利用不同指数和深度神经网络(DNN)模型提取建筑密集区的方法

IF 3.1 3区 物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION
Waseem Ahmad Ismaeel, J Satish Kumar
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引用次数: 0

摘要

利用遥感数据绘制城市化和建筑密集区地图是一项艰巨的挑战,尤其是当建筑密集区的光谱反射率与其他土地类型相交时。为此,人们开发了许多光谱指数。本文采用了多种指数:归一化差异建成区指数 (NDBI)、建成区提取指数 (BAEI)、归一化建成区指数 (NBAI)、新建成区指数 (NBI)、修正建成区指数 (MBI)、建成区波段比 (BRBA) 和归一化差异植被指数 (NDVI) 来划分建成区。BAEI、NBAI 和 NDVI 之间的交叉方法完善了这一方法,最终得到的建成区地图准确率为 92.5%,Kappa 系数为 0.848。随后,在该地图上训练的深度神经网络(DNN)模型从大地遥感卫星 5 号图像预测建成区的准确率超过 95%,最终建成区地图的总体准确率达到 92%,Kappa 系数为 0.85。所提出的方法展示了时间序列分析的效率,并解决了建筑密集区的误分类问题。此外,当细致的训练和验证过程纳入更精确的样本数据集时,DNN 模型的优化证明是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An approach for built-up area extraction using different indices and deep neural network (DNN) model

Mapping urbanization and built-up areas from remotely sensed data poses a formidable challenge, particularly when the spectral reflectance of built-up regions intersects with other land types. To address this, numerous spectral indices have been developed. This paper utilizes multiple indices: Normalized Difference Built-up Index (NDBI), Built-up Area Extraction Index (BAEI), Normalized Built-up Area Index (NBAI), New Built-up Index (NBI), Modified Built-up Index (MBI), Band Ratio for Built-up Area (BRBA), and Normalized Difference Vegetation Index (NDVI) to delineate built-up regions. An intersection approach between BAEI, NBAI, and NDVI refines the methodology, resulting in a final built-up map with 92.5 % accuracy and a 0.848 Kappa coefficient. Subsequently, a Deep Neural Network (DNN) model trained on this map achieves over 95 % accuracy in predicting built-up areas from Landsat 5 imagery, and the resultant built-up map achieved an overall accuracy of 92 % and a Kappa coefficient of 0.85. The proposed methodology demonstrates efficiency for time-series analysis and addresses misclassification in built-up areas. Moreover, the optimization of the DNN model proves effective when meticulous training and validation processes incorporate more precise sample datasets.

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来源期刊
CiteScore
5.70
自引率
12.10%
发文量
400
审稿时长
67 days
期刊介绍: The Journal covers the entire field of infrared physics and technology: theory, experiment, application, devices and instrumentation. Infrared'' is defined as covering the near, mid and far infrared (terahertz) regions from 0.75um (750nm) to 1mm (300GHz.) Submissions in the 300GHz to 100GHz region may be accepted at the editors discretion if their content is relevant to shorter wavelengths. Submissions must be primarily concerned with and directly relevant to this spectral region. Its core topics can be summarized as the generation, propagation and detection, of infrared radiation; the associated optics, materials and devices; and its use in all fields of science, industry, engineering and medicine. Infrared techniques occur in many different fields, notably spectroscopy and interferometry; material characterization and processing; atmospheric physics, astronomy and space research. Scientific aspects include lasers, quantum optics, quantum electronics, image processing and semiconductor physics. Some important applications are medical diagnostics and treatment, industrial inspection and environmental monitoring.
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