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Improving hyperspectral image segmentation by applying inverse noise weighting and outlier removal for optimal scale selection
ISPRS Journal of Photogrammetry and Remote Sensing ( IF 12.7 ) Pub Date : 2020-12-11 , DOI: 10.1016/j.isprsjprs.2020.11.013
Phuong D. Dao , Kiran Mantripragada , Yuhong He , Faisal Z. Qureshi

Optimal scale selection for image segmentation is an essential component of the Object-Based Image Analysis (OBIA) and interpretation. An optimal segmentation scale is a scale at which image objects, overall, best represent real-world ground objects and features across the entire image. At this scale, the intra-object variance is ideally lowest and the inter-object spatial autocorrelation is ideally highest, and a change in the scale could cause an abrupt change in these measures. Unsupervised parameter optimization methods typically use global measures of spatial and spectral properties calculated from all image objects in all bands as the target criteria to determine the optimal segmentation scale. However, no studies consider the effect of noise in image spectral bands on the segmentation assessment and scale selection. Furthermore, these global measures could be affected by outliers or extreme values from a small number of objects. These issues may lead to incorrect assessment and selection of optimal scales and cause the uncertainties in subsequent segmentation and classification results. These issues become more pronounced when segmenting hyperspectral data with large spectral variability across the spectrum. In this study, we propose an enhanced method that 1) incorporates the band’s inverse noise weighting in the segmentation and 2) detects and removes outliers before determining segmentation scale parameters. The proposed method is evaluated on three well-established segmentation approaches – k-means, mean-shift, and watershed. The generated segments are validated by comparing them with reference polygons using normalized over-segmentation (OS), under-segmentation (US), and the Euclidean Distance (ED) indices. The results demonstrate that this proposed scale selection method produces more accurate and reliable segmentation results. The approach can be applied to other segmentation selection criteria and are useful for automatic multi-parameter tuning and optimal scale parameter selections in OBIA methods in remote sensing.



中文翻译:

通过应用反噪声加权和离群值消除来改善高光谱图像分割,以实现最佳比例选择

图像分割的最佳比例选择是基于对象的图像分析(OBIA)和解释的重要组成部分。最佳分割比例是这样的比例:图像对象总体上最能代表整个图像的真实地面对象和特征。在此尺度上,理想情况下,对象内方差最低,理想情况下,对象间空间自相关性最高,并且尺度的变化可能会导致这些度量的突然变化。无监督参数优化方法通常使用从所有波段中所有图像对象计算出的空间和光谱特性的全局度量作为目标标准,以确定最佳分割尺度。然而,没有研究考虑图像光谱带中的噪声对分割评估和标度选择的影响。此外,这些全局度量可能会受到少数对象的异常值或极端值的影响。这些问题可能会导致错误的评估和最佳比例的选择,并导致后续细分和分类结果的不确定性。当在光谱范围内以较大的光谱可变性分割高光谱数据时,这些问题变得更加明显。在这项研究中,我们提出了一种增强的方法,该方法是:1)在分割中加入频带的逆噪声加权,以及2)在确定分割标度参数之前检测并消除异常值。所提出的方法是在三种公认的分割方法上进行评估的-k均值,均值漂移和分水岭。通过使用归一化过分割(OS),欠分割(US),和欧几里得距离(ED)指数。结果表明,该提出的尺度选择方法产生了更准确和可靠的分割结果。该方法可以应用于其他分段选择标准,并且对于遥感中的OBIA方法中的自动多参数调整和最佳比例参数选择很有用。

更新日期:2020-12-11
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