Asbestos has been used extensively in several applications. Once it is known as a dangerous mineral, its usage has been prohibited and its identification and remediation play a very important role from the health safety point of view. Nowadays, deep learning techniques are used in many applications, especially for image analysis. They can be used to significantly reduce the time and cost of traditional detection methods. In this paper, taking advantage of asbestos spectral signature, a deep neural network is introduced in order to implement a complete methodology to identify asbestos roofings starting from hyperspectral images in a regional context. The novelty of the proposed approach is a dynamic mixing of models with different features, in order to accommodate classifications on widespread areas of both urban and rural territories. Indeed, the dataset used during the experiments described in this paper is a large one, consisting of many wide hyperspectral images with a geometric resolution of 1 m and with 186 bands, covering an entire region of approximately 8,000 . This is in contrast to other works in the literature where the analyzed areas are limited in size and uniform for physical features.

中文翻译：

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一种动态神经网络模型，用于在覆盖大面积区域的高光谱图像中识别石棉屋顶


石棉已广泛用于多种应用。一旦它被认定为危险矿物，就被禁止使用，从健康安全的角度来看，它的识别和补救起着非常重要的作用。如今，深度学习技术被用于许多应用，尤其是图像分析。它们可用于显著减少传统检测方法的时间和成本。在本文中，利用石棉光谱特征，引入了一个深度神经网络，以实现一种完整的方法，从区域背景下的高光谱图像开始识别石棉屋顶。所提出的方法的新颖之处在于具有不同特征的模型的动态混合，以适应对城市和农村地区广泛区域的分类。事实上，本文描述的实验中使用的数据集是一个很大的数据集，由许多几何分辨率为 1 m 和 186 个波段的宽高光谱图像组成，覆盖了大约 8,000 个的整个区域。这与文献中的其他工作形成鲜明对比，在这些工作中，分析区域的大小有限且物理特征一致。