Evaluating the Effects of Shadow Detection on QuickBird Image Classification and Spectroradiometric Restoration

Wu, Jindong; Bauer, Marvin E.

doi:10.3390/rs5094450

Cited by 21 publications

(12 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since shadows in VHR multispectral remote sensing images indicate additional information about ground objects, shadows may supply useful information for applications such as buildings reconstruction and height evaluation [11,12]. Notably, shadows always result in loss and distortion of land cover within shadow regions, because shadow regions always present low radiance and different spectral reflectivity properties from nonshadow areas [3,[13][14][15][16][17]. However, objects in shadow regions may contain significant information, especially in VHR multispectral remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

A Mixed Property-Based Automatic Shadow Detection Approach for VHR Multispectral Remote Sensing Images

Han

Xue

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

Shadows in very high-resolution multispectral remote sensing images hinder many applications, such as change detection, target recognition, and image classification. Though a wide variety of significant research has explored shadow detection, shadow pixels are still more or less omitted and are wrongly confused with vegetation pixels in some cases. In this study, to further manage the problems of shadow omission and vegetation misclassification, a mixed property-based shadow index is developed for detecting shadows in very high-resolution multispectral remote sensing images based on the difference of the hue component and the intensity component between shadows and nonshadows, and the difference of the reflectivity of the red band and the near infrared band between shadows and vegetation cover in nonshadows. Then, the final shadow mask is achieved, with an optimal threshold automatically obtained from the index image histogram. To validate the effectiveness of our approach for shadow detection, three test images are selected from the multispectral WorldView-3 images of Rio de Janeiro, Brazil, and are tested with our method. When compared with other investigated standard shadow detection methods, the resulting images produced by our method deliver a higher average overall accuracy (95.02%) and a better visual sense. The highly accurate data show the efficacy and stability of the proposed approach in appropriately detecting shadows and correctly classifying shadow pixels against the vegetation pixels for very high-resolution multispectral remote sensing images.

show abstract

Section: Introductionmentioning

confidence: 99%

A Mixed Property-Based Automatic Shadow Detection Approach for VHR Multispectral Remote Sensing Images

Han

Xue

et al. 2018

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…High-resolution multispectral data, by contrast, are widely available, offering great opportunities to better resolve the spatial heterogeneous details of urban landscapes where land cover changes in a short distance [71][72][73].Urban tree canopy maps derived from high-resolution multispectral imagery have been used to scale-up biomass estimates from limited field plots to the surrounding landscape [18,57]. However, tree misclassification, often caused by shadows [74], spectral confusion with other vegetation types [75], and insufficient field sampling, directly affects the accuracy of biomass estimation [76]. The reported accuracy of tree classification was 74.3% with QuickBird satellite imagery in Los Angeles, California [9] and 86.2% with digital color-infrared aerial imagery in Syracuse, New York [77].The classification accuracy of high-resolution imagery was even lower at the species level [54].…”

mentioning

confidence: 99%

“…Urban tree canopy maps derived from high-resolution multispectral imagery have been used to scale-up biomass estimates from limited field plots to the surrounding landscape [18,57]. However, tree misclassification, often caused by shadows [74], spectral confusion with other vegetation types [75], and insufficient field sampling, directly affects the accuracy of biomass estimation [76]. The reported accuracy of tree classification was 74.3% with QuickBird satellite imagery in Los Angeles, California [9] and 86.2% with digital color-infrared aerial imagery in Syracuse, New York [77].…”

mentioning

confidence: 99%

Developing General Equations for Urban Tree Biomass Estimation with High-Resolution Satellite Imagery

