On Drivers of Subpixel Classification Accuracy—An

Subpixel classification (SPC) extracts meaningful information on land-cover classes from the mixed pixels.However, the major challenges for SPC are to obtain reliable soft reference data (RD), use apt input data, and achieve maximum accuracy. This article addresses these issues and applies the support vector machine (SVM) to retrieve the subpixel estimates of glacier facies (GF) using high radiometric-resolution Advanced Wide Field Sensor (AWiFS) data. Precise quantification of GF has fundamental importance in the glaciological research. Efficacy of the approach was first tested on the synthetic data followed by the input AWiFS and reference MultiSpectral Instrument data, including ancillary data. SPC of synthetic data resulted in overall accuracy (OA) of 95%, proving the merit of SVM. Classification accuracy is inversely related to the glacier’s surface heterogeneity. Reducing the number of classes enhanced the OA by ∼18%. Source and timing of RD invariably controls the SPC accuracy. OA improved by ∼5% on addressing the issue of temporal gap between input and RD. ∼11% increase in OA with the inclusion of ancillary data confirmed their positive effect on the accuracy. Input and reference fractional area of GF were strongly correlated (r > 0.9) with each other substantiating the results.

亚像素分类（Subpixel Classification, SPC）可从混合像元中提取土地覆盖类别的有效信息。然而，亚像素分类的核心挑战在于获取可靠的软参考数据（Reference Data, RD）、选用适配的输入数据以及实现最高分类精度。本文针对上述问题展开研究，采用支持向量机（Support Vector Machine, SVM），结合高辐射分辨率的先进宽视场传感器（Advanced Wide Field Sensor, AWiFS）数据，反演冰川相（Glacier Facies, GF）的亚像素占比估计值。冰川相的精准定量分析在冰川学研究中具有核心意义。本方法的有效性首先通过合成数据进行验证，随后分别使用输入AWiFS数据、参考多光谱仪器数据以及辅助数据开展测试。合成数据的亚像素分类总体精度（Overall Accuracy, OA）达95%，验证了支持向量机方法的优越性。分类精度与冰川表面异质性呈负相关关系。减少分类类别可使总体精度提升约18%。参考数据的来源与获取时间始终会影响亚像素分类的精度。通过解决输入数据与参考数据之间的时间匹配缺口问题，总体精度提升了约5%。加入辅助数据后，总体精度提升约11%，证实了辅助数据对分类精度的积极作用。冰川相的输入分数面积与参考分数面积之间存在极强的相关性（相关系数r>0.9），进一步验证了本研究结果的可靠性。