Target oriented dimensionality reduction of hyperspectral data by Kernel Fukunaga–Koontz Transform

Binol, Hamidullah; Ochilov, S.; Alam, Mohammad S.; Bal, Abdullah

doi:10.1016/j.optlaseng.2016.03.009

Cited by 10 publications

(7 citation statements)

References 22 publications

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“…It is clear from these studies that all methods have their strengths and limitations in agricultural studies [81]. We selected PCA because it is easy to implement, interpret, and understand, widely used [84], good with numerical variables [83], and successful at identifying the most important variables [85]. However, we encourage readers to explore multiple methods and approaches outlined in [80,81].…”

Section: Selection Of Ohnbs By Data Mining and Overcoming Data Redundmentioning

confidence: 99%

Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

Aneece

Thenkabail

2018

Remote Sensing

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As the global population increases, we face increasing demand for food and nutrition. Remote sensing can help monitor food availability to assess global food security rapidly and accurately enough to inform decision-making. However, advances in remote sensing technology are still often limited to multispectral broadband sensors. Although these sensors have many applications, they can be limited in studying agricultural crop characteristics such as differentiating crop types and their growth stages with a high degree of accuracy and detail. In contrast, hyperspectral data contain continuous narrowbands that provide data in terms of spectral signatures rather than a few data points along the spectrum, and hence can help advance the study of crop characteristics. To better understand and advance this idea, we conducted a detailed study of five leading world crops (corn, soybean, winter wheat, rice, and cotton) that occupy 75% and 54% of principal crop areas in the United States and the world respectively. The study was conducted in seven agroecological zones of the United States using 99 Earth Observing-1 (EO-1) Hyperion hyperspectral images from 2008–2015 at 30 m resolution. The authors first developed a first-of-its-kind comprehensive Hyperion-derived Hyperspectral Imaging Spectral Library of Agricultural crops (HISA) of these crops in the US based on USDA Cropland Data Layer (CDL) reference data. Principal Component Analysis was used to eliminate redundant bands by using factor loadings to determine which bands most influenced the first few principal components. This resulted in the establishment of 30 optimal hyperspectral narrowbands (OHNBs) for the study of agricultural crops. The rest of the 242 Hyperion HNBs were redundant, uncalibrated, or noisy. Crop types and crop growth stages were classified using linear discriminant analysis (LDA) and support vector machines (SVM) in the Google Earth Engine cloud computing platform using the 30 optimal HNBs (OHNBs). The best overall accuracies were between 75% to 95% in classifying crop types and their growth stages, which were achieved using 15–20 HNBs in the majority of cases. However, in complex cases (e.g., 4 or more crops in a Hyperion image) 25–30 HNBs were required to achieve optimal accuracies. Beyond 25–30 bands, accuracies asymptote. This research makes a significant contribution towards understanding modeling, mapping, and monitoring agricultural crops using data from upcoming hyperspectral satellites, such as NASA’s Surface Biology and Geology mission (formerly HyspIRI mission) and the recently launched HysIS (Indian Hyperspectral Imaging Satellite, 55 bands over 400–950 nm in VNIR and 165 bands over 900–2500 nm in SWIR), and contributions in advancing the building of a novel, first-of-its-kind global hyperspectral imaging spectral-library of agricultural crops (GHISA: www.usgs.gov/WGSC/GHISA).

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Section: Selection Of Ohnbs By Data Mining and Overcoming Data Redundmentioning

confidence: 99%

Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

Aneece

Thenkabail

2018

Remote Sensing

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“…KFKT has also proved to be immensely helpful in the field of face detection and recognition [7], helping to resolve difficulties arising out of hyperspectral image classification issues [8,9] as well as the dimensionality reduction of hyperspectral data [10]. Another new technique has been put forward by Binol et al that suggest employing differential evolution algorithm-based kernel parameter selection technique for radial basis function (RBF) kernel within KFKT [11].…”

Section: *Author For Correspondencementioning

confidence: 99%

“…Even though the majority or unweighted method is the easiest technique to combine, it may not provide the perfect results. Unlike in weighted voting, where each voter has samples of different weights and the final decision is taken accordingly (10).…”

Section: Constructing a Kfkt Ensemblementioning

confidence: 99%

A supervised discriminant subspaces-based ensemble learning for binary classification

Binol¹,

Cukur²,

Bal³

2016

IJACR

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“…Use of PCA for dimensionality reduction in HSI is a computationally suitable approach and it helps preserve the most of the variance of the raw data. Although PCA has some theoretical inadequacies [11,12] for use on remote sensing data, particularly hyperspectral images [13], the practical applications show that the results obtained using PCA are still competitive for the purpose of classification [14,15]. The ability of PCA is limited for high-dimensional data since it relies on only second-order statistical information.…”

Section: Introductionmentioning

confidence: 99%

“…(a) Obtain 's for corresponding pre-selected kernels using Eqs (12). and(13). (b) Create the kernel matrix K for each as in Eq (9)…”

mentioning

confidence: 99%

Ensemble Learning Based Multiple Kernel Principal Component Analysis for Dimensionality Reduction and Classification of Hyperspectral Imagery

Binol

2018

Mathematical Problems in Engineering

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Classification is one of the most challenging tasks of remotely sensed data processing, particularly for hyperspectral imaging (HSI). Dimension reduction is widely applied as a preprocessing step for classification; however the reduction of dimension using conventional methods may not always guarantee high classification rate. Principal component analysis (PCA) and its nonlinear version kernel PCA (KPCA) are known as traditional dimension reduction algorithms. In a previous work, a variant of KPCA, denoted as Adaptive KPCA (A-KPCA), is suggested to get robust unsupervised feature representation for HSI. The specified technique employs several KPCAs simultaneously to obtain better feature points from each applied KPCA which includes different candidate kernels. Nevertheless, A-KPCA neglects the influence of subkernels employing an unweighted combination. Furthermore, if there is at least one weak kernel in the set of kernels, the classification performance may be reduced significantly. To address these problems, in this paper we propose an Ensemble Learning (EL) based multiple kernel PCA (M-KPCA) strategy. M-KPCA constructs a weighted combination of kernels with high discriminative ability from a predetermined set of base kernels and then extracts features in an unsupervised fashion. The experiments on two different AVIRIS hyperspectral data sets show that the proposed algorithm can achieve a satisfactory feature extraction performance on real data.

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Target oriented dimensionality reduction of hyperspectral data by Kernel Fukunaga–Koontz Transform

Cited by 10 publications

References 22 publications

Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

A supervised discriminant subspaces-based ensemble learning for binary classification

Ensemble Learning Based Multiple Kernel Principal Component Analysis for Dimensionality Reduction and Classification of Hyperspectral Imagery

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