Abstract

In this study, Hölder Exponent (HE), Variance (var) and densely populated range are measured to extract meaningful features from the high resolution (HR) images. These extracted features are considered here as the reduced representation of high resolution (HR) images. Five-layer simplified Artificial Neural Network (ANN) architecture configured and trained using the reduced representation for building detection and counting in HR images. The scale and orientation of the movable window are changed to find an optimum bounding box of the buildings. The method is validated by applying on World View-2 pan-sharpened multispectral images having spatial resolution 0.46 m. In comparison with CNN and ResNet-18, the accuracy assessment result is quite promising (92%) with proposed method for detecting buildings in HR images. The proposed method, distinctly identified scale variant buildings and detected individual buildings even if they are in close proximity.

摘要

在这项研究中，对霍尔德指数（HE），方差（var）和人口稠密范围进行了测量，以从高分辨率（HR）图像中提取有意义的特征。这些提取的特征在这里被认为是高分辨率（HR）图像的简化表示。五层简化人工神经网络（（ANN）架构使用减少的表示进行配置和培训，以进行HR图像中的建筑物检测和计数。更改可移动窗口的比例和方向以找到建筑物的最佳边界框。该方法通过在具有空间分辨率为0.46 m的World View-2泛锐化多光谱图像上进行验证。与CNN和ResNet-18相比，提出的用于在HR图像中检测建筑物的方法的准确性评估结果非常有希望（92％）。所提出的方法可以清楚地识别出比例变化的建筑物，并且即使它们非常接近也可以检测到单独的建筑物。