Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery

Feng, Ruyi; Wang, Lizhe; Zhong, Yanfei

doi:10.3390/rs10101546

Cited by 12 publications

(6 citation statements)

References 51 publications

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“…Similarly, Feng et al . (2018) note that linear mixing approaches do not always give consistent and easy to interpret results when predicting class memberships.…”

Section: Discussionmentioning

confidence: 99%

UAV‐based training for fully fuzzy classification of Sentinel‐2 fluvial scenes

Carbonneau

Belletti

Micotti

et al. 2020

Earth Surf Processes Landf

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An estimated 76% of global stream area is occupied by channels with widths above 30m. Sentinel-2 imagery with resolutions of 10m could supply information about the composition of river corridors at national and global scales. Fuzzy classification models that infer sub-pixel composition could further be used to compensate for small channel widths imaged at 10m of spatial resolution. A major challenge to this approach is the acquisition of suitable training data useable in machine learning models that can predict land-cover type information from image radiance values. In this contribution, we present a method which combines unmanned aerial vehicles (UAVs) and Sentinel-2 imagery in order to develop a fuzzy classification approach capable of large-scale investigations. Our approach uses hyperspatial UAV imagery in order to derive high-resolution class information that can be used to train fuzzy classification models for Sentinel-2 data where all bands are super-resolved to a spatial resolution of 10m. We use a multi-temporal UAV dataset covering an area of 5.25km 2. Using a novel convolutional neural network (CNN) classifier, we predict sub-pixel membership for Sentinel-2 pixels in the fluvial corridor as divided into classes of water, vegetation and dry sediment. Our CNN model can predict fuzzy class memberships with median errors from À5% to +3% and mean absolute errors from 10% to 20%. We also show that our CNN fuzzy predictor can be used to predict crisp classes with accuracies from 95.5% to 99.9%. Finally, we use an example to show how a fuzzy CNN model trained with localized UAV data can be applied to longer channel reaches and detect new vegetation growth. We therefore argue that the novel use of UAVs as field validation tools for freely available satellite data can bridge the scale gap between local and regional fluvial studies.

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“…Similarly, Feng et al . (2018) note that linear mixing approaches do not always give consistent and easy to interpret results when predicting class memberships.…”

Section: Discussionmentioning

confidence: 99%

UAV‐based training for fully fuzzy classification of Sentinel‐2 fluvial scenes

Carbonneau

Belletti

Micotti

et al. 2020

Earth Surf Processes Landf

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“…where r is the initial state; k is the feature that is most relevant to r; sign f T k r is the forward direction, namely, β k is updated in the direction of sign f T k r ; and δ k is the step size [59].…”

Section: Least Angle Regression (Lars)mentioning

confidence: 99%

Performance Comparison of Machine Learning Algorithms for Estimating the Soil Salinity of Salt-Affected Soil Using Field Spectral Data

et al. 2019

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Salt-affected soil is a prominent ecological and environmental problem in dry farming areas throughout the world. China has nearly 9.9 million km2 of salt-affected land. The identification, monitoring, and utilization of soil salinization have become important research topics for promoting sustainable progress. In this paper, using field-measured spectral data and soil salinity parameter data, through analysis and transformation of spectral data, five machine learning models, namely, random forest regression (RFR), support vector regression (SVR), gradient-boosted regression tree (GBRT), multilayer perceptron regression (MLPR), and least angle regression (Lars) are compared. The following performance measures of each model were evaluated: the collinear problems, handling data noise, stability, and the accuracy. In terms of these four aspects, the performance of each model on estimating soil salinity is evaluated. The results demonstrate that among the five models, RFR has the best performance in dealing with collinearity, RFR and MLPR have the best performance in dealing with data noise, and the SVR model is the most stable. The Lars model has the highest accuracy, with a determination coefficient (R2) of 0.87, ratio of performance to deviation (RPD) of 2.67, root mean square error (RMSE) of 0.18, and mean absolute percentage error (MAPE) of 0.11. Then, the comprehensive comparison and analysis of the five models are carried out, and it is found that the comprehensive performance of RFR model is the best; hence, this method is most suitable for estimating soil salinity using hyperspectral data. This study can provide a reference for the selection of regression methods in subsequent studies on estimating soil salinity using hyperspectral data.

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“…H YPERSPECTRAL imagery is a very important data source for deriving detailed thematic information on the earth surface, since it contains hundreds of narrow spectral channels to distinguish the subtle spectral difference of various materials [1], [2]. Therefore, hyperspectral image classification is an enduring research topic [3].…”

Section: Introductionmentioning

confidence: 99%

Spectral-Spatial Classification Integrating Band Selection for Hyperspectral Imagery With Severe Noise Bands

Zhao

Tian

Geis

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Spectral-spatial classification for hyperspectral imagery has been receiving much attention, since the detailed spectral and rich spatial information of hyperspectral images can be fully exploited to improve the classification accuracy. However, when the original hyperspectral images have very noisy bands, these bands may have an unfavorable impact on the classification, and are often discarded in advance based on expert knowledge. In this study, a spectral-spatial conditional random field classification algorithm integrating band selection (CRFBS) is developed for hyperspectral imagery with severe noise bands. The proposed algorithm integrates band selection based on the relative utility of the spectral bands for classification. Consequently, negative effects of severe noise bands are eliminated and the need for high-quality image data is substantially reduced. In addition, the CRFBS algorithm makes comprehensive use of both the spectral and the spatial cues to improve the classification performance. The spectral cues are formulated by integrating the support vector machine and random forest algorithms to improve the spectral discriminative ability in the unary potentials, and the spatial information are modeled to consider the interactions between pixels in pairwise potentials. The experiments using different airborne and UAV-borne hyperspectral data verified the effectiveness of the CRFBS method. The CRFBS algorithm can achieve accurate interpretation of the various classification categories and a more than 3% improvement in classification accuracy, compared with the method using the original hyperspectral image with severe noise bands.

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Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery

Cited by 12 publications

References 51 publications

UAV‐based training for fully fuzzy classification of Sentinel‐2 fluvial scenes

UAV‐based training for fully fuzzy classification of Sentinel‐2 fluvial scenes

Performance Comparison of Machine Learning Algorithms for Estimating the Soil Salinity of Salt-Affected Soil Using Field Spectral Data

Spectral-Spatial Classification Integrating Band Selection for Hyperspectral Imagery With Severe Noise Bands

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