Efficient Semantic Segmentation of Hyperspectral Images Using Adaptable Rectangular Convolution

José Luis Rodríguez García

doi:10.1109/LGRS.2022.3140950

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Efficient Semantic Segmentation of Hyperspectral Images Using Adaptable Rectangular Convolution

2022-01-01

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José Luis Rodríguez García

DOI：10.1109/LGRS.2022.3140950

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Abstract

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Convolutional neural networks (CNNs) are relevant tools for remote sensing data processing in the last few years. Kernels process and integrate the spatial information of remotely sensed hyperspectral images (HSIs) accurately enough, so as to reduce the noise and spectral variations present in the data. Despite the great efficiency of the CNNs with HSI data, kernels need to be composed of a large number of layers and parameters to be able to obtain good performance, forcing the model to process a large amount of information from the datasets and making the model prone to overfitting, due to the limited number of labels in some cases. On the other hand, with that huge amount of data to consume, a large number of kernels are needed, making the model less efficient due to computational complexity. To overcome these challenges, this letter presents a new technique to reduce the computational cost and increase the model accuracy [based on adaptable rectangular convolutions (ARCs)], leading to a substantial reduction of the number of parameters and improving the model, in order to achieve better results in the context of HSI semantic segmentation. Thus, the model learns through the convolution the dimensions and offsets of the kernel of this Adaptable layer, performing the average operation based on integral image works to achieve better results with fewer parameters, reducing the risk of overfitting and computational cost. The source code can be found in the repository available at the link. [https://github.com/jlgs96/segHSI]

Keywords:

Convolution

Computational modeling

Kernel

Adaptation models

Data models

Feature extraction

Semantics

Adaptable rectangular convolution (ARC)

convolutional neural networks (CNNs)

hyperspectral images (HSIs)