Shadow Segmentation and Compensation in High Resolution Satellite Images

Ma, Hui; Qin, Qiming; Xinyi, Shen

doi:10.1109/igarss.2008.4779175

Cited by 66 publications

(93 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Differences in the classification scheme adopted for the two sites, especially at the lower finer level, was motivated by the absence of shrub understory and the large off-nadir acquisition angle (i.e., more intense presence of shadows) of the Quickbird image in Thessaloniki site. While shadows in high spatial resolution images can provide geometric and semantic information such as indications about the shape, surface characteristics and the relative position of objects, they may also influence negatively tasks such as image segmentation, automated object recognition, change detection and scene matching through the loss of feature information, false color tone and objects shape distortions [47].…”

Section: Crown Regionsmentioning

confidence: 99%

Canopy Fuel Load Mapping of Mediterranean Pine Sites Based on Individual Tree-Crown Delineation

et al. 2013

View full text Add to dashboard Cite

This study presents an individual tree-crown-based approach for canopy fuel load estimation and mapping in two Mediterranean pine stands. Based on destructive sampling, an allometric equation was developed for the estimation of crown fuel weight considering only pine crown width, a tree characteristic that can be estimated from passive imagery. Two high resolution images were used originally for discriminating Aleppo and Calabrian pines crown regions through a geographic object based image analysis approach. Subsequently, the crown region images were segmented using a watershed segmentation algorithm and crown width was extracted. The overall accuracy of the tree crown isolation expressed through a perfect match between the reference and the delineated crowns was 34.00% for the Kassandra site and 48.11% for the Thessaloniki site, while the coefficient of determination between the ground measured and the satellite extracted crown width was 0.5. Canopy fuel load values estimated in the current study presented mean values from 1.29 ± 0.6 to 1.65 ± 0.7 kg/m 2 similar to other conifers worldwide. Despite the modest accuracies attained in this first study of individual tree crown fuel load mapping, the OPEN ACCESSRemote Sens. 2013, 5 6462 combination of the allometric equations with satellite-based extracted crown width information, can contribute to the spatially explicit mapping of canopy fuel load in Mediterranean areas. These maps can be used among others in fire behavior prediction, in fuel reduction treatments prioritization and during active fire suppression.

show abstract

Section: Crown Regionsmentioning

confidence: 99%

Canopy Fuel Load Mapping of Mediterranean Pine Sites Based on Individual Tree-Crown Delineation

et al. 2013

View full text Add to dashboard Cite

show abstract

“…In the following step Otsu's method (Otsu, 1979) was applied for automatic determining of the optimal threshold to delineate shadows from no shadows regions. The indexes considered and compared were those develop by Tsai and Lin (2006) and derived from the transformation of RGB bands to HIS, HSV YCbCr respectively, the NSDVI index proposed by Ma et al (2008), and the WBI index of Domenech and Mallet (2014). These indexes are defined as follows:…”

Section: Shadow Detectionmentioning

confidence: 99%

Comparison of Unsupervised Vegetation Classification Methods From VHR Images After Shadows Removal by Innovative Algorithms

Movia

Beinat

Crosilla

2015

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

View full text Add to dashboard Cite

ABSTRACT:The recognition of vegetation by the analysis of very high resolution (VHR) aerial images provides meaningful information about environmental features; nevertheless, VHR images frequently contain shadows that generate significant problems for the classification of the image components and for the extraction of the needed information. The aim of this research is to classify, from VHR aerial images, vegetation involved in the balance process of the environmental biochemical cycle, and to discriminate it with respect to urban and agricultural features. Three classification algorithms have been experimented in order to better recognize vegetation, and compared to NDVI index; unfortunately all these methods are conditioned by the presence of shadows on the images. Literature presents several algorithms to detect and remove shadows in the scene: most of them are based on the RGB to HSI transformations. In this work some of them have been implemented and compared with one based on RGB bands. Successively, in order to remove shadows and restore brightness on the images, some innovative algorithms, based on Procrustes theory, have been implemented and applied. Among these, we evaluate the capability of the so called "not-centered oblique Procrustes" and "anisotropic Procrustes" methods to efficiently restore brightness with respect to a linear correlation correction based on the Cholesky decomposition. Some experimental results obtained by different classification methods after shadows removal carried out with the innovative algorithms are presented and discussed.

show abstract

“…The segmentation approach is one of many different approaches that are used to detect shadow regions from single VHR multispectral images. Ma et al (2008) presented an approach in shadow segmentation and compensation that was implemented on an IKONOS image through a normalising Saturation-Value difference index (NSVDI). The approach was based on analysis Hue-Saturation-Value (HSV) colour space to detect shadow regions of buildings and a histogram matching technique was exploited to retrieve the information under shadow.…”

Section: Cs Dsmentioning

confidence: 99%

Shadow Detection From Very High Resoluton Satellite Image Using Grabcut Segmentation and Ratio-Band Algorithms

Kadhim

Mourshed

Bray

2015

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

View full text Add to dashboard Cite

ABSTRACT:Very-High-Resolution (VHR) satellite imagery is a powerful source of data for detecting and extracting information about urban constructions. Shadow in the VHR satellite imageries provides vital information on urban construction forms, illumination direction, and the spatial distribution of the objects that can help to further understanding of the built environment. However, to extract shadows, the automated detection of shadows from images must be accurate. This paper reviews current automatic approaches that have been used for shadow detection from VHR satellite images and comprises two main parts. In the first part, shadow concepts are presented in terms of shadow appearance in the VHR satellite imageries, current shadow detection methods, and the usefulness of shadow detection in urban environments. In the second part, we adopted two approaches which are considered current state-of-theart shadow detection, and segmentation algorithms using WorldView-3 and Quickbird images. In the first approach, the ratios between the NIR and visible bands were computed on a pixel-by-pixel basis, which allows for disambiguation between shadows and dark objects. To obtain an accurate shadow candidate map, we further refine the shadow map after applying the ratio algorithm on the Quickbird image. The second selected approach is the GrabCut segmentation approach for examining its performance in detecting the shadow regions of urban objects using the true colour image from WorldView-3. Further refinement was applied to attain a segmented shadow map. Although the detection of shadow regions is a very difficult task when they are derived from a VHR satellite image that comprises a visible spectrum range (RGB true colour), the results demonstrate that the detection of shadow regions in the WorldView-3 image is a reasonable separation from other objects by applying the GrabCut algorithm. In addition, the derived shadow map from the Quickbird image indicates significant performance of the ratio algorithm. The differences in the characteristics of the two satellite imageries in terms of spatial and spectral resolution can play an important role in the estimation and detection of the shadow of urban objects.

show abstract

Shadow Segmentation and Compensation in High Resolution Satellite Images

Cited by 66 publications

References 9 publications

Canopy Fuel Load Mapping of Mediterranean Pine Sites Based on Individual Tree-Crown Delineation

Canopy Fuel Load Mapping of Mediterranean Pine Sites Based on Individual Tree-Crown Delineation

Comparison of Unsupervised Vegetation Classification Methods From VHR Images After Shadows Removal by Innovative Algorithms

Shadow Detection From Very High Resoluton Satellite Image Using Grabcut Segmentation and Ratio-Band Algorithms

Contact Info

Product

Resources

About