2019

Sustainability

Self Cite

View full text Add to dashboard Cite

Urban trees provide various important ecological services, the quantification of which is vital to sustainable urban development and requires accurate estimation of tree biomass. A limited number of allometric biomass equations, however, have been developed for urban species due to the prohibitive cost. Remote sensing has provided cost-effective means for estimating urban forest biomass, although the propagation of error in the estimation process is not well understood. This study aimed to offer a baseline assessment of the feasibility of estimating urban tree biomass with remote sensing-based general equations applicable to broad taxonomic groups by conducting a large urban tree inventory on a university campus. The biomasses of 191 trees of seven species from the inventory, separated into two categories (i.e., evergreen and deciduous), were calculated exclusively with urban-based species-specific allometric equations. WorldView-2 satellite imagery data were acquired to retrieve normalized difference vegetation index (NDVI) values at the location, crown, and stand levels. The results indicated that biomass correlated with NDVI in varying forms and degrees. The general equations at the crown level yielded the most accurate biomass estimates, while the location-level estimates were the least accurate. Crown-level spectral responses provided adequate information for delivering spatially explicit biomass estimation. could be substantial [50][51][52], ranging from −97% to 205% depending on the individual species and the sizes of the trees [43]. Biomass estimates could even vary from 27% less to 29% more when applying the allometric equation developed for an individual urban species in California to the same species in Colorado because of considerable differences in site conditions [43].Considering the time and monetary cost of developing site-and species-specific allometric equations, it has been suggested to use general equations that are applicable to all species in a broad taxonomic grouping instead [43,53,54]. Such equations have been developed for 35 natural forest-grown genera on a national scale [39,40]. However, studies focused on developing allometric equations for general urban tree groups are scant [49], because urban-based equations over the entire range of site conditions are not available for compilation and synthesis [55].Another approach to overcome the prohibitive cost associated with the development of allometric equations is to utilize remote sensing and geospatial techniques [49,[56][57][58][59][60]. Remote sensing obtains information about the Earth's environment from a distance without the need for extensive field surveys over large areas. It has been used in several different ways as a non-destructive and cost-effective method for biomass estimation with varying degrees of success.Generated with Light Detection and Ranging (LiDAR) technology, point cloud data have been regularly used to measure urban tree biomass directly [58,[60][61][62] or extract basic silvicultural variables such as tree he...

show abstract

“…Shadows exist in most very high resolution (VHR) orthoimagery (with a resolution of up to 1 m) and lead to more complex and detailed land-cover features, especially in urban areas [1]. Shadow areas usually have incomplete spectral information, lower intensity, and fuzzy boundaries, which seriously affect the subsequent interpretation [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…The current methods for shadow detection can be divided into three types [11][12][13]: (1) property-based methods [9,[13][14][15][16][17][18][19][20]; (2) geometrical methods [14,[17][18][19][20]; and (3) machine learning methods [15,16,21,22].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Shadow Detection Method for VHR Remote Sensing Orthoimagery

Wang

Yuan

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:The application potential of very high resolution (VHR) remote sensing imagery has been boosted by recent developments in the data acquisition and processing ability of aerial photogrammetry. However, shadows in images contribute to problems such as incomplete spectral information, lower intensity brightness, and fuzzy boundaries, which seriously affect the efficiency of the image interpretation. In this paper, to address these issues, a simple and automatic method of shadow detection is presented. The proposed method combines the advantages of the property-based and geometric-based methods to automatically detect the shadowed areas in VHR imagery. A geometric model of the scene and the solar position are used to delineate the shadowed and non-shadowed areas in the VHR image. A matting method is then applied to the image to refine the shadow mask. Different types of shadowed aerial orthoimages were used to verify the effectiveness of the proposed shadow detection method, and the results were compared with the results obtained by two state-of-the-art methods. The overall accuracy of the proposed method on the three tests was around 90%, confirming the effectiveness and robustness of the new method for detecting fine shadows, without any human input. The proposed method also performs better in detecting shadows in areas with water than the other two methods.

show abstract

Evaluating the Effects of Shadow Detection on QuickBird Image Classification and Spectroradiometric Restoration

Cited by 21 publications

References 20 publications

A Mixed Property-Based Automatic Shadow Detection Approach for VHR Multispectral Remote Sensing Images

A Mixed Property-Based Automatic Shadow Detection Approach for VHR Multispectral Remote Sensing Images

Developing General Equations for Urban Tree Biomass Estimation with High-Resolution Satellite Imagery

An Automatic Shadow Detection Method for VHR Remote Sensing Orthoimagery

Contact Info

Product

Resources

About