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Optical Remote Sensing Applications in Urban Areas

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 48791

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Special Issue Editors

Dr. Saeid Homayouni

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Guest Editor

Centre Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), Quebec City, QC, Canada
Interests: geomatics, remote sensing, and the analysis of optical and synthetic aperture radar Earth observations through artificial intelligence and machine learning approaches for urban and agro-environmental applications

Dr. Ying Zhang

E-Mail Website
Guest Editor

Canada Centre for Mapping and Earth Observation, Natural Resources Canada, Ottawa, ON K1S 5K2, Canada
Interests: optical remote sensing technology development and EO-based spatial analysis for applications related to urban and mining development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban areas have been the center of human settlement and civilization. They also play essential roles in various aspects of human life, including economic, political, cultural, and educational activities. On the other hand, these areas are physically and geographically complex systems and phenomena due to the presence and integration of various elements such as residential, industrial, infrastructure, road networks, green spaces, and water bodies. As such, these phenomena are the study subject of experts and researchers in different fields, from social to physical sciences and engineering. In particular, the physical characteristics of an urban area are essential for various applications in geography, sustainable development, urban planning, and civil engineering. Geospatial information, with different levels of details at local and regional scales, can provide a valuable source of information to reach the ultimate objectives of urban studies.

Remote sensing technology and techniques are among the most effective observation and analysis tools for provision of the geospatial information about urban land complexes. From the beginning of the remote sensing era, aerial photography has provided unprecedented views of the urban area. In addition, Earth observation (EO) systems, such as Landsat satellites, have acquired unique and valuable spatial, spectral, and temporal information of surfaces of the planet, including urban areas. This collection of EOs has progressively continued and been improved by new operational spaceborne, airborne, and drone imagery, as well as optical, lidar, thermal, and radar data sources. In addition, technology revolutions related to open data and informatics resources, big data, and cloud computing platforms bring both opportunities and challenges for the user and the academic community in urban studies.

The main objective of this Special Issue (SI) of the Remote Sensing journal is to promote recent thematic research and development applications and state-of-the-art outcomes and results based on optical Earth observations. For this SI, we invite researchers with different expertise and interest to consider this opportunity and submit their papers on both applications and methodologies on “Optical Remote Sensing for Urban Area.”

Dr. Saeid Homayouni
Dr. Ying Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Spatiotemporal analysis of urban land
Urban natural/manmade hazards
Land-cover mapping
Land-cover land-use changes (LCLUC) and modeling
Urban feature detection and extraction
Urbanization impacts and sustainable development
Change detection
Green space monitoring
3D mapping and modeling from remote sensing data
Big data
Data mining
Image processing
Machine and deep learning
Object-based image analysis

Published Papers (14 papers)

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Research

29 pages, 38336 KiB

Open AccessArticle

Change Detection of Building Objects in High-Resolution Single-Sensor and Multi-Sensor Imagery Considering the Sun and Sensor’s Elevation and Azimuth Angles

by Sejung Jung, Won Hee Lee and Youkyung Han

Remote Sens. 2021, 13(18), 3660; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183660 - 13 Sep 2021

Cited by 3 | Viewed by 2090

Abstract

Building change detection is a critical field for monitoring artificial structures using high-resolution multitemporal images. However, relief displacement depending on the azimuth and elevation angles of the sensor causes numerous false alarms and misdetections of building changes. Therefore, this study proposes an effective object-based building change detection method that considers azimuth and elevation angles of sensors in high-resolution images. To this end, segmentation images were generated using a multiresolution technique from high-resolution images after which object-based building detection was performed. For detecting building candidates, we calculated feature information that could describe building objects, such as rectangular fit, gray-level co-occurrence matrix (GLCM) homogeneity, and area. Final building detection was then performed considering the location relationship between building objects and their shadows using the Sun’s azimuth angle. Subsequently, building change detection of final building objects was performed based on three methods considering the relationship of the building object properties between the images. First, only overlaying objects between images were considered to detect changes. Second, the size difference between objects according to the sensor’s elevation angle was considered to detect the building changes. Third, the direction between objects according to the sensor’s azimuth angle was analyzed to identify the building changes. To confirm the effectiveness of the proposed object-based building change detection performance, two building density areas were selected as study sites. Site 1 was constructed using a single sensor of KOMPSAT-3 bitemporal images, whereas Site 2 consisted of multi-sensor images of KOMPSAT-3 and unmanned aerial vehicle (UAV). The results from both sites revealed that considering additional shadow information showed more accurate building detection than using feature information only. Furthermore, the results of the three object-based change detections were compared and analyzed according to the characteristics of the study area and the sensors. Accuracy of the proposed object-based change detection results was achieved over the existing building detection methods. Full article

