Feature Selection on Sentinel-2 Multispectral Imagery for Mapping a Landscape Infested by Parthenium Weed

Kiala, Zolo; Mutanga, Onisimo; Odindi, John; Peerbhay, Kabir

doi:10.3390/rs11161892

Cited by 29 publications

(18 citation statements)

References 53 publications

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“…FASTENER results were compared with the results of the KBEST algorithm and the RELIEFF algorithm. Although outdated, they are presented in the literature as the currently best-performing methods in the field of land cover classification [ 22 ]. To no surprise, FASTENER achieves better results.…”

Section: Resultsmentioning

confidence: 99%

FASTENER Feature Selection for Inference from Earth Observation Data

Koprivec

Kenda

Šircelj

2020

Entropy

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In this paper, a novel feature selection algorithm for inference from high-dimensional data (FASTENER) is presented. With its multi-objective approach, the algorithm tries to maximize the accuracy of a machine learning algorithm with as few features as possible. The algorithm exploits entropy-based measures, such as mutual information in the crossover phase of the iterative genetic approach. FASTENER converges to a (near) optimal subset of features faster than other multi-objective wrapper methods, such as POSS, DT-forward and FS-SDS, and achieves better classification accuracy than similarity and information theory-based methods currently utilized in earth observation scenarios. The approach was primarily evaluated using the earth observation data set for land-cover classification from ESA’s Sentinel-2 mission, the digital elevation model and the ground truth data of the Land Parcel Identification System from Slovenia. For land cover classification, the algorithm gives state-of-the-art results. Additionally, FASTENER was tested on open feature selection data sets and compared to the state-of-the-art methods. With fewer model evaluations, the algorithm yields comparable results to DT-forward and is superior to FS-SDS. FASTENER can be used in any supervised machine learning scenario.

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Section: Resultsmentioning

confidence: 99%

FASTENER Feature Selection for Inference from Earth Observation Data

Koprivec

Kenda

Šircelj

2020

Entropy

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“…The superiority of the new approach may be explained by the fact that it combines the strengths of its constituent feature selection methods. For instance, ReliefF, and SVM-b were, respectively, found to select small subset of optimal features and yield high classification accuracies [26], [28]. RF further makes the selection of the most relevant variables from the output of ReliefF and SVM-b [55].…”

Section: Discussionmentioning

confidence: 99%

“…This is because, among other factors, the spectral signature of herbaceous weeds is similar to that of the surrounding herbaceous plant species, such as grasses, resulting in low overall classification accuracies of infested landscapes [3], [43]. Nevertheless, strategic bands (e.g., bands in the red-edge, near-infra (NIR) and short-wave (SWIR) regions) were found to contribute the most in developing more accurate models of the spatial distribution of Parthenium weed [26], [44]. Although multidate images of Sentinel-2 provide additional spectral information due to the combination of several images, without the implementation of an efficient feature selection method, the contributing effects of these bands may be suppressed by redundant spectral bands in mapping Parthenium weed.…”

Section: Ecology Of the Parthenium Weedmentioning

confidence: 99%

“…In forward selection strategy, the search of relevant features starts with an empty set of features, then features are progressively added into larger subsets, whereas in backward elimination it starts with the full set of features and then progressively eliminates the least relevant ones [49]. According to Kiala et al [26], SVM-b is faster and more efficient in predictive accuracy than SVM-f. The code behind SVM-b can be found in the skfeature python package [50].…”

Section: ) Relieffmentioning

confidence: 99%

“…The use of hybrid feature methods is critical for accurately mapping invasive species using multidate images, since they can select fewer spectral bands or features with high accuracies. In their research, Kiala et al [26], Hall and Holmes [27], Kira and Rendell [28] found that ReliefF, a filter method, and SVM-b, a wrapper method, could, respectively, select a small subset of optimal features and yield high classification accuracies. Therefore, in this study, we propose a novel hybrid feature method that combines ReliefF, SVM-b and RF for handling correlated variables in a multidate Sentinel-2 image in mapping a landscape infested by the common Parthenium weed invasive plant species.…”

