We propose the convolutional neural networks approach for detecting urban slums from very high resolution (VHR) satellite images. Slums are becoming an inevitable and growing phenomenon in cities of the global south, whose locations are inappropriate in official statistics and maps. Thus the automatic detection and identification of slums provides vibrant information to decision-makers for formulating pro-poor policies in urban planning. However, field surveys are used as the conventional methods for slum detection, which are expensive and inefficient. The challenge is to find an automatic approach for identifying slums from VHR imagery. Although numerous studies focused on detecting slums from satellite data, only limited number captured their differences. Due to the unique spectral signatures of urban slums, it could not be simply classified as other urban (buildings, vegetation, and roads) features. We explore the potential of the dilated kernel-based deep convolutional neural network (DK-DCNN) approach to the learning of discriminatory spatial features and the automatic detection of slums from other features. However, the result obtained by the proposed DK-DCNN achieves high accuracy. The open area of informal settlements and the roofing structures of formal settlements are misclassified as slums. Morphological spatial pattern analysis, based on mathematical morphology, is used as a postprocessing method to enhance the classification accuracy. The four distinct very high-resolution satellite images captured by WorldView-2 Sensor (1.84 m) of Madurai and Tiruppur city, South India, have shown the performance of the proposed method to distinguish urban slums from other features by producing higher accuracy than any other approach.

中文翻译：

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深度卷积神经网络与基于数学形态学的后分类框架的集成，用于城市贫民窟测绘

我们提出了卷积神经网络方法，用于从超高分辨率（VHR）卫星图像中检测城市贫民窟。在全球南方的城市中，贫民窟正在成为不可避免的且正在增长的现象，在官方统计数据和地图中，贫民窟的位置是不合适的。因此，对贫民窟的自动检测和识别为决策者提供了生机勃勃的信息，以制定城市规划中的扶贫政策。然而，现场勘测被用作贫民窟检测的常规方法，这种方法昂贵且效率低下。面临的挑战是找到一种从VHR图像中识别贫民窟的自动方法。尽管许多研究集中于从卫星数据中检测贫民窟，但只有极少数的研究记录了它们的差异。由于城市贫民窟具有独特的光谱特征，它不能简单地归类为其他城市（建筑物，植被和道路）特征。我们探索了基于膨胀核的深度卷积神经网络（DK-DCNN）方法的潜力，该方法可用于学习区分性空间特征和自动检测其他特征中的贫民窟。然而，所提出的DK-DCNN获得的结果达到了高精度。非正式住区的开放区域和正式住区的屋顶结构被错误地分类为贫民窟。基于数学形态学的形态空间格局分析被用作后处理方法，以提高分类的准确性。印度南部马杜赖市和蒂鲁普布尔市的WorldView-2传感器（1.84 m）捕获的四个截然不同的超高分辨率卫星图像，