Ning Zhang
ABSTRACT
The traditional pests and diseases identification methods do not work well for massive high-resolution remote sensing image data. Thus, we are expected to find an efficient way to automatically learn the presentations from the massive image data, and find the relationships among the data. This paper proposes an end-to-end system for pests and diseases identification in massive high-resolution remote sensing data based on deep learning. To achieve good performance on pests and diseases identification, this hierarchical model jointly learns the parameters of a neural network and the cluster assignments of the features. Our network named ClusterNet iteratively groups the features with a standard clustering algorithm k-means, and uses the subsequent assignments as supervision to update the weights of the network. Qualitatively, we only need to provide the remote sensing image of target area, and the system will automatically identify pests and diseases. This is more accurate and convenient compared to the traditional method of manual detection. Quantitatively the resulting model outperforms the traditional convolutional neutral networks on our pests and diseases remote sensing dataset.
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Index Terms
- An End-to-end System for Pests and Diseases Identification
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