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[ Hot Focus on Chemical Machinery and Equipment Network ] People take food as their heaven
.
The importance of food in human daily life is irreplaceable
.
And the production of food, whether it is a processed product or a primary product, is inseparable from crops
.
Crops include food crops, cash crops (such as oil crops, vegetable crops, etc.
) two categories
.
Chemical machinery and equipment network hotspots focus on chemical machinery and equipment.
The importance of food in human daily life is irreplaceable
.
And the production of food, whether it is a processed product or a primary product, is inseparable from crops
.
Crops include food crops, cash crops (such as oil crops, vegetable crops, etc.
) two categories
.
Identifying and classifying crop varieties is conducive to strengthening the management and rational promotion of new crop varieties
.
Hyperspectral remote sensing data contains rich spectral information, which is widely used in the monitoring of crop distribution and dynamic changes, and plays an irreplaceable role in the classification of crop types in precision agriculture
.
At present, the utilization of features mainly includes traditional feature selection involving expert knowledge and automatic feature selection closely combined with Convolutional Neural Networks (CNN)
.
CNN automatic feature selection can automatically extract high-level domain-oriented features from the input data to achieve higher classification accuracy
.
But compared with mining spatial features, CNN is still insufficient in mining spectral features
.
.
Hyperspectral remote sensing data contains rich spectral information, which is widely used in the monitoring of crop distribution and dynamic changes, and plays an irreplaceable role in the classification of crop types in precision agriculture
.
At present, the utilization of features mainly includes traditional feature selection involving expert knowledge and automatic feature selection closely combined with Convolutional Neural Networks (CNN)
.
CNN automatic feature selection can automatically extract high-level domain-oriented features from the input data to achieve higher classification accuracy
.
But compared with mining spatial features, CNN is still insufficient in mining spectral features
.
At present, traditional feature selection has been proved by related research to improve the accuracy of multi-class classifiers including CNN.
Combining traditional feature selection methods with CNN advanced spatial feature automatic extraction is a gradually popular classification strategy
.
However, the current combined methods do not comprehensively utilize spatial features and spectral information, and do not reflect the rich spectral information of hyperspectral images in crop classification
.
At the same time, using traditional feature selection to mine spectral features and combining with CNN to automatically extract domain-oriented high-level features remains to be further studied
.
Combining traditional feature selection methods with CNN advanced spatial feature automatic extraction is a gradually popular classification strategy
.
However, the current combined methods do not comprehensively utilize spatial features and spectral information, and do not reflect the rich spectral information of hyperspectral images in crop classification
.
At the same time, using traditional feature selection to mine spectral features and combining with CNN to automatically extract domain-oriented high-level features remains to be further studied
.
Recently, Beijing Normal University and the Institute of Crop Science of the Chinese Academy of Agricultural Sciences published a research paper online in The Crop Journal, proposing a novel spectral feature—stacked spectral feature space patch.
SSFSP) for CNN-based crop classification of hyperspectral remote sensing images
.
This feature converts original recessive spectral features into dominant spatial features, which can be combined with 2D CNN to mine spectral and spatial features simultaneously
.
A comparative study of multiple high-spatial-resolution hyperspectral datasets shows that SSFSP can achieve higher classification accuracy than raw spectral inputs
.
SSFSP) for CNN-based crop classification of hyperspectral remote sensing images
.
This feature converts original recessive spectral features into dominant spatial features, which can be combined with 2D CNN to mine spectral and spatial features simultaneously
.
A comparative study of multiple high-spatial-resolution hyperspectral datasets shows that SSFSP can achieve higher classification accuracy than raw spectral inputs
.
Original title: Stacked spectral feature space maps: CNN-based crop classification of hyperspectral remote sensing images
