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

Zhou, Weiqi; Huang, Ganlin; Troy, Austin; Cadenasso, Mary L.

doi:10.1016/j.rse.2009.04.007

Cited by 236 publications

(190 citation statements)

References 23 publications

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“…In the case that both spectral and spatial characteristics of objects are quite vague in GE imagery, other ancillary data such as DEM, slope and aspect could be taken into consideration and may improve the classification accuracy [50,51]. It is expected that the inclusion of DEM data into the future list could improve the identification capacity between woodland and grassland types, as well as between buildings and shadows, which are significantly confused in this study [42,52].…”

Section: Potentials Analysis Of Ge Imagery For Land Use/cover Mappingmentioning

confidence: 94%

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Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

et al. 2013

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Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study in Wuhan City to perform an object-based land use/cover classification. The classification accuracy was assessed by using 570 validation points generated by a random sampling scheme and compared with a parallel classification of QuickBird (QB) imagery based on an object-based classification method. The results showed that GE has an overall classification accuracy of 78.07%, which is slightly lower than that of QB. No significant difference was found between these two classification results by the adoption of Z-test, which strongly proved the potentials of GE in land use/cover mapping. Moreover, GE has different discriminating capacity for specific land use/cover types. It possesses some advantages for mapping those types with good spatial characteristics in terms of geometric, shape and context. The object-based method is recommended for imagery classification when using GE imagery for mapping land use/cover. However, GE has some limitations for those types classified by using only spectral characteristics largely due to its poor spectral characteristics. OPEN ACCESSRemote Sens. 2013, 5 6027

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Section: Potentials Analysis Of Ge Imagery For Land Use/cover Mappingmentioning

confidence: 94%

“…The accuracy results were then compared to check their difference. This method was widely used in existing studies [27,41,42] as it can minimize the statistical and human bias in the process of validation sample selections [43]. It was implemented in three steps in this study.…”

Section: Classification Accuracy Assessment and Comparisonmentioning

confidence: 99%

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

et al. 2013

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“…In an optical image, shadows are formed by obstructing direct light. The lower DN values in the shadow areas cause partial or total loss of radiometric information in the affected areas (Dare, 2005;Yuan, 2008;Zhou et al, 2009), the loss of radiometric information is the absence of direct light (Adeline et al, 2013;Wu et al, 2014 In addition, the mean DN values of all plots for the R, G, and B bands in the non-shadow area are non-vegetation>water bodies>vegetation, whereas those in the shadow area are water bodies>non-vegetation>vegetation (Table 3 and Table 4) (Figure 2). Regardless of shadow or non-shadow, the DN values for vegetation in the NIR band are significantly highest than water bodies and non-vegetation, indicating that NIR effectively reflects vegetation conditions (Table 3 and Table 4) (Figure 2).…”

Section: Spectral Characteristics Of the Shadow Areamentioning

confidence: 99%

Analyzing Spectral Characteristics of Shadow Area From Ads-40 High Radiometric Resolution Aerial Images

Hsieh

Chen

et al. 2016

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

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Commission VII, WG VII/4KEY WORDS: Shadow, shadow spectra, ADS-40, high radiometric resolution ABSTRACT:The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i) The DN values in shadow area are much lower than in nonshadow area; (ii) DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii) The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv) The shadow area NIR of vegetation category also shows a strong reflection; (v) Generally, vegetation indexes (NDVI) still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40) is potential for the extract land cover information of shadow areas.

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“…A number of studies have investigated the problems in the first stage, i.e., shadow detection and removal in either high resolution satellite imagery (e.g., [1,7,8]) or aerial photography (e.g., [9][10][11][12][13][14]). An invariant color space based non-linear transformation was proposed [7], while histogram thresholding was used [1,8] to discriminate shadows from non-shadow areas in QuickBird and IKONOS images.…”

Section: Introductionmentioning

confidence: 99%

“…An invariant color space based non-linear transformation was proposed [7], while histogram thresholding was used [1,8] to discriminate shadows from non-shadow areas in QuickBird and IKONOS images. Similar approaches have also been used to detect shadows in aerial photography, such as invariant color space based transformations [9] and models [10,11], and histogram thresholding [12]. Additionally, three-dimensional models have been developed if the a priori knowledge of the sensor, the illumination, and the 3-D geometry of the scene are available [13,14].…”

Section: Introductionmentioning

confidence: 99%

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

Bauer

2013

Remote Sensing

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Shadows in high resolution imagery create significant problems for urban land cover classification and environmental application. We first investigated whether shadows were intrinsically different and hypothetically possible to separate from each other with ground spectral measurements. Both pixel-based and object-oriented methods were used to evaluate the effects of shadow detection on QuickBird image classification and spectroradiometric restoration. In each method, shadows were detected and separated either with or without histogram thresholding, and subsequently corrected with a k-nearest neighbor algorithm and a linear correlation correction. The results showed that shadows had distinct spectroradiometric characteristics, thus, could be detected with an optimal brightness threshold and further differentiated with a scene-based near infrared ratio. The pixel-based methods generally recognized more shadow areas and with statistically higher accuracy than the object-oriented methods. The effects of the prior shadow thresholding were not statistically significant. The accuracy of the final land cover classification, after accounting for the shadow detection and separation, was significantly higher for the pixel-based methods than for the object-oriented methods, although both achieved similar accuracy for the non-shadow classes. Both radiometric restoration algorithms significantly reduced shadow areas in the original satellite images.

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

Cited by 236 publications

References 23 publications

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

Analyzing Spectral Characteristics of Shadow Area From Ads-40 High Radiometric Resolution Aerial Images

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

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