scholarly journals Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4328
Author(s):  
Yupeng Kang ◽  
Qingyan Meng ◽  
Miao Liu ◽  
Youfeng Zou ◽  
Xuemiao Wang
Keyword(s):  
Remote Sensing ◽  
Spectral Band ◽  
Stepwise Discriminant Analysis ◽  
Remote Sensing Classification ◽  
Red Edge ◽  
Feature Importance ◽  
Crop Classification ◽  
Edge Features ◽  
Edge Band ◽  
Edge Feature

A red edge band is a sensitive spectral band of crops, which helps to improve the accuracy of crop classification. In view of the characteristics of GF-6 WFV data with multiple red edge bands, this paper took Hengshui City, Hebei Province, China, as the study area to carry out red edge feature analysis and crop classification, and analyzed the influence of different red edge features on crop classification. On the basis of GF-6 WFV red edge band spectral analysis, different red edge feature extraction and red edge indices feature importance evaluation, 12 classification schemes were designed based on GF-6 WFV of four bands (only including red, green, blue and near-infrared bands), stepwise discriminant analysis (SDA) and random forest (RF) method were used for feature selection and importance evaluation, and RF classification algorithm was used for crop classification. The results show the following: (1) The red edge 750 band of GF-6 WFV data contains more information content than the red edge 710 band. Compared with the red edge 750 band, the red edge 710 band is more conducive to improving the separability between different crops, which can improve the classification accuracy; (2) According to the classification results of different red edge indices, compared with the SDA method, the RF method is more accurate in the feature importance evaluation; (3) Red edge spectral features, red edge texture features and red edge indices can improve the accuracy of crop classification in different degrees, and the red edge features based on red edge 710 band can improve the accuracy of crop classification more effectively. This study improves the accuracy of remote sensing classification of crops, and can provide reference for the application of GF-6 WFV data and its red edge bands in agricultural remote sensing.

scholarly journals Recognition of Banana Fusarium Wilt Based on UAV Remote Sensing

2020 ◽  
Vol 12 (6) ◽  
pp. 938 ◽  
Author(s):  
Huichun Ye ◽  
Wenjiang Huang ◽  
Shanyu Huang ◽  
Bei Cui ◽  
Yingying Dong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fusarium Wilt ◽  
Wilt Disease ◽  
Identification Accuracy ◽  
Validation Dataset ◽  
Multispectral Imagery ◽  
Red Edge ◽  
Fusarium Wilt Disease ◽  
Chlorophyll Index ◽  
Edge Band

Fusarium wilt (Panama disease) of banana currently threatens banana production areas worldwide. Timely monitoring of Fusarium wilt disease is important for the disease treatment and adjustment of banana planting methods. The objective of this study was to establish a method for identifying the banana regions infested or not infested with Fusarium wilt disease using unmanned aerial vehicle (UAV)-based multispectral imagery. Two experiments were conducted in this study. In experiment 1, 120 sample plots were surveyed, of which 75% were used as modeling dataset for model fitting and the remaining were used as validation dataset 1 (VD1) for validation. In experiment 2, 35 sample plots were surveyed, which were used as validation dataset 2 (VD2) for model validation. An UAV equipped with a five band multispectral camera was used to capture the multispectral imagery. Eight vegetation indices (VIs) related to pigment absorption and plant growth changes were chosen for determining the biophysical and biochemical characteristics of the plants. The binary logistic regression (BLR) method was used to assess the spatial relationships between the VIs and the plants infested or not infested with Fusarium wilt. The results showed that the banana Fusarium wilt disease can be easily identified using the VIs including the green chlorophyll index (CIgreen), red-edge chlorophyll index (CIRE), normalized difference vegetation index (NDVI), and normalized difference red-edge index (NDRE). The fitting overall accuracies of the models were greater than 80%. Among the investigated VIs, the CIRE exhibited the best performance both for the VD1 (OA = 91.7%, Kappa = 0.83) and VD2 (OA = 80.0%, Kappa = 0.59). For the same type of VI, the VIs including a red-edge band obtained a better performance than that excluding a red-edge band. A simulation of imagery with different spatial resolutions (i.e., 0.5-m, 1-m, 2-m, 5-m, and 10-m resolutions) showed that good identification accuracy of Fusarium wilt was obtained when the resolution was higher than 2 m. As the resolution decreased, the identification accuracy of Fusarium wilt showed a decreasing trend. The findings indicate that UAV-based remote sensing with a red-edge band is suitable for identifying banana Fusarium wilt disease. The results of this study provide guidance for detecting the disease and crop planting adjustment.

scholarly journals Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite

2021 ◽  
Vol 13 (1) ◽  
pp. 416-430
Author(s):  
Zhengdong Deng ◽  
Zhao Lu ◽  
Guangyuan Wang ◽  
Daqing Wang ◽  
Zhibin Ding ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Gansu Province ◽  
Vegetation Coverage ◽  
Wide Field ◽  
Red Edge ◽  
The Impact ◽  
Edge Band

