Abstract

Geographic object-based image analysis (GEOBIA) has emerged as an effective and evolving paradigm for analyzing very high resolution (VHR) images as it demonstrates preeminence over the traditional pixel-wise methods and enables the utilization of diverse spectral, geometrical, and textural information to for image classification. Among feature selection (FS) methods, metaheuristic FS techniques have recently demonstrated effective performance in the dimensionality reduction of GEOBIA features. In this study, an artificial neural network (ANN) was integrated with particle swarm optimization (PSO) to enhance the learning process and more effectively determine the most significant features and their importance using WorldView-3 (WV-3) satellite data. First, multi-resolution image segmentation parameters were tuned using Taguchi optimization technique and unsupervised segmentation quality measure. Second, the proposed ANN–PSO was compared with PSO under 100 iterations. The ANN–PSO integration achieved lower root mean square error (RMSE) in all the iterations. Third, state-of-the-art extreme gradient boosting (Xgboost) image classifier was used to derive the land use/land cover (LULC) map of the first study area and assess the transferability of the selected features on the second and third regions. The Xgboost classifier obtained 91.68%, 89.54%, and 89.33% overall accuracies for the first, second, and third sites, respectively. ANN contributed to an intelligent approach for identifying which features are more likely to be relevant and discriminate the land cover types. The proposed integrated FS is a promising approach and an efficient tool for determining significant features and enhancing the detection of urban LULC classes from WV-3 data.

摘要

基于地理对象的图像分析 (GEOBIA) 已成为分析超高分辨率 (VHR) 图像的一种有效且不断发展的范例，因为它展示了优于传统像素方法的优势，并能够利用各种光谱、几何和纹理信息用于图像分类。在特征选择（FS）方法中，元启发式 FS 技术最近在 GEOBIA 特征的降维方面表现出有效的性能。在这项研究中，人工神经网络 (ANN) 与粒子群优化 (PSO) 相结合，以增强学习过程，并使用 WorldView-3 (WV-3) 卫星数据更有效地确定最重要的特征及其重要性。第一的，使用田口优化技术和无监督分割质量测量来调整多分辨率图像分割参数。其次，将建议的 ANN-PSO 与 100 次迭代下的 PSO 进行比较。ANN-PSO 集成在所有迭代中实现了较低的均方根误差 (RMSE)。第三，使用最先进的极端梯度增强（Xgboost）图像分类器来推导第一个研究区域的土地利用/土地覆盖（LULC）图，并评估所选特征在第二和第三区域的可迁移性. Xgboost 分类器对第一、第二和第三站点的总体准确率分别为 91.68%、89.54% 和 89.33%。ANN 促成了一种智能方法，用于识别哪些特征更可能相关并区分土地覆盖类型。