Abstract
Hyperspectral image data are widely used in real life because it contains rich spectral and spatial information. Hyperspectral image classification is to distinguish different functions based on different features. The computer performs quantitative analysis through the captured image and classifies each pixel in the image. However, the traditional deep learning-based hyperspectral image classification technology, due to insufficient spatial-spectral feature extraction, too many network layers, and complex calculations, leads to large parameters and optimizes hyperspectral images. For this reason, I proposed the I3D-CNN model. The number of classification parameters is large, and the network is complex. This method uses hyperspectral image cubes to directly extract spectral-spatial coupling features, adds depth separable convolution to 3D convolution to reextract spatial features, and extracts the parameter amount and calculation time at the same time. In addition, the model removes the pooling layer to achieve fewer parameters, smaller model scale, and easier training effects. The performance of the I3D-CNN model on the test datasets is better than other deep learning-based methods after comparison. The results show that the model still exhibits strong classification performance, reduces a large number of learning parameters, and reduces complexity. The accuracy rate, average classification accuracy rate, and kappa coefficient are all stable above 95%.
Highlights
Security and Communication Networks natural resource exploration [17], military defense security [18], and natural disaster assessment [19]
Indian Pines (IP) is an image of the Indiana agricultural and forestry hyperspectral test site in northwest Indiana collected by the AVIRIS sensor [39]. e image consists of 145 × 145 pixels, of which 220 spectral bands range from 0.2 to 0.4 m, with a spatial resolution well
Like the Indian Pines image, Salinas data is captured by AVIRIS imaging spectrometer, which is an image of Salinas Valley in California, USA
Summary
Security and Communication Networks natural resource exploration [17], military defense security [18], and natural disaster assessment [19]. The physical meaning of the feature selection method is very clear, it can retain the useful information of the hyperspectral image without spatial transformation, but these methods often need to be matched with the search algorithm to search for the most effective band or combination of bands. The network structure is highly unchanged for general geometric transformations (such as translation and scaling)
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