A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

Tatar, Nurollah; Saadatseresht, M.; Arefi, Hossein; Hadavand, Ahmad

doi:10.1016/j.asr.2018.03.011

Cited by 24 publications

(12 citation statements)

References 29 publications

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“…The value of these parameters has a direct effect on the final segmentation and shadow detection result. The scale parameter, shape and compactness weight coefficients are selected as 80, 0.1 and 0.9 respectively and considered constant in the experiments based on our previous experiences on the same dataset (Tatar et al, 2015b;Tatar et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

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The Effect of Shadow Area on SGM Algorithm and Disparity Map Refinement From High Resolution Satellite Stereo Images

Tatar

Saadatseresht

Arefi

2017

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

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ABSTRACT:Semi Global Matching (SGM) algorithm is known as a high performance and reliable stereo matching algorithm in photogrammetry community. However, there are some challenges using this algorithm especially for high resolution satellite stereo images over urban areas and images with shadow areas. As it can be seen, unfortunately the SGM algorithm computes highly noisy disparity values for shadow areas around the tall neighborhood buildings due to mismatching in these lower entropy areas. In this paper, a new method is developed to refine the disparity map in shadow areas. The method is based on the integration of potential of panchromatic and multispectral image data to detect shadow areas in object level. In addition, a RANSAC plane fitting and morphological filtering are employed to refine the disparity map. The results on a stereo pair of GeoEye-1 captured over Qom city in Iran, shows a significant increase in the rate of matched pixels compared to standard SGM algorithm.

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Section: Resultsmentioning

confidence: 99%

“…The threshold percentage of the shadow pixels in each image segment is needed to make decisions here. From previous studies, the best majority threshold is selected as 20% (Tatar et al, 2015a;Tatar et al, 2016). Figure 4 shows the comparison of pixel and object level shadow detection results.…”

Section: Figure 3 Stereo Anaglyph Of the Generated Epipolar Imagesmentioning

confidence: 99%

The Effect of Shadow Area on SGM Algorithm and Disparity Map Refinement From High Resolution Satellite Stereo Images

Tatar

Saadatseresht

Arefi

2017

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

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“…The Sentinel-2 Multispectral Imager (MSI) sensors have very different spatial and temporal resolutions (10 to 60m) [28]. Many studies on land covering analysis and mapping have been conducted in large area with different satellites images such as: Landsat 8 [29] and SPOT 4 [30], SPOT 5 [31], Worldview ІІ and ІІІ [32], ENVIsat and ASAR [33], Landsat MMS [34] and also many methods of accuracy assessment have been discussed in the remote sensing literature by Aronoff, 1982 [35], Kalkhanl, 1995 [36], Koukoulas & Blackburn, 2001 [37], Rosenfield & FitzpatrickLins, 1986 [38]. The most widely promoted and used, may be derived from a confusion matrix (CM) [39].…”

Section: Introductionmentioning

confidence: 99%

Land Cover Classification by using Sentinel-2 Images: A case study in the city of Rome

Nezhad¹,

Heydari²,

Fusilli³

et al. 2019

World Congress on Civil, Structural, and Environmental Engineering

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Land cover mapping of land area in Mediterranean climate regions from satellite images is not simple, due to the similarity of the spectral characteristics of the urban area and city surroundings. In this study, satellite images from Sentinel 2A by ESA (European Space Agency) were used to classify the land cover of Rome city, Italy. This paper presents two methods aiming at improving the land cover classification accuracy by using multispectral satellite images. The classification process was performed by using two different algorithms, namely: Maximum Likelihood (ML) and Support Vector Machine (SVM). The supervised "Maximum Likelihood" and "Support Vector Machine" classification algorithms available in ENVI (Environment for Visualizing Images) software, were used to detect five land cover classes: urban, forest, water, agriculture and empty land classes. The results show the ML method applied to Sentinel-2A images provides a higher overall accuracy and kappa coefficient than the SVM method. The main reasons are to increase two amounts in the ML method and over the next few years in this study, first: carry out three steps for increase accuracy and kappa with Sieve Classes, Clump Classes and Majority/Minority Analysis. And second reason the most accurate classification for both approaches was allocated to the year 2018, possibly due to the higher image quality and on-time training sample sites compared to previous 2015, 2016, 2017 years. The results of both methods in this study have been compared.

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“…On one side, satellite imagery has been used for a large number of applications. Some examples are the analysis of urban hazards [9,10], the segmentation of urban features [11,12], or shadow detection [13], among others. On the other side, laser scanner technology has been used in multiple research fields during the last decade, and it is proven to be a useful tool for a wide number of applications that may have an impact on the evolution of the sustainability and resilience of the cities.…”

Section: Introductionmentioning

confidence: 99%

Automatic Parametrization of Urban Areas Using ALS Data: The Case Study of Santiago de Compostela

Soilán

Riveiro

Liñares

et al. 2018

IJGI

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Nowadays, gathering accurate and meaningful information about the urban environment with the maximum efficiency in terms of cost and time has become more relevant for city administrations, as this information is essential if the sustainability or the resilience of the urban structure has to be improved. This work presents a methodology for the automatic parametrization and characterization of different urban typologies, for the specific case study of Santiago de Compostela (Spain), using data from Aerial Laser Scanners (ALS). This methodology consists of a number of sequential processes of point cloud data, using exclusively their geometric coordinates. Three of the main elements of the urban structure are assessed in this work: intersections, building blocks, and streets. Different geometric and contextual metrics are automatically extracted for each of the elements, defining the urban typology of the studied area. The accuracy of the measurements is validated against a manual reference, obtaining average errors of less than 3%, proving that the input data is valid for this assessment.

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A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

Cited by 24 publications

References 29 publications

The Effect of Shadow Area on SGM Algorithm and Disparity Map Refinement From High Resolution Satellite Stereo Images

The Effect of Shadow Area on SGM Algorithm and Disparity Map Refinement From High Resolution Satellite Stereo Images

Land Cover Classification by using Sentinel-2 Images: A case study in the city of Rome

Automatic Parametrization of Urban Areas Using ALS Data: The Case Study of Santiago de Compostela

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