A Strategy of Rapid Extraction of Built-Up Area Using Multi-Seasonal Landsat-8 Thermal Infrared Band 10 Images

Zhang, Ping; Sun, Qiangqiang; Liu, Ming; Li, Jing; Sun, Deyong

doi:10.3390/rs9111126

Cited by 19 publications

(15 citation statements)

References 47 publications

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“…In the case of VgNIR-BI, the threshold must separate the (1) built-up class from the (2) non-built-up and water classes. Previous studies have utilized the use of Otsu thresholding method (Otsu, 1979) especially for VgNIR-BI where good results were obtained (Estoque and Murayama, 2015;Zhang et al, 2017). Initial results of Otsu-thresholding with RapidEye and PlanetScope data were compared with visible RGB images and adjustment of values were applied.…”

Section: Optimal Thresholdingmentioning

confidence: 99%

Spatiotemporal Multi-Satellite Biophysical Data Analysis of the Effect of Urbanization on Land Surface and Air Temperature in Baguio City, Philippines

Baloloy

Ana

Cruz

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Urbanization can be observed through the occurrence of land-use changes as more land is being transformed and developed for urban use. One of the Philippine cities with high rate of urbanization is Baguio City, known for having a subtropical highland climate. To understand the spatiotemporal relationship between urbanization and temperature, this study aims to analyze the correlation of urban extent with land surface and air temperature in Baguio City using satellite-based built-up extents, land surface temperature (LST) maps, and weather station-recorded air temperature data. Built-up extent layers were derived from three satellite images: Landsat, RapidEye and PlanetScope. Land-use land cover (LULC) maps were generated from Landsat images using biophysical indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI); while RapidEye and PlanetScope built-up extent maps were generated by applying the visible green-based built-up index (VgNIR-BI). Mean LST values from 1988 to 2018 during the dry and wet seasons were calculated from the Landsat-retrieved surface temperature layers. The result of the study shows that the increase in the built-up extent significantly intensified the LST during the dry season which was observed in all satellite data-derived built-up maps: RapidEye+PlanetScope (2012 r = 0.88), Landsat 8 (2012 r = 0.88), Landsat 8 ( -2018 r = 0.63) and Landsat 5,7,8 (1988-2018; r = 0.61). The main LST hotspots were detected inside the Central Business District where it expanded gradually from year 1998 (43ha) to 2011 (83ha), but have increased extensively within the years 2014 to 2019 (305 ha). On average, 98.5% of the hotspots detected from 1995 to 2019 are within the equivalent built-up area.

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Section: Optimal Thresholdingmentioning

confidence: 99%

Spatiotemporal Multi-Satellite Biophysical Data Analysis of the Effect of Urbanization on Land Surface and Air Temperature in Baguio City, Philippines

Baloloy

Ana

Cruz

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…In smaller regions, research on built-up area extraction methods has become a popular topic. For example, Zhang et al [57] proposed an empirical normalized difference of a seasonal brightness temperature index (NDSTI) for enhancing a built-up area based on the contrast heat emission seasonal response of the area to solar radiation and adopted a decision tree classification method for the rapid and accurate extraction of the area. Goldblatt et al [5] presented an efficient and low-cost machine-learning approach for the pixel-based image classification of built-up areas at a large geographic scale using Landsat data.…”

Section: Built-up Area Extraction From Medium-resolution Imagesmentioning

confidence: 99%

A Comprehensive Evaluation of Approaches for Built-Up Area Extraction from Landsat OLI Images Using Massive Samples

