ABSTRACT The recent development of hybrid polarimetry synthetic aperture radar (SAR) data with decomposition techniques has improved land cover mapping. The generation of land cover maps using land cover classifications of different scenarios was carried out for Theni district, Tamil Nadu, India using optical and hybrid polarimetry SAR data. The present study focuses on evaluating the capability and contribution of hybrid decomposition techniques from Radar Imaging Satellite-1 (RISAT-1) data to improve the optical image classification accuracy. Hybrid decomposition techniques such as m-delta (m-δ), m-chi (m-χ) and m-alpha (m-α) were extracted using Stokes parameters from circular fine resolution stripmap-1 (cFRS-1) mode of RISAT-1 data. Grey level co-occurrence matrix (GLCM) textural bands were extracted from backscattering images of RISAT-1 and spectral bands of linear imaging self scanning sensor – IV (LISS-IV) image. The hybrid decomposition parameters were used to improve the classification accuracies of SAR and optical images and compared with GLCM textural bands. Support vector machine (SVM) classifier was performed for different scenarios and accuracy assessment was evaluated for all the classified images using confusion matrix with help of reference data. The study observed that the RISAT-1 derived products provided reasonable classification accuracy (54%) and also provided better results when added to spectral bands of LISS-IV (74%). The mean and dissimilarity of GLCM textural bands provided best classification, when added individually with RISAT-1 (66%) and LISS-IV (82%), and combined with RISAT-1 and LISS-IV (80%). The results indicate that the optical sensor performs better for the classification of water bodies, fallow land and settlement, however, plantation and rice crops are better classified when combined with SAR hybrid decomposition parameters. The study also observed that GLCM textural bands may change the pixel values based on the window size. The changed pixel values contributed to improving the classification accuracy by 15%. The selected classification and processing methods resulted in reasonable accuracy in land cover mapping in a hilly area with intermittent plains.

中文翻译：

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使用混合极化 SAR 数据增强多光谱图像的土地覆盖分类

摘要 混合极化合成孔径雷达 (SAR) 数据与分解技术的最新发展改进了土地覆盖制图。使用光学和混合偏振 SAR 数据在印度泰米尔纳德邦的 Theni 区使用不同场景的土地覆盖分类生成土地覆盖图。本研究的重点是评估来自雷达成像卫星 1 (RISAT-1) 数据的混合分解技术在提高光学图像分类精度方面的能力和贡献。混合分解技术如 m-delta (m-δ)、m-chi (m-χ) 和 m-alpha (m-α) 是使用 Stokes 参数从圆形精细分辨率条带图-1 (cFRS-1) 模式中提取的RISAT-1 数据。从RISAT-1的背向散射图像和线性成像自扫描传感器-IV（LISS-IV）图像的光谱带中提取灰度共生矩阵（GLCM）纹理带。混合分解参数用于提高SAR和光学图像的分类精度，并与GLCM纹理带进行比较。针对不同场景执行支持向量机（SVM）分类器，并在参考数据的帮助下使用混淆矩阵对所有分类图像进行精度评估。该研究观察到，RISAT-1 衍生产品提供了合理的分类准确度 (54%)，并且在添加到 LISS-IV 的光谱带时也提供了更好的结果 (74%)。GLCM 纹理带的平均值和差异提供了最好的分类，当与 RISAT-1 (66%) 和 LISS-IV (82%) 单独添加，并与 RISAT-1 和 LISS-IV (80%) 组合时。结果表明，光学传感器在水体、休耕地和聚落的分类方面表现更好，而结合SAR杂交分解参数对种植园和水稻作物进行更好的分类。该研究还观察到 GLCM 纹理带可能会根据窗口大小改变像素值。更改后的像素值有助于将分类精度提高 15%。所选的分类和处理方法使得在具有间歇性平原的丘陵地区的土地覆盖制图具有合理的准确性。然而，当结合 SAR 杂交分解参数时，可以更好地对休耕地和定居点进行分类。该研究还观察到 GLCM 纹理带可能会根据窗口大小改变像素值。更改后的像素值有助于将分类精度提高 15%。所选的分类和处理方法使得在具有间歇性平原的丘陵地区的土地覆盖制图具有合理的准确性。然而，当结合 SAR 杂交分解参数时，可以更好地对休耕地和定居点进行分类。该研究还观察到 GLCM 纹理带可能会根据窗口大小改变像素值。更改后的像素值有助于将分类精度提高 15%。所选的分类和处理方法使得在具有间歇性平原的丘陵地区的土地覆盖制图具有合理的准确性。