Applicability of Hyperspectral Imaging Technology for the Check of Cadastre's Land Category

Lee, In-Su; Hyun, Chang Ho

doi:10.7848/ksgpc.2014.32.4-2.421

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…L ARGE sets of high-order data have become ubiquitous across science and engineering disciplines, primarily due to recent advances in sensing technologies and increasingly affordable recording devices. Remote Sensing is not an exception, where large hyperspectral data -collections of high-order images-are becoming available and used for a variety of applications, including urban and rural planning, change detection, mapping, geographic information systems, monitoring, housing value, and navigation [1], [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Rank-R FNN: A Tensor-Based Learning Model for High-Order Data Classification

et al. 2021

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An increasing number of emerging applications in data science and engineering are based on multidimensional and structurally rich data. The irregularities, however, of high-dimensional data often compromise the effectiveness of standard machine learning algorithms. We hereby propose the Rank-R Feedforward Neural Network (FNN), a tensor-based nonlinear learning model that imposes Canonical/Polyadic decomposition on its parameters, thereby offering two core advantages compared to typical machine learning methods. First, it handles inputs as multilinear arrays, bypassing the need for vectorization, and can thus fully exploit the structural information along every data dimension. Moreover, the number of the model's trainable parameters is substantially reduced, making it very efficient for small sample setting problems. We establish the universal approximation and learnability properties of Rank-R FNN, and we validate its performance on real-world hyperspectral datasets. Experimental evaluations show that Rank-R FNN is a computationally inexpensive alternative of ordinary FNN that achieves state-of-theart performance on higher-order tensor data.

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Section: Introductionmentioning

confidence: 99%

Rank-R FNN: A Tensor-Based Learning Model for High-Order Data Classification

et al. 2021

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Extraction of Spatial Characteristics of Cadastral Land Category from RapidEye Satellite Images

La¹,

Huh²,

Eo³

et al. 2014

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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With rapid land development, land category should be updated on a regular basis. However, manual field surveys have certain limitations. In this study, attempts were made to extract a feature vector considering spectral signature by parcel, PIMP (Percent Imperviousness), texture, and VIs (Vegetation Indices) based on RapidEye satellite image and cadastral map. A total of nine land categories in which feature vectors were significantly extracted from the images were selected and classified using SVM (Support Vector Machine). According to accuracy assessment, by comparing the cadastral map and classification result, the overall accuracy was 0.74. In the paddy-field category, in particular, PO acc. (producer's accuracy) and US acc. (user's accuracy) were highest at 0.85 and 0.86, respectively. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Applicability of Hyperspectral Imaging Technology for the Check of Cadastre's Land Category

Cited by 2 publications

References 13 publications

Rank-R FNN: A Tensor-Based Learning Model for High-Order Data Classification

Rank-R FNN: A Tensor-Based Learning Model for High-Order Data Classification

Extraction of Spatial Characteristics of Cadastral Land Category from RapidEye Satellite Images

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