The present study reports classification and analysis of composite land features using fusion images obtained by fusing two original hyperspectral and multispectral datasets. The high spatial-spectral resolution, multi-instrument and multi-period satellite images were used for fusion. Three pixel level fusion based techniques, Color Normalized Spectral Sharpening (CNSS), Principal Component Spectral Sharpening Transform (PCSST) and Gram-Schmidt Transform (GST), were implemented on the datasets. Performance evaluations of three fusion algorithms were done using classification results. The Support Vector Machine (SVM) and Gaussian Maximum Likelihood Classification (MLC) were used for classification using five types of images, viz. hyperspectral, multispectral and three fused images. Number of classes considered was eight. Sufficient number of ground field data for each class has also been acquired which was needed for supervise based classification. The accuracy was improved from 74.44 to 97.65% when the fused images were considered with SVM classifier. Similarly, the results were improved from 69.25 to 94.61% with original and fused data using MLC classifier. The fusion image technique was found to be superior to the single original image and the SVM is better than the MLC method.

本研究报告了通过融合两个原始的高光谱和多光谱数据集获得的融合图像，对复合土地特征进行分类和分析。使用高空间光谱分辨率，多仪器和多周期卫星图像进行融合。在数据集上实现了基于三种像素级别融合的技术，即色彩归一化光谱锐化（CNSS），主成分光谱锐化变换（PCSST）和Gram-Schmidt变换（GST）。使用分类结果对三种融合算法进行了性能评估。支持向量机（SVM）和高斯最大似然分类（MLC）用于使用五种类型的图像进行分类，即。高光谱，多光谱和三个融合图像。考虑的班级数量为八。每个类别的地面数据也已获取足够数量，这是基于监督的分类所必需的。当使用SVM分类器考虑融合图像时，准确性从74.44提高到97.65％。同样，使用MLC分类器将原始数据和融合数据的结果从69.25％提高到94.61％。发现融合图像技术优于单个原始图像，并且SVM优于MLC方法。