Unsupervised multiple endmember spectral mixture analysis-based detection of opium poppy fields from an EO-1 Hyperion image in Helmand, Afghanistan

Wang, Jianjun; Zhang, Yun; Bussink, Coen

doi:10.1016/j.scitotenv.2014.01.006

Cited by 10 publications

(9 citation statements)

References 19 publications

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“…In general, the pixels with normalized difference vegetation index (NDVI) values of 0.2-0.3 were mixed pixels of non-vegetation and vegetation [55][56][57]. A careful visual check of the image revealed that the NDVI value of 0.25 could better discriminate wheat from soil, so the pixels with NDVI values lower than 0.25 were masked out in this study.…”

Section: Methodology 241 Uav Image Processing and Index Extractionmentioning

confidence: 86%

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

et al. 2021

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In recent years, the delay in sowing has become a major obstacle to high wheat yield in Jiangsu Province, one of the major wheat producing areas in China; hence, it is necessary to screen wheat varieties are resilient for late sowing. This study aimed to provide an effective, fast, and non-destructive monitoring method of soil plant analysis development (SPAD) values, which can represent leaf chlorophyll contents, for late-sown winter wheat variety screening. This study acquired multispectral images using an unmanned aerial vehicle (UAV) at the overwintering stage of winter wheat growth, and further processed these images to extract reflectance of five single spectral bands and calculated 26 spectral vegetation indices. Based on these 31 variables, this study combined three variable selection methods (i.e., recursive feature elimination (RFE), random forest (RF), and Pearson correlation coefficient (r)) with four machine learning algorithms (i.e., random forest regression (RFR), linear kernel-based support vector regression (SVR), radial basis function (RBF) kernel-based SVR, and sigmoid kernel-based SVR), resulted in seven SVR models (i.e., RFE-SVR_linear, RF-SVR_linear, RF-SVR_RBF, RF-SVR_sigmoid, r-SVR_linear, r-SVR_RBF, and r-SVR_sigmoid) and three RFR models (i.e., RFE-RFR, RF-RFR, and r-RFR). The performances of the 10 machine learning models were evaluated and compared with each other according to the achieved coefficient of determination (R2), residual prediction deviation (RPD), root mean square error (RMSE), and relative RMSE (RRMSE) in SPAD estimation. Of the 10 models, the best one was the RF-SVR_sigmoid model, which was the combination of the RF variable selection method and the sigmoid kernel-based SVR algorithm. It achieved high accuracy in estimating SPAD values of the wheat canopy (R2 = 0.754, RPD = 2.017, RMSE = 1.716 and RRMSE = 4.504%). The newly developed UAV- and machine learning-based model provided a promising and real time method to monitor chlorophyll contents at the overwintering stage, which can benefit late-sown winter wheat variety screening.

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Section: Methodology 241 Uav Image Processing and Index Extractionmentioning

confidence: 86%

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

et al. 2021

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“…It has been suggested that natural and disturbed systems are best modelled by two-or three-endmember models (Powell and Roberts 2008;Wang et al 2012); thus, we used VIPER Tools to investigate two-, three-and four-endmember models to estimate char, soil, GV and NPV cover and photometric shade after wildfire. A large set of potential endmembers (59 unique endmembers, with 5-30 replicates of each) was iteratively narrowed down.…”

Section: Multiple Endmember Spectral Mixture Analysismentioning

confidence: 99%

Indicators of burn severity at extended temporal scales: a decade of ecosystem response in mixed-conifer forests of western Montana

Lewis

Hudak

Robichaud

et al. 2017

Int. J. Wildland Fire

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Abstract. We collected field and remotely sensed data spanning 10 years after three 2003 Montana wildfires to monitor ecological change across multiple temporal and spatial scales. Multiple endmember spectral mixture analysis was used to create post-fire maps of: char, soil, green (GV) and non-photosynthetic ( Initial estimates of char and NPV from the HS images were significantly correlated with their ground-measured counterparts (r ¼ 0.60 (P ¼ 0.03) and 0.68 (P ¼ 0.01) respectively), whereas HS GV and Landsat GV were correlated with canopy GV (r ¼ 0.75 and 0.70 (P ¼ 0.003) respectively). HS imagery had stronger direct correlations with all classes of fine-scale ground data than Landsat and also had stronger predictive correlations with 10-year canopy data (r ¼ 0.65 (P ¼ 0.02) to 0.84 (P ¼ 0.0003)). There was less than 5% understorey GV cover on the sites initially, but by 2013, it had increased to nearly 60% regardless of initial condition. The data suggest it took twice as long for understorey GV and NPV to replace char and soil as primary ground cover components on the highburn-severity sites compared with other sites.

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“…These pixels are called bad pixels, and the anomalous columns arranged by bad pixels are called bad lines. The pixel gray-scale slope threshold method (PGST) was used to repair the bad lines, that is, the bad lines were corrected by replacing their DN values with the average DN values of their immediate left and right neighboring pixels [50]. In this experiment, Grayscale Slope Threshold (GST) was set to 2.1 and Badline Flag Number Percentage (BFNP) was set to 0.4.…”

Section: Pre-processing Of Gf-5 Ahsi Imagerymentioning

confidence: 99%

Application of Lithological Mapping Based on Advanced Hyperspectral Imager (AHSI) Imagery Onboard Gaofen-5 (GF-5) Satellite

Tian

Cheng

et al. 2020

Remote Sensing

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The Advanced Hyperspectral Imager (AHSI), carried by the Gaofen-5 (GF-5) satellite, is the first hyperspectral sensor that simultaneously offers broad coverage and a broad spectrum. Meanwhile, deep-learning-based approaches are emerging to manage the growing volume of data produced by satellites. However, the application potential of GF-5 AHSI imagery in lithological mapping using deep-learning-based methods is currently unknown. This paper assessed GF-5 AHSI imagery for lithological mapping in comparison with Shortwave Infrared Airborne Spectrographic Imager (SASI) data. A multi-scale 3D deep convolutional neural network (M3D-DCNN), a hybrid spectral CNN (HybridSN), and a spectral–spatial unified network (SSUN) were selected to verify the applicability and stability of deep-learning-based methods through comparison with support vector machine (SVM) based on six datasets constructed by GF-5 AHSI, Sentinel-2A, and SASI imagery. The results show that all methods produce classification results with accuracy greater than 90% on all datasets, and M3D-DCNN is both more accurate and more stable. It can produce especially encouraging results by just using the short-wave infrared wavelength subset (SWIR bands) of GF-5 AHSI data. Accordingly, GF-5 AHSI imagery could provide impressive results and its SWIR bands have a high signal-to-noise ratio (SNR), which meets the requirements of large-scale and large-area lithological mapping. And M3D-DCNN method is recommended for use in lithological mapping based on GF-5 AHSI hyperspectral data.

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Unsupervised multiple endmember spectral mixture analysis-based detection of opium poppy fields from an EO-1 Hyperion image in Helmand, Afghanistan

Cited by 10 publications

References 19 publications

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

Indicators of burn severity at extended temporal scales: a decade of ecosystem response in mixed-conifer forests of western Montana

Application of Lithological Mapping Based on Advanced Hyperspectral Imager (AHSI) Imagery Onboard Gaofen-5 (GF-5) Satellite

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