Deep Learning-Based Building Extraction from Remote Sensing Images: A Comprehensive Review

Luo, Lin; Li, Pengpeng; Yan, Xuesong

doi:10.3390/en14237982

Cited by 50 publications

(38 citation statements)

References 113 publications

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“…Automatic building detection from remote sensing has been widely debated in the scientific literature and a detailed review is beyond the scope of the present paper. Readers are referred to recent review papers [4][5][6]. Here, only contributions where the classification of roofing materials is considered (alongside the problem of roof detection) are briefly discussed.…”

Section: Introductionmentioning

confidence: 99%

An Object-Oriented Approach to the Classification of Roofing Materials Using Very High-Resolution Satellite Stereo-Pairs

et al. 2022

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The availability of multispectral images, with both high spatial and spectral resolution, makes it possible to obtain valuable information about complex urban environment, reducing the need for more expensive surveying techniques. Here, a methodology is tested for the semi-automatic extraction of buildings and the mapping of the main roofing materials over a urban area of approximately 100 km2, including the entire city of Bologna (Italy). The methodology follows an object-oriented approach and exploits a limited number of training samples. After a validation based on field inspections and close-range photos acquired by a drone, the final map achieved an overall accuracy of 94% (producer accuracy 79%) regarding the building extraction and of 91% for the classification of the roofing materials. The proposed approach proved to be flexible enough to catch the strong variability of the urban texture in different districts and can be easily reproducible in other contexts, as only satellite imagery is required for the mapping.

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Section: Introductionmentioning

confidence: 99%

An Object-Oriented Approach to the Classification of Roofing Materials Using Very High-Resolution Satellite Stereo-Pairs

et al. 2022

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“…Remote sensing has been widely utilized to identify impervious surfaces. Several articles have been published describing the state-of-the-art of this topic [13][14][15][16][17][18][19][20][21]. Earlier, statistical remote sensing indices including the Normalized Difference Built-up Index (NDBI) [22], Normalized Difference Impervious Surface Index (NDISI) [23], modified NDISI [24], and perpendicular impervious surface index(PISI) [25] , biophysical composition index (BCI) [26] , and the normalized difference vegetation index (NDVI) have been developed to map impervious surfaces.…”

Section: Introductionmentioning

confidence: 99%

Impervious Surface Prediction in Marrakech City using Artificial Neural Network

Mahyoub¹,

Rhinane²,

Mansour³

et al. 2022

IJACSA

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Determining an impervious surface is one of the most important topics of remote sensing because of its great role in providing information that benefits decision-makers in urban planning, sustainable development goals, and environmental protection. In recent years, a great development in this field has occurred due to the huge improvement in the algorithms and techniques that are used to map impervious surfaces. In this paper, the deep learning technique has been implemented to investigate the extraction of impervious surfaces in Marrakesh city, based on Landsat images. 9000 polygons and 13840 points have been taken to prepare label data by random forest in Google Earth Engine (GEE). In addition, all preprocessing steps for remote sensing images have been implemented in GEE. An artificial neural network (ANN) has been used to determine impervious surfaces. After training and testing the proposed network on Landsat image datasets, precision, accuracy, recall, and F1-score matrix scores were 0.79, 0.98, 0.87, and 0.82, respectively. The experimental results show that this method is efficient and precise for mapping the impervious surfaces of Marrakesh city.

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“…The co-register of images captured from different devices has generated research interest in applications such as building extraction [1], image classification [2], 3D city modeling [3], land cover change detection [4], and image fusion [5], radiometric correction [6], [7], among others.…”

Section: Introductionmentioning

confidence: 99%

Coregistration of Remote Sensing Image Based on Histogram Kernel Predictability

Hugo¹,

Aranda²,

Mejia-Zuluaga³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Registration of remote sensing images has been approached using different strategies; one of the most popular is based on similarity measures. There are different measures of similarity in the literature: Normalized Cross-Correlation (NCC), Mutual Information (MI), etc. Normalized Mutual Information (NMI) has received the most attention in image processing; among the most important limitations are its high computational cost and lack of robustness to strong radiometric changes. For this reason, in this work, we introduce a co-registration approach based on the Histogram Kernel Predictability (HKP). This formulation reduces numerical errors and requires less computing time in comparison to NMI. To the best of our knowledge, this is the first work for registering any remote sensing images by using HKP. Additionally, we propose to use an algorithm based on meta-heuristics called Evolutionary Centers Algorithm (ECA), which allows having fewer iterations to solve the registration problem. In addition, we incorporate a parallelization scheme that permits reducing processing times. The results show that our proposal can solve co-registration problems that the NMI cannot solve while obtaining competitive computational times and registration errors comparable with other existing works in the literature. The HKP approach solves most of all the transformations of a set of simulated registration problems, while the NMI, in some cases, only solves half of the registration problems. Moreover, we compare our approach with feature-based methods in real datasets. This research presents an alternative to remote sensing problems where MI has traditionally been used.

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Deep Learning-Based Building Extraction from Remote Sensing Images: A Comprehensive Review

Cited by 50 publications

References 113 publications

An Object-Oriented Approach to the Classification of Roofing Materials Using Very High-Resolution Satellite Stereo-Pairs

An Object-Oriented Approach to the Classification of Roofing Materials Using Very High-Resolution Satellite Stereo-Pairs

Impervious Surface Prediction in Marrakech City using Artificial Neural Network

Coregistration of Remote Sensing Image Based on Histogram Kernel Predictability

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