Multi Color Channel vs. Multi Spectral Band Representations for Texture Classification

Vandenbroucke, Nicolas; Porebski, Alice

doi:10.1007/978-3-030-68790-8_25

Cited by 3 publications

(2 citation statements)

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“…Likewise, texture analysis of infra-red and/or multi-spectral images is not in the scope of the present work: again we refer the reader to refs. [5][6][7][8][9] for an overview on this topic.…”

Section: Introductionmentioning

confidence: 99%

Colour and Texture Descriptors for Visual Recognition: A Historical Overview

et al. 2021

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Colour and texture are two perceptual stimuli that determine, to a great extent, the appearance of objects, materials and scenes. The ability to process texture and colour is a fundamental skill in humans as well as in animals; therefore, reproducing such capacity in artificial (‘intelligent’) systems has attracted considerable research attention since the early 70s. Whereas the main approach to the problem was essentially theory-driven (‘hand-crafted’) up to not long ago, in recent years the focus has moved towards data-driven solutions (deep learning). In this overview we retrace the key ideas and methods that have accompanied the evolution of colour and texture analysis over the last five decades, from the ‘early years’ to convolutional networks. Specifically, we review geometric, differential, statistical and rank-based approaches. Advantages and disadvantages of traditional methods vs. deep learning are also critically discussed, including a perspective on which traditional methods have already been subsumed by deep learning or would be feasible to integrate in a data-driven approach.

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Section: Introductionmentioning

confidence: 99%

Colour and Texture Descriptors for Visual Recognition: A Historical Overview

et al. 2021

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“…Commonly used feature extraction methods include: CA (canonical analysis) [10], PCA (principal component analysis) [11], linear discriminant analysis (LDA) [12], etc. Feature selection is to select some bands from all bands based on the needs of data users, such as the band selection based on spatial autocorrelation in [13] or the embedded band selection scheme proposed in [14]. Data dimension reduction retains the main information of hyperspectral data while reducing the dimension of feature space, which is a vital preprocessing technology for hyperspectral images.…”

Section: Introductionmentioning

confidence: 99%

Improved Fusion of Spatial Information into Hyperspectral Classification through the Aggregation of Constrained Segment Trees: Segment Forest

Ling

Wang

2021

Remote Sensing

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Compared with traditional optical and multispectral remote sensing images, hyperspectral images have hundreds of bands that can provide the possibility of fine classification of the earth’s surface. At the same time, a hyperspectral image is an image that coexists with the spatial and spectral. It has become a hot research topic to combine the spatial spectrum information of the image to classify hyperspectral features. Based on the idea of spatial–spectral classification, this paper proposes a novel hyperspectral image classification method based on a segment forest (SF). Firstly, the first principal component of the image was extracted by the process of principal component analysis (PCA) data dimension reduction, and the data constructed the segment forest after dimension reduction to extract the non-local prior spatial information of the image. Secondly, the images’ initial classification results and probability distribution were obtained using support vector machine (SVM), and the spectral information of the images was extracted. Finally, the segment forest constructed above is used to optimize the initial classification results and obtain the final classification results. In this paper, three domestic and foreign public data sets were selected to verify the segment forest classification. SF effectively improved the classification accuracy of SVM, and the overall accuracy of Salinas was enhanced by 11.16%, WHU-Hi-HongHu by 15.89%, and XiongAn by 19.56%. Then, it was compared with six decision-level improved space spectrum classification methods, including guided filtering (GF), Markov random field (MRF), random walk (RW), minimum spanning tree (MST), MST+, and segment tree (ST). The results show that the segment forest-based hyperspectral image classification improves accuracy and efficiency compared with other algorithms, proving the algorithm’s effectiveness.

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