Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation

Shi, Xue; Li, Yu; Zhao, Quanhua

doi:10.3390/rs12071219

Cited by 15 publications

(13 citation statements)

References 31 publications

(42 reference statements)

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“…Binary is also known as logistic regression models, which builds models with only two outputs:-pass/fail, presence/absence [44] . We use count/Poisson regression models if the dependent variables are the counts/number of occurrences of an event [45] . The dependent variable cannot be negative or decimal values [45] .…”

Section: Linear Regressionmentioning

confidence: 99%

Machine learning application in GIS and remote sensing: An overview

Upreti¹

2022

IJMRGE

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Machine learning (ML) is a subdivision of artificial intelligence in which the machine learns from machine-readable data and information. It uses data, learns the pattern and predicts the new outcomes. Its popularity is growing because it helps to understand the trend and provides a solution that can be either a model or a product. Applications of ML algorithms have increased drastically in G.I.S. and remote sensing in recent years. It has a broad range of applications, from developing energy-based models to assessing soil liquefaction to creating a relation between air quality and mortality. Here, in this paper, we discuss the most popular supervised ML models (classification and regression) in G.I.S. and remote sensing. The motivation for writing this paper is that ML models produce higher accuracy than traditional parametric classifiers, especially for complex data with many predictor variables. This paper provides a general overview of some popular supervised non-parametric ML models that can be used in most of the G.I.S. and remote sensing based projects. We discuss classification (Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT)) and regression models (Random Forest (RF), Support Vector Machine (SVM), Linear and Non-Linear) here. Therefore, the article can be a guide to those interested in using ML models in their G.I.S. and remote sensing based projects.

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Section: Linear Regressionmentioning

confidence: 99%

Machine learning application in GIS and remote sensing: An overview

Upreti¹

2022

IJMRGE

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“…To accommodate the nonuniformity of trajectory data, a self-adopting machine learning algorithm GMM (Gaussian mixture model) is adopted. The GMM is a classical machine learning algorithm that is most widely used for feature recognition, data classification, and image segmentation [41,42]. The GMM assumes that all the data are generated from a superposition of a finite number of Gaussian distributions with some unknown parameters.…”

Section: Gaussian Mixture Modelmentioning

confidence: 99%

Exploring the Attractiveness of Residential Areas for Human Activities Based on Shared E-Bike Trajectory Data

Cheng

et al. 2020

IJGI

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Human activities generate diverse and sophisticated functional areas and may impact the existing planning of functional areas. Understanding the relationship between human activities and functional areas is key to identifying the real-time urban functional areas based on trajectories. Few previous studies have analyzed the interactive information on humans and regions for functional area identification. The relationship between human activities and residential areas is the most representative for urban functional areas because residential areas cover a wide range and are closely connected with human life. The aim of this paper is to propose the CARA (Commuting Activity and Residential Area) model to quantify the correlation between human activities and urban residential areas. In this model, human activities are represented by hot spots extracted by the Gaussian Mixture Model algorithm while residential areas are represented by POI (point of interest) data. The model shows that human activities and residential areas present a logarithmic relationship. The CARA model is further assessed by retrieving urban residential areas in Tengzhou City from shared e-bike trajectories. Compared with the actual map, the accuracy reaches 83.3%, thus demonstrating the model’s reliability and feasibility. This study provides a new method for functional areas identification based on trajectory data, which is helpful for formulating the urban people-oriented policies.

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“…In the light of the Bayesian theorem, the HGMM and the prior distributions of parameters are combined to establish the segmentation model. [ 11 ]. Wang Y et al proposed a hierarchical Gaussian mixture model segmentation algorithm on the basis of Markov random field (MRF).…”

Section: Introductionmentioning

confidence: 99%

NeDSeM: Neutrosophy Domain-Based Segmentation Method for Malignant Melanoma Images

Bian

Pan

Zhang

et al. 2022

Entropy

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Skin lesion segmentation is the first and indispensable step of malignant melanoma recognition and diagnosis. At present, most of the existing skin lesions segmentation techniques often used traditional methods like optimum thresholding, etc., and deep learning methods like U-net, etc. However, the edges of skin lesions in malignant melanoma images are gradually changed in color, and this change is nonlinear. The existing methods can not effectively distinguish banded edges between lesion areas and healthy skin areas well. Aiming at the uncertainty and fuzziness of banded edges, the neutrosophic set theory is used in this paper which is better than fuzzy theory to deal with banded edge segmentation. Therefore, we proposed a neutrosophy domain-based segmentation method that contains six steps. Firstly, an image is converted into three channels and the pixel matrix of each channel is obtained. Secondly, the pixel matrixes are converted into Neutrosophic Set domain by using the neutrosophic set conversion method to express the uncertainty and fuzziness of banded edges of malignant melanoma images. Thirdly, a new Neutrosophic Entropy model is proposed to combine the three memberships according to some rules by using the transformations in the neutrosophic space to comprehensively express three memberships and highlight the banded edges of the images. Fourthly, the feature augment method is established by the difference of three components. Fifthly, the dilation is used on the neutrosophic entropy matrixes to fill in the noise region. Finally, the image that is represented by transformed matrix is segmented by the Hierarchical Gaussian Mixture Model clustering method to obtain the banded edge of the image. Qualitative and quantitative experiments are performed on malignant melanoma image dataset to evaluate the performance of the NeDSeM method. Compared with some state-of-the-art methods, our method has achieved good results in terms of performance and accuracy.

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Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation

Cited by 15 publications

References 31 publications

Machine learning application in GIS and remote sensing: An overview

Machine learning application in GIS and remote sensing: An overview

Exploring the Attractiveness of Residential Areas for Human Activities Based on Shared E-Bike Trajectory Data

NeDSeM: Neutrosophy Domain-Based Segmentation Method for Malignant Melanoma Images

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