(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)

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32 pages, 11818 KiB

Open AccessArticle

Understanding Spatio-Temporal Patterns of Land Use/Land Cover Change under Urbanization in Wuhan, China, 2000–2019

by Han Zhai, Chaoqun Lv, Wanzeng Liu, Chao Yang, Dasheng Fan, Zikun Wang and Qingfeng Guan

Remote Sens. 2021, 13(16), 3331; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163331 - 23 Aug 2021

Cited by 81 | Viewed by 5014

Abstract

Exploring land use structure and dynamics is critical for urban planning and management. This study attempts to understand the Wuhan development mode since the beginning of the 21st century by profoundly investigating the spatio-temporal patterns of land use/land cover (LULC) change under urbanization in Wuhan, China, from 2000 to 2019, based on continuous time series mapping using Landsat observations with a support vector machine. The results indicated rapid urbanization, with large LULC changes triggered. The built-up area increased by 982.66 km² (228%) at the expense of a reduction of 717.14 km² (12%) for cropland, which threatens food security to some degree. In addition, the natural habitat shrank to some extent, with reductions of 182.52 km², 23.92 km² and 64.95 km² for water, forest and grassland, respectively. Generally, Wuhan experienced a typical urbanization course that first sped up, then slowed down and then accelerated again, with an obvious internal imbalance between the 13 administrative districts. Hanyang, Hongshan and Dongxihu specifically presented more significant land dynamicity, with Hanyang being the active center. Over the past 19 years, Wuhan mainly developed toward the east and south, with the urban gravity center transferred from the northwest to the southeast of Jiang’an district. Lastly, based on the predicted land allocation of Wuhan in 2029 by the patch-generating land use simulation (PLUS) model, the future landscape dynamic pattern was further explored, and the result shows a rise in the northern suburbs, which provides meaningful guidance for urban planners and managers to promote urban sustainability. Full article

(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)

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19 pages, 8146 KiB

Open AccessEditor’s ChoiceArticle

A Novel Framework for Rapid Detection of Damaged Buildings Using Pre-Event LiDAR Data and Shadow Change Information

by Ying Zhang, Matthew Roffey and Sylvain G. Leblanc

Remote Sens. 2021, 13(16), 3297; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163297 - 20 Aug 2021

Cited by 4 | Viewed by 2826

Abstract

After a major earthquake in a dense urban area, the spatial distribution of heavily damaged buildings is indicative of the impact of the event on public safety. Timely assessment of the locations of severely damaged buildings and their damage morphologies using remote sensing approaches is critical for search and rescue actions. Detection of damaged buildings that did not suffer collapse can be highly challenging from aerial or satellite optical imagery, especially those structures with height-reduction or inclination damage and apparently intact roofs. A key information cue can be provided by a comparison of predicted building shadows based on pre-event building models with shadow estimates extracted from post-event imagery. This paper addresses the detection of damaged buildings in dense urban areas using the information of building shadow changes based on shadow simulation, analysis, and image processing in order to improve real-time damage detection and analysis. A novel processing framework for the rapid detection of damaged buildings without collapse is presented, which includes (a) generation of building digital surface models (DSMs) from pre-event LiDAR data, (b) building shadow detection and extraction from imagery, (c) simulation of predicted building shadows utilizing building DSMs, and (d) detection and identification of shadow areas exhibiting significant pre- and post-event differences that can be attributed to building damage. The framework is demonstrated through two simulated case studies. The building damage types considered are those typically observed in earthquake events and include height-reduction, over-turn collapse, and inclination. Total collapse cases are not addressed as these are comparatively easy to be detected using simpler algorithms. Key issues are discussed including the attributes of essential information layers and sources of error influencing the accuracy of building damage detection. Full article

(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)

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21 pages, 6913 KiB

Open AccessArticle

Gabor Features Extraction and Land-Cover Classification of Urban Hyperspectral Images for Remote Sensing Applications

by Clara Cruz-Ramos, Beatriz P. Garcia-Salgado, Rogelio Reyes-Reyes, Volodymyr Ponomaryov and Sergiy Sadovnychiy

