Spectral-texture approach to hyperspectral image analysis for plant classification with SVMs

AlSuwaidi, Ali; Grieve, Bruce; Yin, Hujun

doi:10.1109/ist.2017.8261496

Cited by 3 publications

(9 citation statements)

References 21 publications

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“…This framework consists of two stages: feature and decision fusions. The first stage, including selection, is based on our previous work introduced in [2,24], while the goal of the second stage is to fuse texture features based on selected spectral bands (selected fusion). A scheme of boosting weak spectral features from textural information is also considered in the second fusion stage.…”

Section: Several Methods Can Be Employed To Fuse Featuresmentioning

confidence: 99%

“…Two extensions (selected fusion [2,24] and a new one termed boosting fusion) have been made to the core of our proposed framework to enhance the overall classification performance. In the selected fusion, the unweighted majority voting is used to fuse spectral and texture information and then another voting stage is used to form the final decision.…”

Section: Proposed Framework and Extensionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The number of imaging modalities has increased with the evolution of imaging technology [1]. Hyperspectral imaging is one of these modalities that has triggered a considerable amount of interest in exploring innovative research missions [2]. It can be considered to be a natural extension of multispec tral imaging in terms of individual bandwidth and spectrum range.…”

Section: Introductionmentioning

confidence: 99%

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Combining spectral and texture features in hyperspectral image analysis for plant monitoring

AlSuwaidi

Grieve

Yin

2018

Meas. Sci. Technol.

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A texture enhanced spectral analysis framework is proposed for classifying hyperspectral images of plants of different conditions. Differentiating different plant conditions is important to precision agriculture as it helps detect diseases and stresses and optimise growth control. Advanced machine learning techniques are used to identify distinctive features in the spectral domain of hyperspectral images. In addition, texture properties are explored in the subband images. The framework integrates these two levels of properties at both feature extraction and classifying decision stages. The main crux of the work lies in the use of the significant spectral and texture features and a decision fusion mechanism to enhance the image properties, thus improving classification accuracy. Two hyperspectral datasets, originated from proximal hyperspectral systems, were used in the evaluation and significant improvements in classification accuracy achieved.

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Section: Several Methods Can Be Employed To Fuse Featuresmentioning

confidence: 99%

Section: Proposed Framework and Extensionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combining spectral and texture features in hyperspectral image analysis for plant monitoring

AlSuwaidi

Grieve

Yin

2018

Meas. Sci. Technol.

Self Cite

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“…The texture features obtained with a MRF approach have been used for glaucoma detection [189]. They also have proven to give interesting results for hyper-spectral image analysis for plant classification [190] and for image classification [191].…”

Section: C: Examples Of Applicationsmentioning

confidence: 99%

Texture Feature Extraction Methods: A Survey

2019

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Texture analysis is used in a very broad range of fields and applications, from texture classification (e.g., for remote sensing) to segmentation (e.g., in biomedical imaging), passing through image synthesis or pattern recognition (e.g., for image inpainting). For each of these image processing procedures, first, it is necessary to extract-from raw images-meaningful features that describe the texture properties. Various feature extraction methods have been proposed in the last decades. Each of them has its advantages and limitations: performances of some of them are not modified by translation, rotation, affine, and perspective transform; others have a low computational complexity; others, again, are easy to implement; and so on. This paper provides a comprehensive survey of the texture feature extraction methods. The latter are categorized into seven classes: statistical approaches, structural approaches, transform-based approaches, model-based approaches, graph-based approaches, learning-based approaches, and entropybased approaches. For each method in these seven classes, we present the concept, the advantages, and the drawbacks and give examples of application. This survey allows us to identify two classes of methods that, particularly, deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet.

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Segmentation of weeds and crops using multispectral imaging and CRF-enhanced U-Net

Sahin

Miftahushudur

Grieve

et al. 2023

Computers and Electronics in Agriculture

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Spectral-texture approach to hyperspectral image analysis for plant classification with SVMs

Cited by 3 publications

References 21 publications

Combining spectral and texture features in hyperspectral image analysis for plant monitoring

Combining spectral and texture features in hyperspectral image analysis for plant monitoring

Texture Feature Extraction Methods: A Survey

Segmentation of weeds and crops using multispectral imaging and CRF-enhanced U-Net

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