A Region-Based Multi-Scale Approach for Object-Based Image  Analysis

Kavzoğlu, Taşkın; Erdemir, Merve Yildiz; Tonbul, Hasan

doi:10.5194/isprs-archives-xli-b7-241-2016

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…Cánovas-García and Alonso-Sarría (2015) demonstrated an improvement in segmentation quality by optimizing the segmentation parameter based on spatially differentiated agricultural plots, instead of selecting a single parameter for the whole scene 14 . Recently, Kavzoglu et al (2016) proposed a regionalized multiscale approach in which an initial coarse segmentation was carried out in order to produce areas for further refinement of the segmentation parameters 15 . Classification results were shown to improve when the optimization of the segmentation parameter was performed regionally rather than globally.…”

Section: Introductionmentioning

confidence: 99%

A local segmentation parameter optimization approach for mapping heterogeneous urban environments using VHR imagery

Grippa

Georganos

Vanhuysse

et al. 2017

Remote Sensing Technologies and Applications in Urban Environments II

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Mapping large heterogeneous urban areas using object-based image analysis (OBIA) remains challenging, especially with respect to the segmentation process. This could be explained both by the complex arrangement of heterogeneous land-cover classes and by the high diversity of urban patterns which can be encountered throughout the scene. In this context, using a single segmentation parameter to obtain satisfying segmentation results for the whole scene can be impossible. Nonetheless, it is possible to subdivide the whole city into smaller local zones, rather homogeneous according to their urban pattern. These zones can then be used to optimize the segmentation parameter locally, instead of using the whole image or a single representative spatial subset. This paper assesses the contribution of a local approach for the optimization of segmentation parameter compared to a global approach. Ouagadougou, located in sub-Saharan Africa, is used as case studies. First, the whole scene is segmented using a single globally optimized segmentation parameter. Second, the city is subdivided into 283 local zones, homogeneous in terms of building size and building density. Each local zone is then segmented using a locally optimized segmentation parameter. Unsupervised segmentation parameter optimization (USPO), relying on an optimization function which tends to maximize both intra-object homogeneity and inter-object heterogeneity, is used to select the segmentation parameter automatically for both approaches. Finally, a land-use/land-cover classification is performed using the Random Forest (RF) classifier. The results reveal that the local approach outperforms the global one, especially by limiting confusions between buildings and their bare-soil neighbors.

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Section: Introductionmentioning

confidence: 99%

A local segmentation parameter optimization approach for mapping heterogeneous urban environments using VHR imagery

Grippa

Georganos

Vanhuysse

et al. 2017

Remote Sensing Technologies and Applications in Urban Environments II

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“…The land use/cover changes occurring as a result of natural and anthropogenic events can be monitored by using satellite imagery to support land planning and disaster monitoring [62][63][64][65]. In this study, Sentinel 2A satellite images in the months of January of 2016, 2017, and 2018 and February of 2019, when flood events occurred, were analyzed to identify the flood inundated areas in the BMR Basin (SensingTime(ST):20160109T090342/BaselineNumber (N):201/RelativeOrbitNumber(R):007/TileNumber(T):35SNB/https://scihub.copernicus.eu/ dhus/;ST:20170113T090321/N:204/R:007/T:35SNB/https://scihub.copernicus.eu/dhus/; ST:20180128T090221/N:206/R:007/T:35SNB/https://scihub.copernicus.eu/dhus/; ST:20190202T090201/N:207/R007/T:35SNB/https://scihub.copernicus.eu/dhus/).…”