Remote Sens. 2021, 13(15), 2914; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152914 - 24 Jul 2021

Cited by 16 | Viewed by 2124

Abstract

The principles of the transform stage of the extract, transform and load (ETL) process can be applied to index the data in functional structures for the decision-making inherent in an urban remote sensing application. This work proposes a method that can be utilised as an organisation stage by reducing the data dimension with Gabor texture features extracted from grey-scale representations of the Hue, Saturation and Value (HSV) colour space and the Normalised Difference Vegetation Index (NDVI). Additionally, the texture features are reduced using the Linear Discriminant Analysis (LDA) method. Afterwards, an Artificial Neural Network (ANN) is employed to classify the data and build a tick data matrix indexed by the belonging class of the observations, which could be retrieved for further analysis according to the class selected to explore. The proposed method is compared in terms of classification rates, reduction efficiency and training time against the utilisation of other grey-scale representations and classifiers. This method compresses up to 87% of the original features and achieves similar classification results to non-reduced features but at a higher training time. Full article

(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)

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18 pages, 5177 KiB

Open AccessArticle

An Efficient Multi-Sensor Remote Sensing Image Clustering in Urban Areas via Boosted Convolutional Autoencoder (BCAE)

by Maryam Rahimzad, Saeid Homayouni, Amin Alizadeh Naeini and Saeed Nadi

Remote Sens. 2021, 13(13), 2501; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132501 - 26 Jun 2021

Cited by 14 | Viewed by 4066

Abstract

High-resolution urban image clustering has remained a challenging task. This is mainly because its performance strongly depends on the discrimination power of features. Recently, several studies focused on unsupervised learning methods by autoencoders to learn and extract more efficient features for clustering purposes. This paper proposes a Boosted Convolutional AutoEncoder (BCAE) method based on feature learning for efficient urban image clustering. The proposed method was applied to multi-sensor remote-sensing images through a multistep workflow. The optical data were first preprocessed by applying a Minimum Noise Fraction (MNF) transformation. Then, these MNF features, in addition to the normalized Digital Surface Model (nDSM) and vegetation indexes such as Normalized Difference Vegetation Index (NDVI) and Excess Green (ExG(2)), were used as the inputs of the BCAE model. Next, our proposed convolutional autoencoder was trained to automatically encode upgraded features and boost the hand-crafted features for producing more clustering-friendly ones. Then, we employed the Mini Batch K-Means algorithm to cluster deep features. Finally, the comparative feature sets were manually designed in three modes to prove the efficiency of the proposed method in extracting compelling features. Experiments on three datasets show the efficiency of BCAE for feature learning. According to the experimental results, by applying the proposed method, the ultimate features become more suitable for clustering, and spatial correlation among the pixels in the feature learning process is also considered. Full article

(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)

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20 pages, 9304 KiB

Open AccessArticle

Building Extraction from Very-High-Resolution Remote Sensing Images Using Semi-Supervised Semantic Edge Detection

by Liegang Xia, Xiongbo Zhang, Junxia Zhang, Haiping Yang and Tingting Chen

Remote Sens. 2021, 13(11), 2187; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112187 - 03 Jun 2021

Cited by 24 | Viewed by 4250

Abstract

The automated detection of buildings in remote sensing images enables understanding the distribution information of buildings, which is indispensable for many geographic and social applications, such as urban planning, change monitoring and population estimation. The performance of deep learning in images often depends on a large number of manually labeled samples, the production of which is time-consuming and expensive. Thus, this study focuses on reducing the number of labeled samples used and proposing a semi-supervised deep learning approach based on an edge detection network (SDLED), which is the first to introduce semi-supervised learning to the edge detection neural network for extracting building roof boundaries from high-resolution remote sensing images. This approach uses a small number of labeled samples and abundant unlabeled images for joint training. An expert-level semantic edge segmentation model is trained based on labeled samples, which guides unlabeled images to generate pseudo-labels automatically. The inaccurate label sets and manually labeled samples are used to update the semantic edge model together. Particularly, we modified the semantic segmentation network D-LinkNet to obtain high-quality pseudo-labels. Specifically, the main network architecture of D-LinkNet is retained while the multi-scale fusion is added in its second half to improve its performance on edge detection. The SDLED was tested on high-spatial-resolution remote sensing images taken from Google Earth. Results show that the SDLED performs better than the fully supervised method. Moreover, when the trained models were used to predict buildings in the neighboring counties, our approach was superior to the supervised way, with line IoU improvement of at least 6.47% and F1 score improvement of at least 7.49%. Full article

