Gray Wolf Optimizer for hyperspectral band selection

Medjahed, Seyyid Ahmed; Saadi, Tamazouzt Ait; Benyettou, Abdelkader; Ouali, Mohammed

doi:10.1016/j.asoc.2015.09.045

Cited by 168 publications

(52 citation statements)

References 26 publications

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“…We used JM distance to measure the separability of each pair-wise crop because previous research proved that JM distance have high potential to measure crop separability (Medjahed et al, 2016; Murakami et al, 2001). The JM distance between a pair of crops could be calculated by Eq.…”

Section: Methodsmentioning

confidence: 99%

Estimation of different data compositions for early-season crop type classification

Hao

Niu

et al. 2018

PeerJ

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Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer’s accuracies (PAs) and user’s accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study.

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Section: Methodsmentioning

confidence: 99%

Estimation of different data compositions for early-season crop type classification

Hao

Niu

et al. 2018

PeerJ

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“…GWO is proven to be superior or competitive to other classical metaheuristics such as differential evolutionary, genetic algorithm and particle swarm optimization algorithm. GWO has been successfully applied to many engineering fields, such as parameter estimation in surface waves [24] , optimization of controller's gains [25] , the optimal power flow problem [26] , hyperspectral image classification [27] and designing photonic crystal waveguides [28] . Based on the effectiveness of GWO and the nature of the multiobjective (MOP), a new multi-objective discrete grey wolf optimizer (MODGWO) is proposed to solve this multi-objective WSP.…”

Section: Introductionmentioning

confidence: 99%

An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production

Xiao

et al. 2016

Advances in Engineering Software

111

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a b s t r a c tThis paper aims to provide a solution method for a real-world scheduling case from a welding process, which is one of the important processes in modern industry. The unique characteristic of the welding scheduling problem (WSP) is that multiple machines can process one operation at a time. Thus, WSP is a new scheduling problem. We first formulate a new multi-objective mixed integer programming model for this WSP based on a comprehensive investigation. This model involves some realistic constraints, controllable processing times (CPT), sequence dependent setup times (SDST) and job dependent transportation times (JDTT). Then we propose a multi-objective discrete grey wolf optimizer (MODGWO) considering not only production efficiency but also machine load on this real-world scheduling case. The solution is encoded as a two-part representation including a permutation vector and a machine assignment matrix. A reduction machine load strategy is used to adjust the number of machines aiming to minimize the machine load. To evaluate the effectiveness of the proposed MODGWO, we compare it with other well-known multi-objective evolutionary algorithms including NSGA-II and SPEA2 on a set of instances. Experimental results demonstrate that the proposed MODGWO is superior to the compared algorithms in terms of convergence, spread and coverage on most instances. Finally, MODGWO is successfully applied to this real-world WSP. This implies that the proposed model is feasible and the proposed algorithm can solve this real-world scheduling problem very well.

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“…This algorithm is inspired by the social hierarchy and hunting strategies of grey wolves in the wild. It can be regarded as a robust swarm-based optimizer [40][41][42][43][44][45]. The following discusses its working mechanism.…”

Section: Gwo Algorithmmentioning

confidence: 99%

SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

et al. 2018

Mathematical Problems in Engineering

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An improved Grey Wolf Optimization (GWO) algorithm with differential evolution (DEGWO) combined with fuzzy C-means for complex synthetic aperture radar (SAR) image segmentation was proposed for the disadvantages of traditional optimization and fuzzy C-means (FCM) in image segmentation precision. In the process of image segmentation based on FCM algorithm, the number of clusters and initial centers estimation is regarded as a search procedure that searches for an appropriate value in a greyscale interval. Hence, an improved differential evolution Grey Wolf Optimization (DE-GWO) algorithm is introduced to search for the optimal initial centers; then the image segmentation approach which bases its principle on FCM algorithm will get a better result. Experimental results in this work infers that both the precision and efficiency of the proposed method are superior to those of the state of the art.

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Gray Wolf Optimizer for hyperspectral band selection

Cited by 168 publications

References 26 publications

Estimation of different data compositions for early-season crop type classification

Estimation of different data compositions for early-season crop type classification

An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production

SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

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