An object-specific image-texture analysis of H-resolution forest imagery

Hay, Geoffrey J.; Niemann, K. Olaf; McLean, G.F.

doi:10.1016/0034-4257(95)00189-1

Cited by 134 publications

(73 citation statements)

References 44 publications

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“…The second method, instead, is based on the empirical semivariance computation, which is a measure of data variations in the spatial domain [42]. The empirical semivariance γ(h) of a random function Z(x) is defined as Although it seems sensible to choose the largest window size, the optimal one is a compromise between having a good OA and minimizing the aforementioned edge effects.…”

Section: Processing Of Multispectral Orthophotosmentioning

confidence: 99%

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Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

Gini

Sona

Ronchetti

et al. 2018

IJGI

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This paper focuses on the use of ultra-high resolution Unmanned Aircraft Systems (UAS) imagery to classify tree species. Multispectral surveys were performed on a plant nursery to produce Digital Surface Models and orthophotos with ground sample distance equal to 0.01 m. Different combinations of multispectral images, multi-temporal data, and texture measures were employed to improve classification. The Grey Level Co-occurrence Matrix was used to generate texture images with different window sizes and procedures for optimal texture features and window size selection were investigated. The study evaluates how methods used in Remote Sensing could be applied on ultra-high resolution UAS images. Combinations of original and derived bands were classified with the Maximum Likelihood algorithm, and Principal Component Analysis was conducted in order to understand the correlation between bands. The study proves that the use of texture features produces a significant increase of the Overall Accuracy, whose values change from 58% to 78% or 87%, depending on components reduction. The improvement given by the introduction of texture measures is highlighted even in terms of User's and Producer's Accuracy. For classification purposes, the inclusion of texture can compensate for difficulties of performing multi-temporal surveys.

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Section: Processing Of Multispectral Orthophotosmentioning

confidence: 99%

Section: Processing Of Multispectral Orthophotosmentioning

confidence: 99%

Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

Gini

Sona

Ronchetti

et al. 2018

IJGI

View full text Add to dashboard Cite

show abstract

“…Availability of high spatial resolution optical data in recent years and existence of a high resolution scene model (H-resolution) [57] for most applications, motivated a shift from the adoption of the pixel as a unit of classification to object-based classification methods [28]. This -high resolution‖ situation where pixels are significantly smaller than object, is predominantly attractive for exploiting the specific advantages of the GEOBIA approaches based on the definition of regions upon contiguous pixels belonging to the same class [58].…”

Section: Image Segmentationmentioning

confidence: 99%

A Comparative Analysis of EO-1 Hyperion, Quickbird and Landsat TM Imagery for Fuel Type Mapping of a Typical Mediterranean Landscape

2014

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Forest fires constitute a natural disturbance factor and an agent of environmental change with local to global impacts on Earth's processes and functions. Accurate knowledge of forest fuel extent and properties can be an effective component for assessing the impacts of possible future wildfires on ecosystem services. Our study aims to evaluate and compare the spectral and spatial information inherent in the EO-1 Hyperion, Quickbird and Landsat TM imagery. The analysis was based on a support vector machine classification approach in order to discriminate and map Mediterranean fuel types. The fuel classification scheme followed a site-specific fuel model within the study area, which is suitable for fire behavior prediction and spatial simulation. The overall accuracy of the Quickbird-based fuel type mapping was higher than 74% with a quantity disagreement of 9% and an allocation disagreement of 17%. Both classifications from the Hyperion and Landsat TM fuel type maps presented approximately 70% overall accuracy and 16% allocation disagreement. The McNemar's test indicated that the overall accuracy differences between the three produced fuel type maps were not significant (p < 0.05). Based on both overall and individual higher accuracies obtained with the use of the Quickbird image, this study suggests that the high spatial resolution might be more decisive than the high spectral resolution in Mediterranean fuel type mapping.

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“…However, it is rare that a classification accuracy of greater than 80% can be achieved (except in the case of homogenous water bodies) using per-pixel classification (so-called hard classification) algorithms [42] due to the h-res problem [43]. This is where the increased spatial resolution of high-resolution imagery, while visually meaningful, confuses traditional classifiers, resulting in reduced classification accuracy.…”

Section: Progress In Image Analysismentioning

confidence: 99%

Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems—An Overview

et al. 2011

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Cities are complex systems composed of numerous interacting components that evolve over multiple spatio-temporal scales. Consequently, no single data source is sufficient to satisfy the information needs required to map, monitor, model, and ultimately understand and manage our interaction within such urban systems. Remote sensing technology provides a key data source for mapping such environments, but is not sufficient for fully understanding them. In this article we provide a condensed urban perspective of critical geospatial technologies and techniques: (i) Remote Sensing; (ii) Geographic Information Systems; (iii) object-based image analysis; and (iv) sensor webs, and recommend a holistic integration of these technologies within the language of open geospatial consortium (OGC) standards in-order to more fully understand urban systems. We then discuss the potential of this integration and conclude that this extends the monitoring and mapping options beyond "hard infrastructure" by addressing "humans as sensors", mobility and human-environment interactions, and future improvements to quality of life and of social infrastructures.

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An object-specific image-texture analysis of H-resolution forest imagery

Cited by 134 publications

References 44 publications

Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

A Comparative Analysis of EO-1 Hyperion, Quickbird and Landsat TM Imagery for Fuel Type Mapping of a Typical Mediterranean Landscape

Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems—An Overview

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