(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)

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17 pages, 8116 KiB

Open AccessArticle

City-Scale Mapping of Urban Façade Color Using Street-View Imagery

by Teng Zhong, Cheng Ye, Zian Wang, Guoan Tang, Wei Zhang and Yu Ye

Remote Sens. 2021, 13(8), 1591; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081591 - 20 Apr 2021

Cited by 22 | Viewed by 4458

Abstract

Precise urban façade color is the foundation of urban color planning. Nevertheless, existing research on urban colors usually relies on manual sampling due to technical limitations, which brings challenges for evaluating urban façade color with the co-existence of city-scale and fine-grained resolution. In this study, we propose a deep learning-based approach for mapping the urban façade color using street-view imagery. The dominant color of the urban façade (DCUF) is adopted as an indicator to describe the urban façade color. A case study in Shenzhen was conducted to measure the urban façade color using Baidu Street View (BSV) panoramas, with city-scale mapping of the urban façade color in both irregular geographical units and regular grids. Shenzhen’s urban façade color has a gray tone with low chroma. The results demonstrate that the proposed method has a high level of accuracy for the extraction of the urban façade color. In short, this study contributes to the development of urban color planning by efficiently analyzing the urban façade color with higher levels of validity across city-scale areas. Insights into the mapping of the urban façade color from the humanistic perspective could facilitate higher quality urban space planning and design. Full article

(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)

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25 pages, 5217 KiB

Open AccessArticle

Inconsistency among Landsat Sensors in Land Surface Mapping: A Comprehensive Investigation Based on Simulation

by Feng Chen, Chenxing Wang, Yuansheng Zhang, Zhenshi Yi, Qiancong Fan, Lin Liu and Yuejun Song

Remote Sens. 2021, 13(7), 1383; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071383 - 03 Apr 2021

Cited by 5 | Viewed by 2066

Abstract

Comprehensive investigations on the between-sensor comparability among Landsat sensors have been relatively limited compared with the increasing use of multi-temporal Landsat records in time series analyses. More seriously, the sensor-related difference has not always been considered in applications. Accordingly, comparisons were conducted among all Landsat sensors available currently, including Multispectral Scanner (MSS), Thematic Mappers (TM), Enhanced Thematic Mappers (ETM+), and Operational Land Imager (OLI)) in land cover mapping, based on a collection of synthesized, multispectral data. Compared to TM, OLI showed obvious between-sensor differences in channel reflectance, especially over the near infrared (NIR) and shortwave infrared (SWIR) channels, and presented positive bias in vegetation spectral indices. OLI did not always outperform TM and ETM+ in classification, which related to the methods used. Furthermore, the channels over SWIR of TM and its successors contributed largely to enhancement of inter-class separability and to improvement of classification. Currently, the inclusion of MSS data is confronted with significant challenges regarding the consistency of surface mapping. Considering the inconsistency among the Landsat sensors, it is applicable to generate a consistent time series of spectral indices through proper transformation models. Meanwhile, it suggests the generation of specific class(es) based on interest instead of including all classes simultaneously. Full article

(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)

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27 pages, 21854 KiB

Open AccessArticle

Shadow Detection and Compensation from Remote Sensing Images under Complex Urban Conditions

by Tingting Zhou, Haoyang Fu, Chenglin Sun and Shenghan Wang

Remote Sens. 2021, 13(4), 699; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040699 - 14 Feb 2021

Cited by 38 | Viewed by 6302

Abstract

Due to the block of high-rise objects and the influence of the sun’s altitude and azimuth, shadows are inevitably formed in remote sensing images particularly in urban areas, which causes missing information in the shadow region. In this paper, we propose a new method for shadow detection and compensation through objected-based strategy. For shadow detection, the shadow was highlighted by an improved shadow index (ISI) combined color space with an NIR band, then ISI was reconstructed by the objects acquired from the mean-shift algorithm to weaken noise interference and improve integrity. Finally, threshold segmentation was applied to obtain the shadow mask. For shadow compensation, the objects from segmentation were treated as a minimum processing unit. The adjacent objects are likely to have the same ambient light intensity, based on which we put forward a shadow compensation method which always compensates shadow objects with their adjacent non-shadow objects. Furthermore, we presented a dynamic penumbra compensation method (DPCM) to define the penumbra scope and accurately remove the penumbra. Finally, the proposed methods were compared with the stated-of-art shadow indexes, shadow compensation method and penumbra compensation methods. The experiments show that the proposed method can accurately detect shadow from urban high-resolution remote sensing images with a complex background and can effectively compensate the information in the shadow region. Full article

