Semiautomatic extraction of building information and variation detection from high resolution remote sensing images

Wang, Yonggang; Liu, Huiping

doi:10.1117/12.712984

Cited by 4 publications

(2 citation statements)

References 6 publications

(2 reference statements)

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“…Building height is an important parameter for defining buildings. Unlike Three-Dimensional (3D) data, Two-dimensional (2D) data does not provide elevation related information (Zeng, 2014).…”

Section: Introductionmentioning

confidence: 99%

Automatic Building Footprint Extraction From High Resolution Stereo Satellite Image

Mia

Islam

2021

Plan Plus

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Automatic buildings footprint extraction is of great importance to city planning, urban growth management, and landscape visualization. Although traditional building footprint extraction from two-dimensional images is relatively easy, but are often both time-consuming and costly. Automated building footprint extraction from imagery has been studied extensively based on image segmentation using the pixel value, while the other dimension of segmentation, such as height value, have not been fully explored to extract the building footprints that have been used in this paper. This approach uses the Digital Terrain model (DTM) and Digital Surface Model (DSM) generated from the stereo imagery using the panchromatic bands with high spatial resolution to calculate the Normalized Digital Surface Model (nDSM) to separate the features which are above the ground surface. After extraction of Elevation (Height) of each building feature, the image segmentation has been performed to separate the building features using the threshold value. When the building features are separated, the Canny Edge Detection algorithm is used to delineate the actual building boundary. After the extraction of building boundary, it is vectorized. After simplification of the vector data, the building footprints are extracted. The vector data is compared to the digitized data sets, which show that the approach can be consistent and precise as the building segmentation approach has achieved greater accuracy because of incorporation of height value. There is no human error involved in the whole process.

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Section: Introductionmentioning

confidence: 99%

Automatic Building Footprint Extraction From High Resolution Stereo Satellite Image

Mia

Islam

2021

Plan Plus

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“…In addition, feature extraction based on NDVI time series data depends on the temporal and spatial resolution of remote sensing data when applied on a large or national scale (Shi, 2020). Computer technology has enabled remote sensing technology, machine learning (Low et al, 2015;Wang et al, 2020), and change detection (Cheng et al, 2011;Kuemmerle et al, 2008;Witmer, 2008;Yang et al, 2019;Zhang, 2010). The most important part of decision tree classification is the establishment of discriminant rules, using differences in the reflection spectral curves of different objects.…”

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confidence: 99%

Abandoned terrace recognition based on deep learning and change detection on the Loess Plateau in China

et al. 2023

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Terraces are an important cultivated land resource. Terrace abandonment affects the soil quality, soil and water conservation benefits, and biodiversity of terraces. Therefore, it is important to quantify the number and spatial distribution of abandoned terraces to protect cultivated land and food security. However, the traditional remote sensing method cannot identify small plots and make accurate assessment of abandoned farmland quickly in mountainous areas. To accurately identifying abandoned terraces, this study used semantic segmentation based on deep learning and change detection to identify abandoned terraces and their spatial distribution in a small watershed on the Loess Plateau in 2021. A comparative analysis of the accuracy of three deep learning models revealed that RefineNet is superior to DeepLabv3+ and DeepLabv3 for identifying abandoned terraces. The user's accuracy, producer's accuracy, overall accuracy, and appa values for RefineNet were 0.817, 0.894, 0.800, and 0.539, respectively. For change detection, the corresponding values were 0.821, 0.753, 0.731, and 0.426, respectively. In addition, semantic segmentation produced better recognition results than change detection in complex terrain and geomorphological areas. The abandoned terraces in the study area were mainly distributed in mountainous areas far from residential areas and more likely at high elevations with large slopes. This study provides a new method for recognizing abandoned terraces and spatial distribution information for managing and utilizing abandoned terraces.

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A review of research on remote sensing images shadow detection and application to building extraction

Dong,

Cao,

Zhao

2023

European Journal of Remote Sensing

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Semiautomatic extraction of building information and variation detection from high resolution remote sensing images

Cited by 4 publications

References 6 publications

Automatic Building Footprint Extraction From High Resolution Stereo Satellite Image

Automatic Building Footprint Extraction From High Resolution Stereo Satellite Image

Abandoned terrace recognition based on deep learning and change detection on the Loess Plateau in China

A review of research on remote sensing images shadow detection and application to building extraction

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