Abstract The red edge band is considered as one of the diagnosable characteristics of green plants, but the large-scale remote sensing retrieval of fractional vegetation coverage (FVC) based on the red edge band is still rare. To explore the application of the red edge band in the remote sensing estimation of FVC, this study proposed a new vegetation index (normalized difference red edge index, RENDVI) based on the two red edge bands of Chinese GaoFen-6 satellite (GF-6). The FVC estimated by using three vegetation indices (NDVI, RENDVI1, and RENDVI2) were evaluated based on the field survey FVC obtained in Minqin Basin of Gansu Province. The results showed that there was a good linear correlation between the FVC estimated by GF-6 WFV data and the FVC investigated in the field, and the most reasonable estimation of FVC was obtained based on RENDVI2 model (R 2 = 0.97611 and RMSE = 0.07075). Meanwhile, the impact of three confidence levels (1, 2, and 5%) on FVC was also analyzed in this study. FVC obtained from NDVI and RENDVI2 has the highest accuracy at 2% confidence, while FVC based on RENDVI1 achieved the best accuracy at 5% confidence. It could be concluded that it is feasible and reliable to estimate FVC based on red edge bands, and the GF-6 Wide Field View (WFV) data with high temporal and spatial resolution provide a new data source for remote sensing estimation of FVC.

scholarly journals Application of red edge band in remote sensing extraction of surface water body: a case study based on GF-6 WFV data in arid area

2021 ◽  
Author(s):  
Zhao Lu ◽  
Daqing Wang ◽  
Zhengdong Deng ◽  
Yue Shi ◽  
Zhibin Ding ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Water Body ◽  
Arid Area ◽  
Single Band ◽  
Water Bodies ◽  
Urban Region ◽  
Threshold Method ◽  
Red Edge ◽  
Edge Band

Abstract This paper mainly researches the application method of red edge band in water body remote sensing extraction. Gaofen-6 (GF-6) WFV data were chosen for the high spatial resolution, more bands, and wide width. Two new methods were proposed: the single-band threshold method based on the red edge 2 band and the decision tree model method based on the combined operation of the green band, red band, near infrared band, red edge 1 band, and red edge 2 band. Four traditional methods were used for comparing the extraction accuracy. Two study areas with different characteristics were chosen to analyze the reliability of the proposed method, one mountainous and one urban region, both located in Minqin, Gansu, China, which is a typical arid area. The results showed that the two red edge bands of the GF-6 WFV data can be utilized to extract water body information. Kappa coefficients extracted from the single-band threshold method based on the red edge 2 band in water bodies in mountainous and urban areas reached 96.18% and 93.21%, respectively. The decision tree method has the best extraction effect. Kappa coefficients of this method in mountain and urban water bodies were 97.73% and 94.41%, respectively.

A spatial-channel progressive fusion ResNet for remote sensing classification

2020 ◽  
Author(s):  
Hao Zhu ◽  
Mengru Ma ◽  
Wenping Ma ◽  
Licheng Jiao ◽  
Shikuan Hong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Classification

scholarly journals An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images

2020 ◽  
Vol 13 (1) ◽  
pp. 65
Author(s):  
Jingtao Li ◽  
Yonglin Shen ◽  
Chao Yang
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Short Term Memory ◽  
Complete Classification ◽  
Support Vector ◽  
Classification Models ◽  
Training Samples ◽  
Agricultural Applications ◽  
Crop Classification ◽  
Increasing Demand

Due to the increasing demand for the monitoring of crop conditions and food production, it is a challenging and meaningful task to identify crops from remote sensing images. The state-of the-art crop classification models are mostly built on supervised classification models such as support vector machines (SVM), convolutional neural networks (CNN), and long- and short-term memory neural networks (LSTM). Meanwhile, as an unsupervised generative model, the adversarial generative network (GAN) is rarely used to complete classification tasks for agricultural applications. In this work, we propose a new method that combines GAN, CNN, and LSTM models to classify crops of corn and soybeans from remote sensing time-series images, in which GAN’s discriminator was used as the final classifier. The method is feasible on the condition that the training samples are small, and it fully takes advantage of spectral, spatial, and phenology features of crops from satellite data. The classification experiments were conducted on crops of corn, soybeans, and others. To verify the effectiveness of the proposed method, comparisons with models of SVM, SegNet, CNN, LSTM, and different combinations were also conducted. The results show that our method achieved the best classification results, with the Kappa coefficient of 0.7933 and overall accuracy of 0.86. Experiments in other study areas also demonstrate the extensibility of the proposed method.

Based on IDRISI Remote Sensing Images Land-Use Types of Supervised Classification Techniques

2013 ◽  
Vol 415 ◽  
pp. 305-308
Author(s):  
Kun Zhang ◽  
Hai Feng Wang ◽  
Zhuang Li
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Classification Techniques ◽  
Analysis And Evaluation ◽  
Remote Sensing Classification ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Remote Sensing Image Classification ◽  
Land Use Types ◽  
Area Distribution

With remote sensing technology and computer technology, remote sensing classification technology has been rapid progress. In the traditional classification of remote sensing technology, based on the combination of today's technology in the field of remote sensing image classification, some new developments and applications for land cover classification techniques to make more comprehensive elaboration. Using the minimum distance classifier extracts of the study area land use types. Ultimately extracted land use study area distribution image and make its analysis and evaluation.

scholarly journals Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1221
Author(s):  
Yuki Hamada ◽  
Colleen R. Zumpf ◽  
Jules F. Cacho ◽  
DoKyoung Lee ◽  
Cheng-Hsien Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Environmental Sustainability ◽  
Vegetation Index ◽  
Mean Squared Error ◽  
Spectral Band ◽  
Great Promise ◽  
Optical Remote Sensing

A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.

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