Abstract

Hyperspectral remote sensing is opening new avenues for multitude of urban applications. This paper extends target detection method for extraction of engineered surfaces or urban targets, particularly roads and roofs. The study involves application of deep learning using AVIRIS-NG data. In pre-processing, generating ground reference image using Vertex Component Analysis (VCA) is done instead of using it for spectral unmixing of mixed pixels. Principal Component analysis (PCA) is carried out at a scale of 30,40 and 50 components for dimensionality reduction followed by implementation of Convolution Neural Network (CNN) for three window sizes (5,7 and 9). This deep learning measure is effective for high prediction and the results appear significantly higher in comparison to the literature. The time complexity increases with increase in PCA components and window size, making a compromise with accuracy. The study analyses least explored subset of AVIRIS-NG hyperspectral data of Udaipur region (India) to assist urbanisation.

摘要

高光谱遥感正在为众多城市应用开辟新的途径。本文扩展了目标检测方法，用于提取工程表面或城市目标，特别是道路和屋顶。该研究涉及使用 AVIRIS-NG 数据的深度学习应用。在预处理中，使用顶点分量分析 (VCA) 生成地面参考图像，而不是将其用于混合像素的光谱解混。主成分分析 (PCA) 以 30,40 和 50 个成分的规模进行降维，然后针对三种窗口大小（5,7 和 9）实施卷积神经网络 (CNN)。这种深度学习措施对于高预测是有效的，与文献相比，结果明显更高。时间复杂度随着 PCA 组件和窗口大小的增加而增加，从而影响准确性。该研究分析了乌代浦地区（印度）的 AVIRIS-NG 高光谱数据的探索最少的子集，以协助城市化。