Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities

Sunar, Filiz; Özkan, Coşkun; Taberner, Malcolm

doi:10.1080/0143116031000150077

Cited by 193 publications

(83 citation statements)

References 16 publications

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“…Global or regional land cover maps from these remotely sensed data are typically based on image classification and a great number of classification methods have been developed, ranging from classical ones such as minimum distance [1] to more advanced ones such as support vector machine [2]. Generally, two groups of classification methods exist: per-pixel (hard) and sub-pixel (soft) classification [3].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Positional Errors on Soft Classification Accuracy Assessment: A Simulation Analysis

Congalton

Pan

2015

Remote Sensing

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Abstract:Validating or accessing the accuracy of soft classification maps has rapidly developed over the past few years. This assessment employs a soft error matrix as generalized from the traditional, hard classification error matrix. However, the impact of positional error on the soft classification is uncertain and whether the well-accepted half-pixel registration accuracy is suitable for the soft classification accuracy assessment is unknown. In this paper, a simulation analysis was conducted to examine the influence of positional error on the overall accuracy (OA) and kappa in soft classification accuracy assessment under different landscape conditions (i.e., spatial characteristics and spatial resolutions). Results showed that with positional error ranging from 0 to 3 soft pixels, the OA-error varied from 0 to 44.6 percent while the kappa-error varied from 0 to 93.7 percent. Landscape conditions with smaller mean patch size (MPS) and greater fragmentation produced greater positional error impact on the accuracy measures at spatial resolutions of 1 and 2 unit distances. However, this trend did not hold for spatial resolutions of 5 and 10 unit distances. A half of a pixel was not sufficient to keep the overall accuracy error and kappa error under 10 percent. The results indicate that for soft classification accuracy assessment the requirement for registration accuracy is higher and depends greatly on the landscape characteristics. There is a great need to consider positional error for validating soft classification maps of different spatial resolutions. OPEN ACCESSRemote Sens. 2015, 7 580

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Section: Introductionmentioning

confidence: 99%

The Impact of Positional Errors on Soft Classification Accuracy Assessment: A Simulation Analysis

Congalton

Pan

2015

Remote Sensing

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“…The main assumption of MLA is that the spectral response for each class is normally distributed [10]. Moreover, for an (m) class case, the decision process calculates the probability of a pixel belonging to each of (m) predetermined classes, and the class with the highest probability is assigned to that pixel [11]. The estimated probability density function or Likelihood function for a given class (wi) is calculated as :…”

Section: Methodsmentioning

confidence: 99%

“…This image was used to generate a surface contour and slope map which very useful to delineate the boundary of sea water inundation. Furthermore, analysis was continued by modeling of water inundation height (run-up) using mathematical equation as follows [11].…”

Section: Methodsmentioning

confidence: 99%

Sea level rise impact modelling on small islands: case study gili raja island of east java

Hidayah¹,

Prayogo²,

Wardhani³

2018

MATEC Web Conf.

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Abstract. Coastal regions and small islands are areas that will be adversely affected by the phenomenon of sea level rise globally. In general, Sea Level Rise (SLR) will result in coastal impacts as follows: increased frequency and intensity of floods, changes in ocean currents and widespread intrusion of sea water. This research was conducted in Gili Raja Island of Sumenep Madura. Objectives of this research were to demonstrate the ability of combining remote sensing and GIS method to determine the impact of SLR on a small island and to model its scale using different scenario. GIS based run-up model were performed to estimate and predict the impact of SLR to the island's area. Three water level scenario (0.5 m, 1.0 m and 1.5 m) were applied. The results showed that in the first scenario 8.73% of the island was flooded by sea water, furthermore in two other scenario the flooded area was increase significantly (15.88% and 22.38%).

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“…In recent years, aerial 15 photos or satellite images have been commonly used in post disaster interpretations and assessments of landslide damage on large-area slopes (Erbek et al, 2004;Lillesand et al, 2004;Nikolakopoulos et al, 2005;Lin et al, 2005;Chen et al, 2009;Otukei and Blaschke, 2010;Chen et al, 2013a). Satellite images offer the advantages of short data acquisition cycles, swift understanding of surface changes, large data ranges, and being low cost, particularly for mountainous and inaccessible areas.…”

Section: (Mhem)mentioning

confidence: 99%

Scale and spatial distribution assessment of rainfall-induced landslides along mountain roads

Tseng

Chen

Wu³

2017

Preprint

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Abstract. This study focused on landslides along mountain roads that were caused by Typhoons Nanmadol (2011) and Kong-rey (2013). Image interpretation techniques were employed to interpret satellite images captured before and after the typhoons to derive the changes in slope surfaces. The multivariate hazard evaluation method was adopted to establish a 10 landslide susceptibility assessment model. We then mapped landslide susceptibility and locations to determine the relationship between the spatial distribution of landslide areas and the natural environment along mountain roads. The results can serve as a reference for preventing and mitigating slope disasters on mountain roads.

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Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities

Cited by 193 publications

References 16 publications

The Impact of Positional Errors on Soft Classification Accuracy Assessment: A Simulation Analysis

The Impact of Positional Errors on Soft Classification Accuracy Assessment: A Simulation Analysis

Sea level rise impact modelling on small islands: case study gili raja island of east java

Scale and spatial distribution assessment of rainfall-induced landslides along mountain roads

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