Comparison of standard maximum likelihood classification and polytomous logistic regression used in remote sensing

Hogland, John; Billor, Nedret; Anderson, Nathaniel

doi:10.5721/eujrs20134637

Cited by 57 publications

(45 citation statements)

References 40 publications

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“…In order to select the proper set of variables, a GLM is adopted, where the depended (classification) variable is the probability that a sub-cluster of pixels w k;i represents a missing plant. More in detail, a Binary MultivariateLogistic Regression (BMLR) model (Glonek & McCullagh, 1995) was selected which uses the logit link-function, the logarithm of odds ratio (Hogland, Billor, & Anderson, 2013).…”

Section: Missing Plant Detectionmentioning

confidence: 99%

Individual plant definition and missing plant characterization in vineyards from high-resolution UAV imagery

Primicerio

Caruso

Comba

et al. 2017

European Journal of Remote Sensing

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In the last few years, high-resolution imaging of vineyards, obtained by unmanned aerial vehicle recognitions, has provided new opportunities to obtain valuable information for precision farming applications. While available semi-automatic image processing algorithms are now able to detect parcels and extract vine rows from aerial images, the identification of single plant inside the rows is a problem still unaddressed. This study presents a new methodology for the segmentation of vine rows in virtual shapes, each representing a real plant. From the virtual shapes, an extensive set of features is discussed, extracted and coupled to a statistical classifier, to evaluate its performance in missing plant detection within a vineyard parcel. Passing from continuous images to a discrete set of individual plants results in a crucial simplification of the statistical investigation of the problem. ARTICLE HISTORY

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Section: Missing Plant Detectionmentioning

confidence: 99%

Individual plant definition and missing plant characterization in vineyards from high-resolution UAV imagery

Primicerio

Caruso

Comba

et al. 2017

European Journal of Remote Sensing

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“…Finally, all the values associated with each node in the new vector are equal to the shortest distance to the seed. In order to select the threshold for the seed, three classes with maximum likelihood measure [16] and for the other pixels one class with maximum likelihood measure are considered. Then the average of the shortest path for the pixels for which the specified class is one of the three classes considered for seed is calculated and displayed by b. Interval [ ] is used for the second stage segmentation.…”

Section: Shortest Path Methodsmentioning

confidence: 99%

Hyperspectral Image Segmentation using Homogeneous Area Limiting and Shortest Path Algorithm

Hajiani¹,

Mahmoodzadeh²

2017

ijacsa

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Abstract-Segmentation, as a preprocessing, plays an important role in hyperspectral images. In this paper, considering the similarity of neighboring pixels, using the size measure, the image spectrum is divided into several segments so that the existence of several sub areas in each segment is possible. Then, using the methods of area limiting and the shortest path to seed pixel, and considering the pixel spectra in all bands, the available areas in each section are separated. The area limiting method controls the amplitude changes of area pixels from seed pixel, and the shortest path method, considering the shortest path to seed, controls the size of area. The proposed method is implemented on AVIRIS images and in terms of the number of areas, the border between areas and the possibility of area interference show better results than other methods.

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“…Hogland et al (2013) [46] discuss the "desirable qualities" of logistic regression underscoring the rather unrestrictive model assumptions, the model's incorporation of both categorical and continuous variables into the classification scheme, ease of model comparisons, and a focus on direct modeling of class probabilities. Environmental variables have been paired with logistic regression to identify distributions of land cover [47] but coupled with multispectral data and derivatives, logistic regression has been utilized to classify land cover [48], identify areas of advancing land degradation [49], and mapped burned lands [50].…”

Section: Review Of Techniques For Remote Sensing Cropland In Savanna mentioning

confidence: 99%

Mapping Cropland in Smallholder-Dominated Savannas: Integrating Remote Sensing Techniques and Probabilistic Modeling

2015

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Traditional smallholder farming systems dominate the savanna range countries of sub-Saharan Africa and provide the foundation for the region's food security. Despite continued expansion of smallholder farming into the surrounding savanna landscapes, food insecurity in the region persists. Central to the monitoring of food security in these countries, and to understanding the processes behind it, are reliable, high-quality datasets of cultivated land. Remote sensing has been frequently used for this purpose but distinguishing crops under certain stages of growth from savanna woodlands has remained a major challenge. Yet, crop production in dryland ecosystems is most vulnerable to seasonal climate variability, amplifying the need for high quality products showing the distribution and extent of cropland. The key objective in this analysis is the development of a classification protocol for African savanna landscapes, emphasizing the delineation of cropland. We integrate remote sensing techniques with probabilistic modeling into an innovative workflow. We OPEN ACCESSRemote Sens. 2015, 7 15296 present summary results for this methodology applied to a land cover classification of Zambia's Southern Province. Five primary land cover categories are classified for the study area, producing an overall map accuracy of 88.18%. Omission error within the cropland class is 12.11% and commission error 9.76%.

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Comparison of standard maximum likelihood classification and polytomous logistic regression used in remote sensing

Cited by 57 publications

References 40 publications

Individual plant definition and missing plant characterization in vineyards from high-resolution UAV imagery

Individual plant definition and missing plant characterization in vineyards from high-resolution UAV imagery

Hyperspectral Image Segmentation using Homogeneous Area Limiting and Shortest Path Algorithm

Mapping Cropland in Smallholder-Dominated Savannas: Integrating Remote Sensing Techniques and Probabilistic Modeling

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