(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)

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20 pages, 12936 KiB

Open AccessArticle

Identification and Portrait of Urban Functional Zones Based on Multisource Heterogeneous Data and Ensemble Learning

by Nan Xu, Jiancheng Luo, Tianjun Wu, Wen Dong, Wei Liu and Nan Zhou

Remote Sens. 2021, 13(3), 373; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030373 - 21 Jan 2021

Cited by 18 | Viewed by 3264

Abstract

Urban functional zones are important space carriers for urban economic and social function. The accurate and rapid identification of urban functional zones is of great significance to urban planning and resource allocation. However, the factors considered in the existing functional zone identification methods are not comprehensive enough, and the recognition of functional zones stops at their categories. This paper proposes a framework that combines multisource heterogeneous data to identify the categories of functional zones and draw the portraits of functional zones. The framework comprehensively describes the features of functional zones from four aspects: building-level metrics, landscape metrics, semantic metrics, and human activity metrics, and uses a combination of ensemble learning and active learning to balance the identification accuracy of functional zones and the labeling cost during large-scale generalization. Furthermore, sentiment analysis, word cloud analysis, and land cover proportion maps are added to the portraits of typical functional zones to make the image of functional zones vivid. The experiment carried out within the Fifth Ring Road, Haidian District, Beijing, shows that the overall accuracy of the method reached 82.37% and the portraits of the four typical functional zones are clear. The method in this paper has good repeatability and generalization, which is helpful to carry out quantitative and objective research on urban functional zones. Full article

(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)

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19 pages, 10925 KiB

Open AccessArticle

Monitoring Spatiotemporal Changes of Impervious Surfaces in Beijing City Using Random Forest Algorithm and Textural Features

by Xuegang Dong, Zhiguo Meng, Yongzhi Wang, Yuanzhi Zhang, Haoteng Sun and Qingshuai Wang

Remote Sens. 2021, 13(1), 153; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010153 - 05 Jan 2021

Cited by 20 | Viewed by 3375

Abstract

As the capital city of China, Beijing has experienced unprecedented economic and population growth and dramatic impervious surface changes during the last few decades. An application of the classification method combining the spectral and textural features based on Random Forest was conducted to monitor the spatial and temporal changes of Beijing’s impervious surfaces. This classification strategy achieved excellent performance in the impervious surface extraction in complex urban areas, as the Kappa coefficient reached 0.850. Based on this strategy, the impervious surfaces inside Beijing’s sixth ring road in 1997, 2002, 2007, 2013, and 2017 were extracted. As the development of Beijing has a special regional feature, the changes of impervious surfaces within the sixth ring road were assessed. The findings are as follows: (1) the textural features can significantly improve the classification accuracy of land cover in urban areas, especially for the impervious surface with high albedo. (2) Impervious surfaces within the sixth ring road expanded dramatically from 1997 to 2017, had three expanding periods: 1997–2002, 2002–2007, and 2013–2017, and only shrank in 2007–2013. There are different possible major driving factors for each period. (3) The region between the fifth and sixth ring roads in Beijing underwent the most significant changes in the two decades. (4) The inner three regions are relatively highly urbanized areas compared to the outer two regions. Urbanization processes in the interior regions tend to be completed compared to the exterior regions. Full article

(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)

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20 pages, 10871 KiB

Open AccessArticle

A Novel Urban Composition Index Based on Water-Impervious Surface-Pervious Surface (W-I-P) Model for Urban Compositions Mapping Using Landsat Imagery

by Lihao Zhang, Yugang Tian and Qingwei Liu

Remote Sens. 2021, 13(1), 3; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010003 - 22 Dec 2020

