ABSTRACT

This study evaluates various combinations of data fusion techniques at Pixel, Feature, and Decision level for crop classification using Sentinel-1 Synthetic Aperture Radar (SAR) data and Resourcesat-2 LISS (Linear Imaging Self Scanning) III, optical data for Yadgir District of Karnataka, India. For Pixel level data fusion, techniques such as brovey transformation (BT), principal component analysis (PCA), multiplicative transformation (MLT), and wavelet with IHS (intensity-hue-saturation) were used. Results were compared between different fusion techniques visually, statistically (using universal image quality index), and through image classification (Rule-based and Maximum likelihood) for major crops (Rice, Cotton, and Pigeon pea) in the area. The estimated crop area for all three major crops was compared with the Government statistics. Among the four pixel-level fusion techniques used, the wavelet method performed best in retaining the image quality. However, the study showed that using the feature-level fusion technique, maximum accuracy was obtained for Rice crop. In contrast, the decision-level fusion improved the efficiency for other crops (Cotton and Pigeon pea).

摘要

这项研究使用Sentinel-1合成孔径雷达（SAR）数据和Resourcesat-2 LISS（线性成像自扫描）III（雅德吉尔地区的光学数据）评估了像素分类，特征分类和决策级数据融合技术对作物分类的各种组合。印度卡纳塔克邦。对于像素级数据融合，使用了诸如布罗维变换（BT），主成分分析（PCA），乘性变换（MLT）和具有IHS（强度-色相饱和度）的小波的技术。在视觉上，统计上（使用通用图像质量指标）并通过图像分类（基于规则和最大似然）对该地区主要作物（水稻，棉花和木豆）的结果进行了比较。将所有三种主要农作物的估计收成面积与政府统计数据进行了比较。在所使用的四种像素级融合技术中，小波方法在保持图像质量方面表现最佳。但是，研究表明，使用特征级融合技术可以使水稻作物获得最大的准确性。相反，决策级融合提高了其他农作物（棉花和木豆）的效率。