Optimizing Feature Selection of Individual Crop Types for Improved Crop Mapping

Yin, Leikun; You, Nanshan; Zhang, Geli; Huang, Jianxi; Dong, Jinwei

doi:10.3390/rs12010162

Cited by 40 publications

(12 citation statements)

References 63 publications

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“…To ensure world food security and agricultural resources management and conservation, it is of utmost importance that decision makers [1,2] are able to attain reliable and timely information on agricultural production and cropland mapping with sufficient accuracies [3][4][5]. Furthermore, up-to-date information on agricultural land use is crucial for crop planning and management [6] as well as retrieving site-specific agronomic information [7], e.g., biomass and yield estimation, crop phenology, plants stress monitoring, and soil productivity assessment [8].…”

Section: Introductionmentioning

confidence: 99%

Crop Mapping Using Random Forest and Particle Swarm Optimization based on Multi-Temporal Sentinel-2

et al. 2020

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Timely and accurate information on crop mapping and monitoring is necessary for agricultural resources management. Accordingly, the applicability of the proposed classification-feature selection ensemble procedure with different feature sets for crop mapping is investigated. Here, we produced various feature sets including spectral bands, spectral indices, variation of spectral index, texture, and combinations of features to map different types of crops. By using various feature sets and the random forest (RF) classifier, the crop maps were created. In aiming to determine the most relevant and distinctive features, the particle swarm optimization (PSO) and RF-variable importance measure feature selection methods were examined. The classification-feature selection ensemble procedure was adapted to combine the outputs of different feature sets from the better feature selection method using majority votes. Multi-temporal Sentinel-2 data has been used in Ghale-Nou county of Tehran, Iran. The performance of RF was efficient in crop mapping especially by spectral bands and texture in combination with other feature sets. Our results showed that the PSO-based feature selection leads to a more accurate classification than the RF-variable importance measure, in almost all feature sets for all crop types. The RF classifier-PSO ensemble procedure for crop mapping outperformed the RF classifier in each feature set with regard to the class-wise and overall accuracies (OA) (of about 2.7–7.4% increases in OA and 0.48–3.68% (silage maize), 0–1.61% (rice), 2.82–15.43% (alfalfa), and 10.96–41.13% (vegetables) improvement in F-scores for all feature sets). The proposed method could mainly be useful to differentiate between heterogeneous crop fields (e.g., vegetables in this study) due to their more obtained omission/commission errors reduction.

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Section: Introductionmentioning

confidence: 99%

Crop Mapping Using Random Forest and Particle Swarm Optimization based on Multi-Temporal Sentinel-2

et al. 2020

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“…Somers and Asner [24] proposed the separability index (SI) (Formula (1)), which is defined as the ratio of the spectral difference inter-and intra-classes. Spectral differences interand intra-classes are used to measure whether the feature set can effectively distinguish different land use types [30]. The feature set that makes the most consistent internally and at the same time has the greatest difference between categories is the best [31].…”

Section: Spectro-temporal Feature Selection Methods Based On the Weighted Separation Indexmentioning

confidence: 99%

Tropical Forests Classification Based on Weighted Separation Index from Multi-Temporal Sentinel-2 Images in Hainan Island

et al. 2021

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Tropical forests play a vital role in biodiversity conservation and the maintenance of sustainability. Although different time-series spatial resolution satellite images have provided opportunities for tropical forests classification, the complexity and diversity of vegetation types still pose challenges, especially for distinguishing different vegetation types. In this paper, we proposed a Spectro-Temporal Feature Selection (STFS) method based on the Weighted Separation Index (WSI) using multi-temporal Sentinel-2 data for mapping tropical forests in Jianfengling area, Hainan Province. The results showed that the tropical forests were classified with an overall accuracy of 93% and an F1 measure of 0.92 with multi-temporal Sentinel-2 data. As our results also revealed, the WSI based STFS method could be efficient in tropical forests classification by using a fewer feature subset compared with Variable Selection Using Random Forest (14 features and all 40 features, respectively) to achieve the same accuracy. The analysis also showed it was not advisable to only pursue a higher WSI value while ignoring the heterogeneity and diversity of features. This study demonstrated that the WSI can provide a new feature selection method for multi-temporal remote sensing image classification.

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“…The automatic spectro-temporal feature selection (ASTFS) [ 15 ] workflow provides a search over the feature space by incrementally extending the feature set with effective features (those that improve classification scores) ordered by their global separability index [ 16 ]. A similar approach is implemented in [ 17 ], where the authors use mutual information (MI) [ 18 ] and Fisher’s criterion to select the k most important features.…”

Section: Related Workmentioning

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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Optimizing Feature Selection of Individual Crop Types for Improved Crop Mapping

Cited by 40 publications

References 63 publications

Crop Mapping Using Random Forest and Particle Swarm Optimization based on Multi-Temporal Sentinel-2

Crop Mapping Using Random Forest and Particle Swarm Optimization based on Multi-Temporal Sentinel-2

Tropical Forests Classification Based on Weighted Separation Index from Multi-Temporal Sentinel-2 Images in Hainan Island

FASTENER Feature Selection for Inference from Earth Observation Data

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