Multi-spectral Satellite Cloud And Snow Image Recognition Based On Deep Learning

China's satellite image recognition technology is increasingly used in fields such as naturaldisasters,surface coverage area monitoring,and environmental resource distribution research.The cloud,snow and other regions in the satellite cloud and snow images have similar hyperspectral features and complex and variable spatial distribution features.Traditional research methods have low utilization rate of multi-spectral features,and it is difficult to effectively obtain high-order semantic information in images.In response to these research problems,this paper proposes a deep learning algorithm to automatically extract the spectral feature information and local texture feature information of multi-spectral satellite cloud and snow images to obtain low-order to high-order semantic features.Convolutional neural networks can effectively extract various feature information in satellite images and have good feature extraction capabilities,but if only deepening the depth of its own structure will cause the gradient signal to disappear and other problems.In order to improve the spectral feature extraction ability,this paper constructs a multi-dimensional dual-granularity deep forest lightweight model and a deep residual aggregation convolution network model to optimize the spectral feature information and improve the recognition ability of multi-spectral satellite cloud and snow images.Through simulation experiments and results analysis,the deep residual aggregation convolutional network and the multi-dimensional dual-granularity deep forest model in this paper can effectively extract various feature information of multi-spectral satellite cloud and snow images,improve the feature utilization rate,and have a good generalization ability.Compared with machine learning,integrated algorithms,and neural networks,deep residual aggregation convolutional networks have better generalization capabilities and can effectively identify cloud regions,snow regions,cloudless and snowless regions in multi-spectral satellite cloud and snow images.Cloud and snow mixed area.In addition,the simulation results in this paper also show that compared with single-spectrum satellite cloud and snow images,multi-spectral satellite cloud and snow images contain more feature information,which is more suitable for cloud and snow image recognition research.In summary,this paper constructs a deep residual aggregation convolutional network model and a multi-dimensional dual-granularity deep forest model to carry out multi-spectral satellite cloud and snow image recognition research is of great reference significance to the relevant research carried out by China's meteorological department.