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The classification and identification of farmland vegetation includes classification based on vegetation index, spectral bands, multi-source data fusion, artificial intelligence learning, and drone remote sensing.
Remote Sensing Classification of Farmland Vegetation | Classification | |
---|---|---|
Farmland vegetation classification based on vegetation index | Normalized difference vegetation index, enhanced vegetation index, surface temperature, etc. | |
Farmland vegetation classification based on spectral band | Remote sensing recognition of crops based on single image | |
Remote sensing recognition of crops based on multi-temporal remote sensing images | Single feature parameter recognition | |
Multiple feature parameter recognition | ||
Multi-feature parameter statistical model | ||
Farmland vegetation classification based on multi-source data fusion | Data consistency scoring | |
Regression analysis | ||
Farmland vegetation classification based on machine learning | Support vector machine algorithm | |
Neural network algorithm | ||
Decision tree algorithm | ||
Object-oriented machine learning algorithms | ||
Deep learning algorithm | ||
Crop classification based on drone remote sensing |
Method | Applicability | Data Source | Classification | Advantages | Disadvantage | |
---|---|---|---|---|---|---|
Remote sensing recognition of crops based on single image | Suitable for areas with relatively simple crop planting structure | SPOT-5 | Decision tree | High efficiency and strong operability | Long revisit period and poor accuracy when the “critical phenological period” is not obvious | |
IRS-1D | Support vector machines | |||||
CBERS-02B | Neural networks | |||||
Maximum likelihood | ||||||
LANDSAT-TM | Spectral angle mapping | |||||
HJ-1B | ||||||
HJ-1A | ||||||
MODIS | ||||||
Remote sensing recognition of crops based on multi-temporal remote sensing images | Single feature parameter recognition | Suitable for areas with relatively simple crop planting structure | MODIS | Fast Fourier transform | Simple operation and high efficiency | Feature selection is subjective and has limitations in areas with complex and diverse crop types |
TM/ETM+ | Unsupervised classification and spectral coupling technology | |||||
BP neural network | ||||||
Threshold method | ||||||
Wavelet transform | ||||||
Shortest distance | ||||||
Multiple feature parameter recognition | Suitable for areas with complex crop planting structures | MODIS | Threshold method | Use multiple spectral time series feature quantities to better capture the characteristics of each type of crop that is different from other crops | Reduce the efficiency of data processing and calculation and increase the accumulation of errors | |
AVHRR | Classification regression tree | |||||
SPOT VGT | See5.0 | |||||
ASTER | Unsupervised classification | |||||
AWIFS | Spectral matching technology | |||||
Landsat | Image segmentation | |||||
TM/ETM+ | Random forest | |||||
HJ-1A/B | ||||||
Multi-feature parameter statistical model | Suitable for areas with land consolidation, diverse terrain, and complex planting structure | MODIS | Temporal decomposition model | Higher extraction accuracy of crop planting area | Stability and universality need to be further strengthened and improved | |
VHRR | Neural network model | |||||
SPOT-VEG | Independent component analysis model | |||||
CPPI index model | ||||||
ETATION |
Fusion Method | Data Source | Research Area | Spatial Resolution | Fusion Process | Literature Source |
---|---|---|---|---|---|
Data consistency scoring | GLC2000, MODIS, IGBP DISCover | Global | 1 km | Calculate affinity index for multi-source data set fusion mapping | [36] |
GLC-2000, MODIS VCF, GIS data, statistical data | Russia | 1 km | Establish a fusion information system for multi-source data set fusion mapping | [40] | |
GLC-2000, MODIS, GlobCover2005, GEOCOVER, cropland probability layer |
Global | 1 km | Analyze the consistency of remote sensing data products, set weights, and establish fusion rules | [37][41] | |
FROM-GLC, GlobCover2009 et al. regional data set (Corine Land Cover et al.), national data set | Global | 250 m | Multi-index analysis, scoring different data sets, setting weights, and fusion | [42] | |
Regression analysis | USGS-Hydro1k DEM, PELCOM, slope, soil data, meteorological data, land use ratio data | Belgium | 1.1 km | Construct a logistic regression model of spatial autocorrelation to predict the spatial distribution of different land cover types | [43] |
GLC2000, MOD12C5, MOD12C4, GLCNMO, UMD, GlobCover | Global | 5′ | Using logistic regression model to predict types of land cover | [40] | |
GLCC, GlobCover GLC2000, UMD LC, MODIS LC, MODIS VCF, | North America | 5 km | Use regression tree model to integrate global and regional land cover products | [44] | |
GlobCover, GLC2000, MODIS | Global | 1 km | Using GWR logistic regression model to predict the type of land cover in the sample-free area | [35] | |
Land cover (MODIS LC, regional mosaics GLC2000, GlobeCover, GLCNMO), tree cover (Hansen’s TC, Landsat VCF, MODIS VCF) | Global | 1 km | Using GWR logistic regression model to predict the proportion of forest coverage in the sample-free area | [40] |