MSFA-Net: A convolutional neural network based on multispectral filter arrays for texture feature extraction - Archive ouverte HAL Access content directly
Journal Articles Pattern Recognition Letters Year : 2023

MSFA-Net: A convolutional neural network based on multispectral filter arrays for texture feature extraction

Abstract

Multispectral snapshot cameras fitted with a multispectral filter array (MSFA) acquire several spectral bands in one shot and provide a raw mosaic image in which a single channel value is available at each pixel. Texture features are classically extracted from fully-defined images that are estimated by demosaicing. Such an estimation may however cause spatio-spectral artifacts. Moreover, texture feature extraction becomes computationally inefficient and yields to high-dimensional features as the number of bands increases. In this paper, we propose an original approach based on a convolutional neural network called MSFA-Net to capture spatio-spectral interactions in raw images at reduced computation costs. Experiments of multispectral image classification and outdoor image segmentation show that the proposed approach outperforms several hand-crafted and deep learning-based feature extractors.
No file

Dates and versions

hal-04018052 , version 1 (07-03-2023)

Identifiers

Cite

Anis Amziane, Olivier Losson, Benjamin Mathon, Ludovic Macaire. MSFA-Net: A convolutional neural network based on multispectral filter arrays for texture feature extraction. Pattern Recognition Letters, 2023, 168, pp.93-99. ⟨10.1016/j.patrec.2023.03.004⟩. ⟨hal-04018052⟩
24 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More