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confidence: 99%

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A Hybrid Feature Method for Handling Redundant Features in a Sentinel-2 Multidate Image for Mapping Parthenium Weed

Kiala

Mutanga

Odindi

et al. 2020

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

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Multidate images provide additional spectral information valuable for mapping plant species. However, correlated or redundant variables created from multiple image data and a large set of variables impede accurate and efficient landscape classification. Nevertheless, with the implementation of an appropriate feature selection method, the full potential of multidate images and subsequent increased classification accuracies may be achieved. Feature selection is a process of automatically selecting features in a dataset that contribute the most to the prediction of a target variable. It improves the classification process in terms of computation cost and predictive accuracy. Feature selection algorithms are typically classified into three groups: filters, wrappers, and embedded. Due to the inherent tradeoffs provided by different feature selection approaches, we hypothesize that a hybrid approach could optimize landscape delineation by leveraging on their complementary strengths. In this regard, we propose a new feature selection method that combines a filter ReliefF, a wrapper Support vector machines-backward (SVM-b), and the embedded random forest in mapping the noxious Parthenium weed using a Sentinel-2 multidate image. The new approach was compared to its three constituents based on the size of optimal feature subsets and classification accuracies. To make comparison and assessment of developed models from selected variables more valuable, the split between training and test datasets was implemented in three different ratios, namely, 3:1(Dataset 1), 1:1(Dataset 2); 1:3 (Dataset 3). The new approach was also evaluated against the multidate image without feature selection and a single-date image. Results showed that the new approach yielded the highest overall accuracies with the smallest optimal feature subsets. For instance, on Dataset 3, the OA was 86.6% with 22 optimal features, whereas it was 84.7% with 35 optimal features using SVM-b, which was the second most performing feature selection algorithm. The new approach yielded higher classification accuracies than the multidate image without feature selection and the single-date image. The findings of this study underscore the capability of hybrid methods to select fewer features from multidate images, with higher predictive accuracies than individual feature selection methods.

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Mapping temperate old-growth forests in Central Europe using ALS and Sentinel-2A multispectral data

Adiningrat,

Schlund,

Skidmore

et al. 2024

Environ Monit Assess

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Old-growth forests are essential to preserve biodiversity and play an important role in sequestering carbon and mitigating climate change. However, their existence across Europe is vulnerable due to the scarcity of their distribution, logging, and environmental threats. Therefore, providing the current status of old-growth forests across Europe is essential to aiding informed conservation efforts and sustainable forest management. Remote sensing techniques have proven effective for mapping and monitoring forests over large areas. However, relying solely on remote sensing spectral or structural information cannot capture comprehensive horizontal and vertical structure complexity profiles associated with old-growth forest characteristics. To overcome this issue, we combined spectral information from Sentinel-2A multispectral imagery with 3D structural information from high-density point clouds of airborne laser scanning (ALS) imagery to map old-growth forests over an extended area. Four features from the ALS data and fifteen from Sentinel-2A comprising raw band (spectral reflectance), vegetation indices (VIs), and texture were selected to create three datasets used in the classification process using the random forest algorithm. The results demonstrated that combining ALS and Sentinel-2A features improved the classification performance and yielded the highest accuracy for old-growth class, with an F1-score of 92% and producer’s and user’s accuracies of 93% and 90%, respectively. The findings suggest that features from ALS and Sentinel-2A data sensitive to forest structure are essential for identifying old-growth forests. Integrating open-access satellite imageries, such as Sentinel-2A and ALS data, can benefit forest managers, stakeholders, and conservationists in monitoring old-growth forest preservation across a broader spatial extent.

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Feature Selection on Sentinel-2 Multispectral Imagery for Mapping a Landscape Infested by Parthenium Weed

Cited by 29 publications

References 53 publications

FASTENER Feature Selection for Inference from Earth Observation Data

FASTENER Feature Selection for Inference from Earth Observation Data

A Hybrid Feature Method for Handling Redundant Features in a Sentinel-2 Multidate Image for Mapping Parthenium Weed

Mapping temperate old-growth forests in Central Europe using ALS and Sentinel-2A multispectral data

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