Texture augmented analysis of high resolution satellite imagery in detecting invasive plant species

Tsai, Fuan; Chou, Ming Jhong

doi:10.1080/02533839.2006.9671155

Cited by 38 publications

(23 citation statements)

References 30 publications

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“…where Z(xi) and Z(xi + h) are two values of the same function Z(x) at a distance h (lag); N is the total number of pairs at given lag h. For digital images; i.e., for digital functions in two dimensions-the optimal window size to describe a specific kind of coverage is the lag (range) that results in the maximum variability (sill) of a scene structure [43]. This method was applied on a sample set of pixels, selected on the Red_S channel, for each tree species.…”

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

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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%

Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

Gini

Sona

Ronchetti

et al. 2018

IJGI

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“…Textural features represent the measure of the regularity, the smoothness, and the coarseness of an image. Texture contained in VHR satellite imagery should contain useful information to extract regions of vegetation from an image as demonstrated by Tsai and Chou [36] who applied the Grey Level Co-Occurrence Matrix (GLCM) to Quickbird imagery to detect invasive plant species. We used the GLCM, one of the most widely used texture measures and first introduced by Haralick et al [37].…”

Section: Extraction Of Spectral and Textural Featuresmentioning

confidence: 99%

“Kill Two Birds with One Stone”: Urban Tree Species Classification Using Bi-Temporal Pléiades Images to Study Nesting Preferences of an Invasive Bird

et al. 2017

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This study presents the results of object-based classifications assessing the potential of bi-temporal Pléiades images for mapping broadleaf and coniferous tree species potentially used by the ring-necked parakeet Psittacula krameri for nesting in the urban area of Marseille, France. The first classification was performed based solely on a summer Pléiades image (acquired on 28 July 2015) and the second classification based on bi-temporal Pléiades images (a spring image acquired on 24 March 2016 and the summer image). An ensemble of spectral and textural features was extracted from both images and two machine-learning classifiers were used, Random Forest (RF) and Support Vector Machine (SVM). Regardless of the classifiers, model results suggest that classification based on bi-temporal Pléiades images produces more satisfying results, with an overall accuracy 11.5-13.9% higher than classification using the single-date image. Textural and spectral features extracted from the blue and the NIR bands were consistently ranked among the most important features. Regardless of the classification scheme, RF slightly outperforms SVM. RF classification using bi-temporal Pléiades images allows identifying 98.5% of the tree species used by the ring-necked parakeet for nesting, highlighting the promising value of remote sensing techniques to assess the ecological requirements of fauna in urban areas.

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“…Among these features, homogeneity, contrast, entropy, and correlation have been reported to be effective in discriminating spatially heterogeneous images [69][70][71]. In this study, these four textural features were adopted in our proposed method as input features of a classifier, which were calculated as follows: …”

Section: Textural Feature Extractionmentioning

confidence: 99%

Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery

Xia

Qin

et al. 2018

Remote Sensing

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Abstract:Mangrove forests, which are essential for stabilizing coastal ecosystems, have been suffering from a dramatic decline over the past several decades. Mapping mangrove forests using satellite imagery is an efficient way to provide key data for mangrove forest conservation. Since mangrove forests are periodically submerged by tides, current methods of mapping mangrove forests, which are normally based on single-date, remote-sensing imagery, often underestimate the spatial distribution of mangrove forests, especially when the images used were recorded during high-tide periods. In this paper, we propose a new method of mapping mangrove forests based on multi-tide, high-resolution satellite imagery. In the proposed method, a submerged mangrove recognition index (SMRI), which is based on the differential spectral signature of mangroves under high and low tides from multi-tide, high-resolution satellite imagery, is designed to identify submerged mangrove forests. The proposed method applies the SMRI values, together with textural features extracted from high-resolution imagery and geographical features of mangrove forests, to an object-based support vector machine (SVM) to map mangrove forests. The proposed method was evaluated via a case study with GF-1 images (high-resolution satellites launched by China) in Yulin City, Guangxi Zhuang Autonomous Region of China. The results show that our proposed method achieves satisfactory performance, with a kappa coefficient of 0.86 and an overall accuracy of 94%, which is better than results obtained from object-based SVMs that use only single-date, remote sensing imagery.

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Texture augmented analysis of high resolution satellite imagery in detecting invasive plant species

Cited by 38 publications

References 30 publications

Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

“Kill Two Birds with One Stone”: Urban Tree Species Classification Using Bi-Temporal Pléiades Images to Study Nesting Preferences of an Invasive Bird

Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery

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