Research on Polarized Multi-Spectral System and Fusion Algorithm for Remote Sensing of Vegetation Status at Night

Pixelated Filter Array for On-Chip Polarized Spectral Detection

On-chip multi-dimensional detection systems integrating pixelated polarization and spectral filter arrays are the latest trend in optical detection instruments, showing broad application potential for diagnostic medical imaging and remote sensing. However, thin-film or microstructure-based filter arrays typically have a trade-off between the detection dimension, optical efficiency, and spectral resolution. Here, we demonstrate novel on-chip integrated polarization spectral detection filter arrays consisting of metasurfaces and multilayer films. The metasurfaces with two nanopillars in one supercell are designed to modulate the Jones matrix for polarization selection. The angle of diffraction of the metasurfaces and the optical Fabry-Perot (FP) cavities determine the spectrum's center wavelength. The polarization spectral filter arrays are placed on top of the CMOS sensor; each array corresponds to one pixel, resulting in high spectral resolution and optical efficiency in the selected polarization state. To verify the methodology, we designed nine-channel polarized spectral filter arrays in a wavelength range of 1350 nm to 1550 nm for transverse electric (TE) linear polarization. The array has a 10 nm balanced spectral resolution and average peak transmission efficiency of over 75%, which is maintained by utilizing lossless dielectric material. The proposed array can be fabricated using overlay e-beam lithography, and the process is CMOS-compatible. The proposed array enables broader applications of in situ on-chip polarization spectral detection with high efficiency and spectral resolution, as well as in vivo imaging systems.

Compressive space-dimensional dual-coded hyperspectral polarimeter (CSDHP) and interactive design method

A compressive space-dimensional dual-coded hyperspectral polarimeter (CSDHP) and interactive design method are introduced. A digital micromirror device (DMD), a micro polarizer array detector (MPA), and a prism grating prism (PGP) are combined to achieve single-shot hyperspectral polarization imaging. The longitudinal chromatic aberration (LCA) and spectral smile of the system are both eliminated to guarantee the matching accuracy of DMD and MPA pixels. A 4D data cube with 100 channels and 3 Stocks parameters is reconstructed in the experiment. The feasibility and fidelity are verified from the image and spectral reconstruction evaluations. It is demonstrated that the target material can be distinguished by CSDHP.

SCANet: Implementation of Selective Context Adaptation Network in Smart Farming Applications

In the last decade, deep learning has enjoyed its spotlight as the game-changing addition to smart farming and precision agriculture. Such development has been predominantly observed in developed countries, while on the other hand, in developing countries most farmers especially ones with smallholder farms have not enjoyed such wide and deep adoption of this new technologies. In this paper we attempt to improve the image classification part of smart farming and precision agriculture. Agricultural commodities tend to possess certain textural details on their surfaces which we attempt to exploit. In this work, we propose a deep learning based approach called Selective Context Adaptation Network (SCANet). SCANet performs feature enhancement strategy by leveraging level-wise information and employing context selection mechanism. In exploiting contextual correlation feature of the crop images our proposed approach demonstrates the effectiveness of the context selection mechanism. Our proposed scheme achieves 88.72% accuracy and outperforms the existing approaches. Our model is evaluated on the cocoa bean dataset constructed from the real cocoa bean industry scene in Indonesia.