Urban feature shadow extraction based on high-resolution satellite remote sensing images

Shi, Lu; Zhao, Yuefeng

doi:10.1016/j.aej.2023.06.046

Cited by 6 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The methodology applied is basically the same as already presented in a previous article [2] where the approach used for the classification of urban land was exposed. Focusing mainly on the correct distinction during the classification phase between shadows and water is a well-known problem in the literature [11][12][13][14] ,during the classification of urban areas. The main steps have remained unchanged, and in the order, the useful indices are first calculated, then a first segmentation is carried out to identify and subsequently classify larger objects such as fields, and a second segmentation to identify and classify smaller objects, water, and vegetation.…”

Section: Methodsmentioning

confidence: 99%

Land Use Recognition by Applying Fuzzy Logic and Object-Based Classification to Very High Resolution Satellite Images

Perregrini,

Casella

2024

Remote Sensing

View full text Add to dashboard Cite

The past decade has seen remarkable advancements in Earth observation satellite technologies, leading to an unprecedented level of detail in satellite imagery, with ground resolutions nearing an impressive 30 cm. This progress has significantly broadened the scope of satellite imagery utilization across various domains that were traditionally reliant on aerial data. Our ultimate goal is to leverage this high-resolution satellite imagery to classify land use types and derive soil permeability maps by attributing permeability values to the different types of classified soil. Specifically, we aim to develop an object-based classification algorithm using fuzzy logic techniques to describe the different classes relevant to soil permeability by analyzing different test areas, and once a complete method has been developed, apply it to the entire image of Pavia. In this study area, a logical scheme was developed to classify the field classes, cultivated and uncultivated, and distinguish them from large industrial buildings, which, due to their radiometric similarity, can be classified incorrectly, especially with uncultivated fields. Validation of the classification results against ground truth data, produced by an operator manually classifying part of the image, yielded an impressive overall accuracy of 95.32%.

show abstract

Section: Methodsmentioning

confidence: 99%

Land Use Recognition by Applying Fuzzy Logic and Object-Based Classification to Very High Resolution Satellite Images

Perregrini,

Casella

2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Urban areas face challenges from building-induced shadows, hindering information extraction from highresolution images. Recent efforts focus on shadow removal for urban regions, employing algorithms assuming a linear relationship between radiance in shadow and non-shadow areas [47][48][49]. Shadow detection and de-shadowing are crucial preprocessing steps for satellite images, significantly improving land use and land cover (LULC) classification accuracy and facilitating vegetation carbon density mapping.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Urban Above-Ground Vegetation Carbon Density Mapping: An Integrated Approach Incorporating De-Shadowing, Spectral Unmixing, and Machine Learning

Qie,

Ye,

Wang

et al. 2024

Forests

View full text Add to dashboard Cite

Accurately mapping urban above-ground vegetation carbon density presents challenges due to fragmented landscapes, mixed pixels, and shadows induced by buildings and mountains. To address these issues, a novel methodological framework is introduced, utilizing a linear spectral unmixing analysis (LSUA) for shadow removal and vegetation information extraction from mixed pixels. Parametric and nonparametric models, incorporating LSUA-derived vegetation fraction, are compared, including linear stepwise regression, logistic model-based stepwise regression, k-Nearest Neighbors, Decision Trees, and Random Forests. Applied in Shenzhen, China, the framework integrates Landsat 8, Pleiades 1A & 1B, DEM, and field measurements. Among the key findings, the shadow removal algorithm is effective in mountainous areas, while LSUA-enhanced models improve urban vegetation carbon density mapping, albeit with marginal gains. Integrating kNN and RF with LSUA reduces errors, and Decision Trees, especially when integrated with LSUA, outperform other models. This study underscores the potential of the proposed framework, particularly the integration of Decision Trees with LSUA, for advancing the accuracy of urban vegetation carbon density mapping.

show abstract

“…These building detection methods can be roughly classified into two groups: two-dimensional (2D) and three-dimension (3D) building detection methods. The 2D methods generally extract buildings using brightness, shape, texture, and concomitant shadows [11][12][13][14][15][16][17][18][19][20]. Huang and Zhang [11] developed a morphological building index (MBI) to extract buildings using brightness, size, and shape.…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Buildings by Height from Single Urban High-Resolution Remote Sensing Images

Zhang,

Xu,

Fan

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Recent improvements in remote sensing technologies have boosted building detection techniques from rough classifications using moderate resolution imagery to precise extraction from high-resolution imagery. Shadows frequently emerge in high-resolution urban images. To exploit shadow information, we developed a novel building detection and classification algorithm for images of urban areas with large-size shadows, employing only the visible spectral bands to determine the height levels of buildings. The proposed method, building general-classified by height (BGCH), calculates shadow orientation, detects buildings using seed-blocks, and classifies the buildings into different height groups. Our proposed approach was tested on complex urban scenes from Toronto and Beijing. The experimental results illustrate that our proposed method accurately and efficiently detects and classifies buildings by their height levels; the building detection rate exceeded 95%. The precision of classification by height levels was over 90%. This novel building-height-level detection method provides rich information at low cost and is suitable for further city scene analysis, flood disaster risk assessment, population estimation, and building change detection applications.

show abstract

Urban feature shadow extraction based on high-resolution satellite remote sensing images

Cited by 6 publications

References 30 publications

Land Use Recognition by Applying Fuzzy Logic and Object-Based Classification to Very High Resolution Satellite Images

Land Use Recognition by Applying Fuzzy Logic and Object-Based Classification to Very High Resolution Satellite Images

Enhancing Urban Above-Ground Vegetation Carbon Density Mapping: An Integrated Approach Incorporating De-Shadowing, Spectral Unmixing, and Machine Learning

Detection and Classification of Buildings by Height from Single Urban High-Resolution Remote Sensing Images

Contact Info

Product

Resources

About