Integrating spectral variability and spatial distribution for object-based image analysis using curve matching approaches
Integrating spectral variability and spatial distribution for object-based image analysis using curve matching approaches
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DOI：
10.1016/j.isprsjprs.2020.09.023
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发表时间：
2020-11
影响因子：
12.7
通讯作者：
Yunwei Tang;Fang Qiu;L. Jing;Fan Shi;Xiao Li
中科院分区：
文献类型：
--
作者：
Yunwei Tang;Fang Qiu;L. Jing;Fan Shi;Xiao Li
基于对象的图像分析（OBIA）已被广泛用于高空间分辨率（HSR）图像分类。在传统的OBIA中，通常使用对象级别的统计摘要（例如平均值）来进行分类。这意味着物体内的光谱值遵循高斯分布。然而，由于对象内的光谱不均匀性，对象中的像素值不一定符合高斯分布。因此，这些统计摘要可能错误地表示对象的特征。针对这一缺点，本文综合了光谱变化和空间分布的像素内的对象，以改善传统的基于对象的图像分类。光谱变异性由目标中像素值的直方图表示，空间分布由这些像素的二进制空间协方差函数表征。为了构建二进制空间协方差函数，首先应用主成分分析（PCA）将多个波段压缩成一个波段，然后执行大津阈值处理以生成反映像素空间配置的二进制图。然后计算该二进制映射的空间协方差，并绘制不同的滞后距离，以导出二进制空间协方差函数。我们提出的模型，利用曲线组成的光谱直方图和二进制空间协方差函数（简称为His-Cov模型），然后用于分类的基础上曲线匹配方法。在城市环境中对复杂的土地利用类型进行分类时，光谱变异性和对象中像素的空间分布的集成产生了上级结果，以单独基于光谱变异性的曲线匹配方法和基于对象的光谱和空间特征的传统OBIA。
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.