Section: Temporal Flood Analysis With Satellite Imagesmentioning

confidence: 99%

UAV-based evaluation of morphological changes induced by extreme rainfall events in meandering rivers

et al. 2020

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Morphological changes, caused by the erosion and deposition processes due to water discharge and sediment flux occur, in the banks along the river channels and in the estuaries. Flow rate is one of the most important factors that can change river morphology. The geometric shapes of the meanders and the river flow parameters are crucial components in the areas where erosion or deposition occurs in the meandering rivers. Extreme precipitation triggers erosion on the slopes, which causes significant morphological changes in large areas during and after the event. The flow and sediment amount observed in a river basin with extreme precipitation increases and exceeds the long-term average value. Hereby, erosion severity can be determined by performing spatial analyses on remotely sensed imagery acquired before and after an extreme precipitation event. Changes of erosion and deposition along the river channels and overspill channels can be examined by comparing multi-temporal Unmanned Aerial Vehicle (UAV) based Digital Surface Model (DSM) data. In this study, morphological changes in the Büyük Menderes River located in the western Turkey, were monitored with pre-flood (June 2018), during flood (January 2019), and post-flood (September 2019) UAV surveys, and the spatial and volumetric changes of eroded/deposited sediment were quantified. For this purpose, the DSAS (Digital Shoreline Analysis System) method and the DEM of Difference (DoD) method were used to determine the changes on the riverbank and to compare the periodic volumetric morphological changes. Hereby, Structure from Motion (SfM) photogrammetry technique was exploited to a low-cost UAV derived imagery to achieve riverbank, areal and volumetric changes following the extreme rainfall events extracted from the time series of Tropical Rainfall Measuring Mission (TRMM) satellite data. The change analyses were performed to figure out the periodic morphodynamic variations and the impact of the flood on the selected meandering structures. In conclusion, although the river water level increased by 0.4–5.9 meters with the flood occurred in January 2019, the sediment deposition areas reformed after the flood event, as the water level decreased. Two-year monitoring revealed that the sinuosity index (SI) values changed during the flood approached the pre-flood values over time. Moreover, it was observed that the amount of the deposited sediments in September 2019 approached that of June 2018.

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“…Additionally, the processing efficiency is low. Differently, Kavzoglu et al [32] applied a multi-resolution segmentation result to divide the image, which resulted in a better accuracy than undivided classification. Zhou et al [33] applied an image scene to divide the VHR images.…”

Section: Introductionmentioning

confidence: 99%

Farmland Extraction from High Spatial Resolution Remote Sensing Images Based on Stratified Scale Pre-Estimation

Ming

Zhou

et al. 2019

Remote Sensing

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Extracting farmland from high spatial resolution remote sensing images is a basic task for agricultural information management. According to Tobler’s first law of geography, closer objects have a stronger relation. Meanwhile, due to the scale effect, there are differences on both spatial and attribute scales among different kinds of objects. Thus, it is not appropriate to segment images with unique or fixed parameters for different kinds of objects. In view of this, this paper presents a stratified object-based farmland extraction method, which includes two key processes: one is image region division on a rough scale and the other is scale parameter pre-estimation within local regions. Firstly, the image in RGB color space is converted into HSV color space, and then the texture features of the hue layer are calculated using the grey level co-occurrence matrix method. Thus, the whole image can be divided into different regions based on the texture features, such as the mean and homogeneity. Secondly, within local regions, the optimal spatial scale segmentation parameter was pre-estimated by average local variance and its first-order and second-order rate of change. The optimal attribute scale segmentation parameter can be estimated based on the histogram of local variance. Through stratified regionalization and local segmentation parameters estimation, fine farmland segmentation can be achieved. GF-2 and Quickbird images were used in this paper, and mean-shift and multi-resolution segmentation algorithms were applied as examples to verify the validity of the proposed method. The experimental results have shown that the stratified processing method can release under-segmentation and over-segmentation phenomena to a certain extent, which ultimately benefits the accurate farmland information extraction.

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A Region-Based Multi-Scale Approach for Object-Based Image Analysis

Cited by 9 publications

References 18 publications

A local segmentation parameter optimization approach for mapping heterogeneous urban environments using VHR imagery

A local segmentation parameter optimization approach for mapping heterogeneous urban environments using VHR imagery

UAV-based evaluation of morphological changes induced by extreme rainfall events in meandering rivers

Farmland Extraction from High Spatial Resolution Remote Sensing Images Based on Stratified Scale Pre-Estimation

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