG 的方法在白色条纹和红色肌肉像素之间进行分类时获得了实质性的改进，正确分类率 (CCR) 为 0.97光谱（CCR = 0.94）。数据集 3 的 Samson 图像通过使用基于 SNV 预处理光谱的 GLCM 方法在 SNV 预处理光谱上使用传统 PCA 将 0.96 的 CCR 提高到 0.98，表明了所提出方法的灵活性和有效性。全面的，