Cloud shadow removal from aerial photographs

Shu, Joseph; Freeman, Herbert

doi:10.1016/0031-3203(90)90040-r

Cited by 26 publications

(14 citation statements)

References 8 publications

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“…Following the shadow detection procedure as discussed above, a linear-correlation correction (LCC) method, also referred to as mean and variance transformation (e.g., Shu & Freeman, 1990;Dare, 2005), was used for shadow removal (Sarabandi et al, 2004;Chen et al, 2007). Several previous studies have shown that LCC methods are superior to some other shadow removal techniques, such as Gamma correction and histogram matching (Shu & Freeman, 1990;Sarabandi et al, 2004;Dare, 2005).…”

Section: Methods 2: Classification Of Shaded Areas With Shadow Restoramentioning

confidence: 98%

“…Several previous studies have shown that LCC methods are superior to some other shadow removal techniques, such as Gamma correction and histogram matching (Shu & Freeman, 1990;Sarabandi et al, 2004;Dare, 2005).…”

Section: Methods 2: Classification Of Shaded Areas With Shadow Restoramentioning

confidence: 98%

“…We applied a histogram thresholding method for shadow detection, which has been successfully used in many previous studies (e.g., Shu & Freeman, 1990;Shettigara & Sumerling, 1998;Dare, 2005;Chen et al, 2007). The principle of a histogram thresholding method is based on the intensity difference of shaded and non-shaded areas.…”

Section: Shadow Detection: Pixel-based Versus Object-basedmentioning

confidence: 99%

“…Different algorithms have been applied for shadow detection, such as invariant color models (e.g., Salvador et al, 2001;Tsai, 2006), histogram analysis/thresholding (Dare, 2005;Chen et al, 2007), three dimensional modeling (e.g., Rau et al, 2002;Li et al, 2005), and hierarchical shadow detection algorithms (Yao & Zhang, 2006). A variety of radiometric enhancement (or restoration) methods have been used for shadow restoration, including linear correlation correction (LCC) (e.g., Shu and Freeman, 1990;Sarabandi et al, 2004;Dare 2005;Chen et al, 2007), Gamma correction (Sarabandi et al, 2004), histogram matching (Sarabandi et al, 2004;Dare, 2005;Tsai, 2006), invariant color models (Tsai, 2006), and companion area intensity mapping (Li et al, 2005). Multisource data fusion has also been applied to remove cloud shadows from low resolution Landsat TM images (Wang et al, 1999), and to minimize shadow problems in high spatial resolution QuickBird imagery (Yuan, 2008).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study

Zhou

Huang

Troy

et al. 2009

Remote Sensing of Environment

236

173

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Section: Methods 2: Classification Of Shaded Areas With Shadow Restoramentioning

confidence: 98%

Section: Methods 2: Classification Of Shaded Areas With Shadow Restoramentioning

confidence: 98%

Section: Shadow Detection: Pixel-based Versus Object-basedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study

Zhou

Huang

Troy

et al. 2009

Remote Sensing of Environment

236

173

View full text Add to dashboard Cite

“…The negative effect of shadows including radiometric information loss (Arevalo et al, 2006;Chen et al, 2007) and inaccurate change detection results due to different azimuth angle (Yamazaki et al, 2009). In order to minimize the negative effect of shadows in aerial photos and satellite images, the concept of brightness compensation was introduced (Shu and Freeman, 1990). This process requires shadows firstly be detected and extracted, and then different algorithms applied to enhance these shadow regions.…”

Section: Negative Effects Of Shadowsmentioning

confidence: 99%

An Assessment of Shadow Enhanced Urban Remote Sensing Imagery of a Complex City – Hong Kong

Wan

King

2012

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

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ABSTRACT:Large portions of shadowed areas in satellite images of urban areas can affect the accuracy of classification and thus reduce an image's effectiveness in urban remote sensing applications. This is particularly acute in cities such as Hong Kong where dense high-rise buildings cast many long shadows across a variety of different surface types. One solution to this problem is to enhance shadowed areas so their spectral range becomes closer to their corresponding non-shadowed areas. Shadowed areas were automatically selected and two techniques, Gamma correction and Linear Correlation Correction, were applied to three study sites of a 2.4m Quickbird image. The selected study sites represent typical urban types of Hong Kong, ranging from high-rise commercial to low-rise residential areas. The shadow detection algorithm is based on the spectral shape index and its limitation is discussed. The histograms of the corresponding non-shadowed areas, the original and the enhanced shadow areas are used to compare the spectral range. The results show that the enhanced areas, in band ratios such as NDVI, show greater similarity after enhancement, but they also look darker than the non-shadowed areas. Where continuous shadowed areas such as in commercial areas, the spectral range cannot be restored.

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Characteristics of snow cover in the Hindukush, Karakoram and Himalaya region using Landsat satellite data

Butt

2012

Hydrological Processes

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Abstract:Estimation of snow cover characteristics (snow grain size, snow contamination, snow depth and liquid water content) from satellite data are important components for many hydrological models used for the water resource management. This research aimed to use Landsat ETM+ (Enhanced Thematic Mapper Plus) data to estimate the snow pack characteristics in northern Pakistan. The Normalized Difference Snow Index (NDSI), Snow Contamination Index (SCI) and Snow Grain Size Index (SGI) are applied to estimate the snow cover characteristics in northern Pakistan for the first time. Qualitative maps are generated to show the snow cover distribution, snow contamination concentration and snow grain size distribution over snow cover area. Our results show that NDSI, SCI and SGI can be effectively used to identify snow area, contaminated snow and ageing snow. Furthermore, the results of the current study indicate that in the HKH region 99.8% of the snow is least contaminated whereas 94.50% of the area has fine snow grain size. As no such attempt in the past has been made in northern Pakistan, it is envisaged that the results of this study will be helpful in planning remote sensing data for the water resource management and in characterizing the snow cover for the climate change applications in the region.

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Cloud shadow removal from aerial photographs

Cited by 26 publications

References 8 publications

Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study

Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study

An Assessment of Shadow Enhanced Urban Remote Sensing Imagery of a Complex City – Hong Kong

Characteristics of snow cover in the Hindukush, Karakoram and Himalaya region using Landsat satellite data

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