Parameter selection for region‐growing image segmentation algorithms using spatial autocorrelation

Espíndola, Giovana Mira de; Câmara, Gilberto; Reis, Ilka Afonso; Bins, Leonardo S.; Monteiro, Antônio Miguel Vieira

doi:10.1080/01431160600617194

Cited by 286 publications

(298 citation statements)

References 5 publications

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“…However, in the case of advanced per-pixel classification algorithms (such as the SOM neural network used here), an equally time consuming "trial-and-error" approach is also commonly required to optimise the network parameters. Nevertheless, it is worth noting that there are on-going attempts to devise more quantitative and automated segmentation parameter selection methods [63][64][65], which are anticipated to help increase the efficiency and optimisation of this process, and ultimately produce better results than currently attainable through "trial-and-error" [50].…”

Section: Lithological Contact Mapping Performancementioning

confidence: 99%

Evaluating the Use of an Object-Based Approach to Lithological Mapping in Vegetated Terrain

2016

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Abstract:Remote sensing-based approaches to lithological mapping are traditionally pixel-oriented, with classification performed on either a per-pixel or sub-pixel basis with complete disregard for contextual information about neighbouring pixels. However, intra-class variability due to heterogeneous surface cover (i.e., vegetation and soil) or regional variations in mineralogy and chemical composition can result in the generation of unrealistic, generalised lithological maps that exhibit the "salt-and-pepper" artefact of spurious pixel classifications, as well as poorly defined contacts. In this study, an object-based image analysis (OBIA) approach to lithological mapping is evaluated with respect to its ability to overcome these issues by instead classifying groups of contiguous pixels (i.e., objects). Due to significant vegetation cover in the study area, the OBIA approach incorporates airborne multispectral and LiDAR data to indirectly map lithologies by exploiting associations with both topography and vegetation type. The resulting lithological maps were assessed both in terms of their thematic accuracy and ability to accurately delineate lithological contacts. The OBIA approach is found to be capable of generating maps with an overall accuracy of 73.5% through integrating spectral and topographic input variables. When compared to equivalent per-pixel classifications, the OBIA approach achieved thematic accuracy increases of up to 13.1%, whilst also reducing the "salt-and-pepper" artefact to produce more realistic maps. Furthermore, the OBIA approach was also generally capable of mapping lithological contacts more accurately. The importance of optimising the segmentation stage of the OBIA approach is also highlighted. Overall, this study clearly demonstrates the potential of OBIA for lithological mapping applications, particularly in significantly vegetated and heterogeneous terrain.

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Section: Lithological Contact Mapping Performancementioning

confidence: 99%

Evaluating the Use of an Object-Based Approach to Lithological Mapping in Vegetated Terrain

2016

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“…Multiresolution segmentation is an option that generates segments of different size, and where the user has the option to choose optimal segment size (Baatz and Schape 2000). To choose such an optimal segment size objectively, Espindola et al (2006) proposed an objective function for measuring the quality of the resulting segments. Therefore, we created segments/objects of different scale parameters, with thresholds ranging from 10 to 50.…”

Section: Generation Of Hsu Through Segmentationmentioning

confidence: 99%

“…Therefore, Moran's I represent how, on average, the mean value of each region differs from the mean values of its neighbours. The objective function thus combines the variance measure and autocorrelation measure using a normalisation procedure (Espindola et al 2006):…”

Section: Generation Of Hsu Through Segmentationmentioning

confidence: 99%

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Probabilistic landslide hazard assessment using homogeneous susceptible units (HSU) along a national highway corridor in the northern Himalayas, India

et al. 2011

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Probabilistic landslide hazard assessment using homogeneous susceptible units (HSU) along a national highway corridor in the northern Himalayas, IndiaAbstract The increased socio-economic significance of landslides has resulted in the application of statistical methods to assess their hazard, particularly at medium scales. These models evaluate where, when and what size landslides are expected. The method presented in this study evaluates the landslide hazard on the basis of homogenous susceptible units (HSU). HSU are derived from a landslide susceptibility map that is a combination of landslide occurrences and geo-environmental factors, using an automated segmentation procedure. To divide the landslide susceptibility map into HSU, we apply a region-growing segmentation algorithm that results in segments with statistically independent spatial probability values. Independence is tested using Moran's I and a weighted variance method. For each HSU, we obtain the landslide frequency from the multi-temporal data. Temporal and size probabilities are calculated using a Poisson model and an inverse-gamma model, respectively. The methodology is tested in a landslide-prone national highway corridor in the northern Himalayas, India. Our study demonstrates that HSU can replace the commonly used terrain mapping units for combining three probabilities for landslide hazard assessment. A quantitative estimate of landslide hazard is obtained as a joint probability of landslide size, of landslide temporal occurrence for each HSU for different time periods and for different sizes.

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“…In unsupervised methods, intra-segment and inter-segment heterogeneity for each segment were calculated and some quality criteria were used for scoring and ranking without reference objects (Johnson et al, 2011). For instance, objective function and global score were determined to detect optimal scale parameters in unsupervised evaluation methods (Espindola et al 2006;Johnson et al, 2011;Gao et al, 2011), Being a popular scale determination tool, estimation of scale parameter (ESP) tool was developed for automated selection of scale parameter by Drăgut et al (2010). In the ESP tool, a graph is generated using local variance (LV) of image and rate of change (RoC) values of LV.…”

Section: Introductionmentioning

confidence: 99%

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

Kavzoğlu¹,

Erdemir²,

Tonbul³

2016

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

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Commission VII, WG VII/4KEY WORDS: Object Based Image Analysis, Region-based scale, Classification, Multiresolution Segmentation, Estimation of Scale Parameter (ESP). ABSTRACT:Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features.Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than imagebased single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.

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Parameter selection for region‐growing image segmentation algorithms using spatial autocorrelation

Cited by 286 publications

References 5 publications

Evaluating the Use of an Object-Based Approach to Lithological Mapping in Vegetated Terrain

Evaluating the Use of an Object-Based Approach to Lithological Mapping in Vegetated Terrain

Probabilistic landslide hazard assessment using homogeneous susceptible units (HSU) along a national highway corridor in the northern Himalayas, India

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

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