ABSTRACT The ability to extract biophysical and structural properties of features in digital images is an important goal in remote sensing. Delineating the boundaries of image features, and building meaningful numerical descriptors of them based on their geometric shape, are both challenging tasks that are necessary to identify shape pattern in image data. This study seeks to evaluate and compare the performance of Fourier harmonic descriptors against dimensionless ratios of image-object shape for characterizing objects in image classification training samples. We took 150 random samples of three different object types – forest canopies, residential houses, and buildings (50 samples each) – from a high spatial-resolution satellite image (WorldView-2) covering a portion of the southern end of California’s Great Central Valley. To identify patterns in object shapes and perform object classification, we followed three steps. First, we performed image segmentation through an object-based image analysis (OBIA) method based on the Watershed Transform. We then carried out shape characterization using Fourier harmonics to measure variation in the silhouette of different object types. Finally, we compared and evaluated the performance of dimensionless ratios with Fourier descriptors by classifying different object shapes and assessing the accuracy of each method. Multinomial regression models were fitted to compare the accuracy and error of the two methods. Classification accuracy assessment was addressed utilizing hypothesis tests and significance of the likelihood ratio (LR) test and Akaike’s Information Criterion (AIC). Dimensionless ratios and Fourier harmonics had similar, moderate accuracy of 70% and 73.33%, respectively, but only four harmonics were required to achieve the best model fit, and six were needed for dimensionless ratios. Harmonic analysis provides quantitative descriptions of object shapes allowing pattern characterizations that can improve a supervised classification.

中文翻译：

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使用谐波分析对高空间分辨率卫星图像进行基于对象的图像分析

摘要 提取数字图像中特征的生物物理和结构特性的能力是遥感的一个重要目标。描绘图像特征的边界，并根据它们的几何形状构建有意义的数字描述符，都是识别图像数据中形状模式所必需的具有挑战性的任务。本研究旨在评估和比较傅立叶谐波描述符与图像对象形状的无量纲比的性能，以表征图像分类训练样本中的对象。我们从覆盖加利福尼亚大中央山谷南端一部分的高空间分辨率卫星图像 (WorldView-2) 中抽取了三种不同对象类型的 150 个随机样本——森林树冠、住宅和建筑物（各 50 个样本） . 为了识别对象形状中的模式并执行对象分类，我们遵循了三个步骤。首先，我们通过基于分水岭变换的基于对象的图像分析 (OBIA) 方法进行图像分割。然后，我们使用傅立叶谐波进行形状表征，以测量不同对象类型轮廓的变化。最后，我们通过对不同物体形状进行分类并评估每种方法的准确性，比较和评估了无量纲比与傅立叶描述符的性能。拟合多项回归模型以比较两种方法的准确性和误差。利用假设检验和似然比 (LR) 检验和 Akaike 信息准则 (AIC) 的显着性来解决分类准确性评估。无量纲比和傅立叶谐波有相似之处，分别为 70% 和 73.33% 的中等精度，但只需要四个谐波即可实现最佳模型拟合，而无量纲比率则需要六个谐波。谐波分析提供对象形状的定量描述，允许模式表征可以改进监督分类。