Cited by 8 | Viewed by 2515

Abstract

Monitoring urban compositions spatially and temporally is a crucial issue for urban planning and management. Nowadays, remote sensing techniques have been widely applied for urban compositions extraction. Compared with other remote sensing techniques, spectral indices have significant advantages due to their parameter-free and easy implementation. However, existing indices cannot extract different urban compositions well, and some of them can only extract one composition with less attention to other urban compositions. In this study, based on the water- impervious surface-pervious surface (W-I-P) model, a novel urban composition index (UCI) was developed by analyzing the robust features from the global spectral samples. Additionally, a semi-empirical threshold of UCI was proposed to extract different urban compositions (water, impervious surface area and pervious surface area). Four cities of China were selected as study areas, Landsat-8 images and Google Earth images were used for quantitative analysis. Correlation analysis, separability analysis, and accuracy assessment were conducted among UCI and five other existed indices (single and multiple composition indices) at the urban and global scales. Results indicated that UCI had a stronger correlation with the ISA proportion and a higher separability between each urban composition. UCI also achieved the highest overall accuracy and Kappa coefficient in urban compositions extraction. The suggested semi-empirical threshold was also testified to be reliable and can be a reference for practical application. There is convincing evidence that UCI is a simple, efficient, and reliable index for urban compositions extraction. Full article

(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)

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21 pages, 12617 KiB

Open AccessArticle

Detection of Undocumented Building Constructions from Official Geodata Using a Convolutional Neural Network

by Qingyu Li, Yilei Shi, Stefan Auer, Robert Roschlaub, Karin Möst, Michael Schmitt, Clemens Glock and Xiaoxiang Zhu

Remote Sens. 2020, 12(21), 3537; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213537 - 28 Oct 2020

Cited by 9 | Viewed by 2596

Abstract

Undocumented building constructions are buildings or stories that were built years ago, but are missing in the official digital cadastral maps (DFK). The detection of undocumented building constructions is essential to urban planning and monitoring. The state of Bavaria, Germany, uses two semi-automatic detection methods for this task that suffer from a high false alarm rate. To solve this problem, we propose a novel framework to detect undocumented building constructions using a Convolutional Neural Network (CNN) and official geodata, including high resolution optical data and the Normalized Digital Surface Model (nDSM). More specifically, an undocumented building pixel is labeled as “building” by the CNN but does not overlap with a building polygon of the DFK. The class of old or new undocumented building can be further separated when a Temporal Digital Surface Model (tDSM) is introduced in the stage of decision fusion. In a further step, undocumented story construction is detected as the pixels that are “building” in both DFK and predicted results from CNN, but shows a height deviation from the tDSM. By doing so, we have produced a seamless map of undocumented building constructions for one-quarter of the state of Bavaria, Germany at a spatial resolution of 0.4 m, which has proved that our framework is robust to detect undocumented building constructions at large-scale. Considering that the official geodata exploited in this research is advantageous because of its high quality and large coverage, a transferability analysis experiment is also designed in our research to investigate the sampling strategies for building detection at large-scale. Our results indicate that building detection results in unseen areas at large-scale can be improved when training samples are collected from different districts. In an area where training samples are available, local training sampless collection and training can save much time and effort. Full article

(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)

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20 pages, 26007 KiB

Open AccessArticle

The Influence of CLBP Window Size on Urban Vegetation Type Classification Using High Spatial Resolution Satellite Images

by Zhou Chen, Xianyun Fei, Xiangwei Gao, Xiaoxue Wang, Huimin Zhao, Kapo Wong, Jin Yeu Tsou and Yuanzhi Zhang

Remote Sens. 2020, 12(20), 3393; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203393 - 16 Oct 2020

Cited by 3 | Viewed by 1881

Abstract

Urban vegetation can regulate ecological balance, reduce the influence of urban heat islands, and improve human beings’ mental state. Accordingly, classification of urban vegetation types plays a significant role in urban vegetation research. This paper presents various window sizes of completed local binary pattern (CLBP) texture features classifying urban vegetation based on high spatial-resolution WorldView-2 images in areas of Shanghai (China) and Lianyungang (Jiangsu province, China). To demonstrate the stability and universality of different CLBP window textures, two study areas were selected. Using spectral information alone and spectral information combined with texture information, imagery is classified using random forest (RF) method based on vegetation type, showing that use of spectral information with CLBP window textures can achieve 7.28% greater accuracy than use of only spectral information for urban vegetation type classification, with accuracy greater for single vegetation types than for mixed ones. Optimal window sizes of CLBP textures for grass, shrub, arbor, shrub-grass, arbor-grass, and arbor-shrub-grass are 3 × 3, 3 × 3, 11 × 11, 9 × 9, 9 × 9, 7 × 7 for urban vegetation type classification. Furthermore, optimal CLBP window size is determined by the roughness of vegetation texture. Full article

(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)

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