Filippi Anthony M, Archibald Rick, Bhaduri Budhendra L, Bright Edward A
Department of Geography, Texas A&M University, College Station, Texas 77843-3147, USA.
Opt Express. 2009 Dec 21;17(26):23823-42. doi: 10.1364/OE.17.023823.
Extracting endmembers from remotely-sensed images of vegetated areas can present difficulties. In this research, we applied a recently-developed endmember-extraction algorithm based on Support Vector Machines to the problem of semi-autonomous estimation of vegetation endmembers from a hyperspectral image. This algorithm, referred to as Support Vector Machine-Based Endmember Extraction (SVM-BEE), accurately and rapidly yields a computed representation of hyperspectral data that can accommodate multiple distributions. The number of distributions is identified without prior knowledge, based upon this representation. Prior work established that SVM-BEE is robustly noise-tolerant and can semi-automatically estimate endmembers; synthetic data and a geologic scene were previously analyzed. Here we compared the efficacies of SVM-BEE, N-FINDR, and SMACC algorithms in extracting endmembers from a real, predominantly-agricultural scene. SVM-BEE estimated vegetation and other endmembers for all classes in the image, which N-FINDR and SMACC failed to do. SVM-BEE was consistent in the endmembers that it estimated across replicate trials. Spectral angle mapper (SAM) classifications based on SVM-BEE-estimated endmembers were significantly more accurate compared with those based on N-FINDR- and (in general) SMACC-endmembers. Linear spectral unmixing accrued overall accuracies similar to those of SAM.
从植被区域的遥感图像中提取端元可能会遇到困难。在本研究中，我们将一种最近开发的基于支持向量机的端元提取算法应用于从高光谱图像中半自动估计植被端元的问题。这种算法被称为基于支持向量机的端元提取（SVM-BEE），它能准确快速地生成高光谱数据的计算表示，该表示可以适应多种分布。基于这种表示，无需先验知识即可确定分布的数量。先前的工作表明，SVM-BEE具有很强的抗噪声能力，能够半自动估计端元；之前已对合成数据和地质场景进行了分析。在此，我们比较了SVM-BEE、N-FINDR和SMACC算法从一个以农业为主的真实场景中提取端元的效率。SVM-BEE为图像中的所有类别估计了植被和其他端元，而N-FINDR和SMACC则未能做到。SVM-BEE在重复试验中估计的端元具有一致性。与基于N-FINDR和（总体而言）SMACC端元的光谱角映射器（SAM）分类相比，基于SVM-BEE估计端元的SAM分类显著更准确。线性光谱混合分析获得的总体精度与SAM的相似。
