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Integrating spectral variability and spatial distribution for object-based image analysis using curve matching approaches
ISPRS Journal of Photogrammetry and Remote Sensing ( IF 12.7 ) Pub Date : 2020-10-09 , DOI: 10.1016/j.isprsjprs.2020.09.023
Yunwei Tang , Fang Qiu , Linhai Jing , Fan Shi , Xiao Li

Object-based image analysis (OBIA) has been widely used to classify high spatial resolution (HSR) imagery. In a traditional OBIA, object-level statistical summaries such as mean values are usually used for classification. This implies that the spectral values within objects follow a Gaussian distribution. However, the pixel values in an object do not necessarily conform to a Gaussian distribution because of within object spectral heterogeneity. Consequently, these statistical summaries may misrepresent the features of the object. This shortcoming is addressed in this paper by integrating both the spectral variability and the spatial distribution of the pixels within objects to improve the traditional object-based image classification. The spectral variability is represented by histograms of the pixel values in the object, and the spatial distribution is characterized by the binary spatial covariogram of these pixels. To construct a binary spatial covariogram, a principal component analysis (PCA) is first applied to compress multiple bands into one, and the Otsu thresholding is then performed to generate a binary map reflecting the spatial configuration of the pixels. Spatial covariance is then computed for this binary map and plotted with different lag distances to derive the binary spatial covariogram. Our proposed model utilizing curves composed of the spectral histograms and binary spatial covariogram (referred to as the His-Cov model) are then used for classification based on curve matching approaches. The integration of spectral variability and spatial distribution of the pixels in the object produced superior results to curve matching approaches based on spectral variability alone and to traditional OBIA based on spectral and spatial features of the objects when classifying complex land use types in urban environments.



中文翻译:

结合光谱可变性和空间分布,使用曲线匹配方法进行基于对象的图像分析

基于对象的图像分析(OBIA)已被广泛用于对高空间分辨率(HSR)图像进行分类。在传统的OBIA中,通常使用对象级别的统计摘要(例如平均值)进行分类。这意味着对象内的光谱值遵循高斯分布。但是,由于物体内的光谱异质性,物体中的像素值不一定符合高斯分布。因此,这些统计摘要可能会歪曲对象的特征。通过集成对象内像素的光谱可变性和空间分布,以改善传统的基于对象的图像分类,可以解决此缺点。光谱可变性由对象中像素值的直方图表示,空间分布的特征是这些像素的二进制空间协变量。为了构建二进制空间协变量图,首先应用主成分分析(PCA)将多个频带压缩为一个频带,然后执行Otsu阈值处理以生成反映像素空间配置的二进制图。然后,为该二元图计算空间协方差,并用不同的滞后距离作图以得出二元空间协方图。我们提出的利用频谱直方图和二元空间协变量组成的曲线的模型(称为His-Cov模型)随后用于基于曲线匹配方法的分类。

更新日期:2020-10-11
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