Zhang

Tang

2018

Remote Sensing

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Detailed information about built-up areas is valuable for mapping complex urban environments. Although a large number of classification algorithms for such areas have been developed, they are rarely tested from the perspective of feature engineering and feature learning. Therefore, we launched a unique investigation to provide a full test of the Operational Land Imager (OLI) imagery for 15-m resolution built-up area classification in 2015, in Beijing, China. Training a classifier requires many sample points, and we proposed a method based on the European Space Agency's (ESA) 38-m global built-up area data of 2014, OpenStreetMap, and MOD13Q1-NDVI to achieve the rapid and automatic generation of a large number of sample points. Our aim was to examine the influence of a single pixel and image patch under traditional feature engineering and modern feature learning strategies. In feature engineering, we consider spectra, shape, and texture as the input features, and support vector machine (SVM), random forest (RF), and AdaBoost as the classification algorithms. In feature learning, the convolutional neural network (CNN) is used as the classification algorithm. In total, 26 built-up land cover maps were produced. The experimental results show the following: (1) The approaches based on feature learning are generally better than those based on feature engineering in terms of classification accuracy, and the performance of ensemble classifiers (e.g., RF) are comparable to that of CNN. Two-dimensional CNN and the 7-neighborhood RF have the highest classification accuracies at nearly 91%; (2) Overall, the classification effect and accuracy based on image patches are better than those based on single pixels. The features that can highlight the information of the target category (e.g., PanTex (texture-derived built-up presence index) and enhanced morphological building index (EMBI)) can help improve classification accuracy. The code and experimental results are available at https://github.com/zhangtao151820/CompareMethod. challenge for remote sensing due to the high spatial frequency and heterogeneity of surface features. Various algorithms have been applied to extract built-up areas, including supervised classification, unsupervised clustering, and reinforcement learning [9][10][11]. In the process of built-up area extraction, the most important considerations are how to design or learn better features to characterize the buildings and how to choose a more appropriate classification strategy. Li et al. [12] tested two unsupervised and 13 supervised classification algorithms to distinguish urban land in Guangzhou City and then assessed all the algorithms in a per-pixel classification decision experiment and all the supervised algorithms in a segment-based experiment. Momeni et al. [13] compared the influence of spatial resolution, spectral band set, and the classification approach for mapping detailed urban land cover in Nottingham, UK. A WorldView-2 image provides the basis for a set of 12 images with variable spatial and s...

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“…In the smaller region, research on built-up area extraction methods has become a popular topic. Ping Zhang [49] proposed an empirical normalized difference of a seasonal brightness temperature index (NDSTI) for enhancing a built-up area based on the contrast heat emission seasonal response of a built-up area to solar radiation, and adopted a decision tree classification method for the rapid and accurate extraction of the built-up area. Ran Goldblatta [10] presented an efficient and low-cost machine-learning approach for pixel-based image classification of built-up areas at a large geographic scale using Landsat data.…”

Section: Built-up Area Extraction From Medium-resolution Imagesmentioning

confidence: 99%

A Comprehensive Evaluation of Approaches for Built-up Area Extraction from Landsat OLI Images Using Massive Samples

Zhang¹,

Tang²

2018

Preprint

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Detailed built-up area information is valuable for mapping complex urban environments. Although a large number of classification algorithms about built-up areas have been developed, they are rarely tested from the perspective of feature engineering and feature learning. Therefore we launched a unique investigation to provide a full test of the OLI imagery for 15-m resolution built-up area classification in 2015, in Beijing, China. Training a classifier requires many sample points, and we propose a method based on the ESA's 38-meter global built-up area data of 2014, Open Street Map and MOD13Q1-NDVI to achieve rapid and automatic generation of a large number of sample points. Our aim is to examine the influence of a single pixel and image patch under traditional feature engineering and modern feature learning strategies. In feature engineering, we consider spectra, shape and texture as the input features, and SVM, random forest (RF) and AdaBoost as the classification algorithms. In feature learning, the convolution neural network (CNN) is used as the classification algorithm. In total, 26 built-up land cover maps were produced. Experimental results show that: (1) the approaches based on feature learning are generally better than those based on feature engineering in terms of classification accuracy, and the performance of ensemble classifiers e.g., RF, is comparable to that of CNN. Two dimensional CNN and the 7 neighborhood RF have the highest classification accuracy of nearly 91%. (2) Overall, the classification effect and accuracy based on image patches are better than those based on single pixels. The features that can highlight the information of the target category (for example, PanTex and EMBI) can help improve classification accuracy.

show abstract

A Strategy of Rapid Extraction of Built-Up Area Using Multi-Seasonal Landsat-8 Thermal Infrared Band 10 Images

Cited by 19 publications

References 47 publications

Spatiotemporal Multi-Satellite Biophysical Data Analysis of the Effect of Urbanization on Land Surface and Air Temperature in Baguio City, Philippines

Spatiotemporal Multi-Satellite Biophysical Data Analysis of the Effect of Urbanization on Land Surface and Air Temperature in Baguio City, Philippines

A Comprehensive Evaluation of Approaches for Built-Up Area Extraction from Landsat OLI Images Using Massive Samples

A Comprehensive Evaluation of Approaches for Built-up Area Extraction from Landsat OLI Images Using Massive Samples

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