Texture-based classification of sub-Antarctic vegetation communities on Heard Island

Murray, Humphrey; Lucieer, Arko; Williams, RN

doi:10.1016/j.jag.2010.01.006

Cited by 98 publications

(68 citation statements)

References 38 publications

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“…The GLCM algorithm computes a matrix that accounts for the difference in grey values between two pixels at a time, called the reference pixel and the neighbor pixel. In this case study, the neighbor pixel was located, each time, one pixel above and to the right of the reference pixel: according to Murray et al [38], this offset of (1,1) is the most commonly used.…”

Section: Processing Of Multispectral Orthophotosmentioning

confidence: 99%

“…The first method performs the classification of all the computed texture features, for each window size, with the Maximum Likelihood classifier, as already done by Murray et al [38], evaluating the behavior of classification accuracies. For each of the 24 different window sizes, the eight texture features were classified together with the Maximum Likelihood algorithm on training samples described in 2.4.…”

Section: Processing Of Multispectral Orthophotosmentioning

confidence: 99%

“…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. According to Murray et al [38], the optimal window size corresponds to where the slope of accuracy curve varies. In this case, the slope value is 1.3 for window size from 0 to 19, 0.3 from 21 to 27 and 0.5 after 27.…”

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%

Section: Processing Of Multispectral Orthophotosmentioning

confidence: 99%

See 1 more Smart Citation

Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

Gini

Sona

Ronchetti

et al. 2018

IJGI

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“…Spatial context features are considered to ease the process of classification and change detection using HR images. Murray et al (2010) [31] proved that the combination of spectral and textural features improves the performance of classification using HR images significantly. By contrast, classification results generated using spectral features result in lower performance.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

Wang

Liu

et al. 2018

Remote Sensing

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Abstract:To improve the accuracy of change detection in urban areas using bi-temporal high-resolution remote sensing images, a novel object-based change detection scheme combining multiple features and ensemble learning is proposed in this paper. Image segmentation is conducted to determine the objects in bi-temporal images separately. Subsequently, three kinds of object features, i.e., spectral, shape and texture, are extracted. Using the image differencing process, a difference image is generated and used as the input for nonlinear supervised classifiers, including k-nearest neighbor, support vector machine, extreme learning machine and random forest. Finally, the results of multiple classifiers are integrated using an ensemble rule called weighted voting to generate the final change detection result. Experimental results of two pairs of real high-resolution remote sensing datasets demonstrate that the proposed approach outperforms the traditional methods in terms of overall accuracy and generates change detection maps with a higher number of homogeneous regions in urban areas. Moreover, the influences of segmentation scale and the feature selection strategy on the change detection performance are also analyzed and discussed.

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“…Major techniques used for the detection, classification, and mapping of vegetation using remote sensing imagery are vegetation indices [20,21], spectral mixture analysis [22], temporal image-fusion [23,24], texture based measures [25], and supervised classification using machine learning classifiers such as maximum likelihood [26], random forests [27,28], decision trees [29], support vector machines [30], fuzzy learning [31], and neural networks [32][33][34]. Nevertheless, performance of existing large-scale land cover maps is limited to the discrimination of vegetation physiognomic types, which is still a challenging field [35].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

Sharma

Hara

Tateishi

2017

Land

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This paper presents an evaluation of the multi-source satellite datasets such as Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) with different spatial and temporal resolutions for nationwide vegetation mapping. The random forests based machine learning and cross-validation approach was applied for evaluating the performance of different datasets. Cross-validation with the rich-feature datasets-with a sample size of 390-showed that the MODIS datasets provided highest classification accuracy (Overall accuracy = 0.80, Kappa coefficient = 0.77) compared with Landsat 8 (Overall accuracy = 0.77, Kappa coefficient = 0.74) and Sentinel-2 (Overall accuracy = 0.66, Kappa coefficient = 0.61) datasets. As a result, temporally rich datasets were found to be crucial for the vegetation physiognomic classification. However, in the case of Landsat 8 or Sentinel-2 datasets, sample size could be increased excessively as around 9800 ground truth points could be prepared within 390 MODIS pixel-sized polygons. The increase in the sample size significantly enhanced the classification using Landsat-8 datasets (Overall accuracy = 0.86, Kappa coefficient = 0.84). However, Sentinel-2 datasets (Overall accuracy = 0.77, Kappa coefficient = 0.74) could not perform as much as the Landsat-8 datasets, possibly because of temporally limited datasets covered by the Sentinel-2 satellites so far. A combination of the Landsat-8 and Sentinel-2 datasets slightly improved the classification (Overall accuracy = 0.89, Kappa coefficient = 0.87) than using the Landsat 8 datasets separately. Regardless of the fact that Landsat 8 and Sentinel-2 datasets have lower temporal resolutions than MODIS datasets, they could enhance the classification of otherwise challenging vegetation physiognomic types due to possibility of training a wider variation of physiognomic types at 30 m resolution. Based on these findings, an up-to-date 30 m resolution vegetation map was generated by using Landsat 8 and Sentinel-2 datasets, which showed better accuracy than the existing map in Japan.

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Texture-based classification of sub-Antarctic vegetation communities on Heard Island

Cited by 98 publications

References 38 publications

Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach

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