In remote sensing and environmental monitoring, the authenticity of the collected hyperspectral image (HSI) is critical since unauthorized changes could cause inaccurate evaluation. Traditional approaches ensure HSI integrity after the acquisition, leaving the data vulnerable to unauthorized access or modification before applying a protective strategy. In response, this work presents a pushbroom HSI system that integrates an authentication key on the acquisition process, ensuring the key imperceptibility without affecting performance in spectral classification tasks. In addition, this work develops an algorithm designed to simulate attacks through class modifications in the HSI. Additionally, a detection algorithm is proposed to identify modifications by comparing the signed HSI with the altered version, effectively recognizing the embedded key. The proposed method was tested using simulated data and acquired data in the optical laboratory. Results demonstrate that the system maintains classification accuracy, further enhancing imperceptibility. Additionally, modifications are detectable by the key with 99% accuracy.

In most imaging applications the spatial resolution is a concern of the systems, but increasing the resolution of the sensor increases substantially the implementation cost. One option with lower cost is the use of spatial light modulators, which allows improving the reconstructed image resolution by including a high-resolution codification. In this paper, we propose a reconstruction methodology that exploits the intrinsic information contained in low-resolution measurements generated by the use of high-resolution spatial light modulators and high-resolution approximations obtained via a CNN. Specifically, based on a high-resolution CNN approximation, an l2fidelity regularization term is introduced into a traditional l2-l1optimization problem. Finally, the simulations of the proposed l2-l1-l2reconstruction approach show a quality improvement in up to 3.7dB in averaged PSNR against the use of the traditional l2-l1approach.

Compressive spectral imaging (CSI) sensors allow the acquisition of spatial and spectral data using a set of coded projections. The single pixel camera (SPC) is a low-cost CSI architecture capable of sensing high-resolution spectral images, whose potential is diminished by its slow acquisition time due to the large number of required projections. To partially alleviate this issue, dual arm optical systems have been designed such that side information of the scene is captured to guide the reconstruction process. To fully exploit the capabilities of the dual system, the SPC sensing matrix, or equivalently the coding patterns; should be properly designed. Therefore, this work proposes an optimized sensing matrix design for the SPC based on a super-pixel map of the scene, obtained from the side information, such that the number of projections is drastically reduced while the reconstruction quality is improved. Indeed, theoretical analysis based on the restricted isometry property indicates that the error of the reconstruction vanishes when the SPC uses the designed sensing matrix. Simulation and experimental results show that the proposed sensing matrix design improves the reconstruction quality. Specifically, the proposed approach improves image quality in up to 15dB compared with the state of the art sensing matrix designs. Moreover, a fast multi-resolution reconstruction approach is proposed based on the designed matrix, which reduces computation time by two orders of magnitude and does not require an iterative process.

Massive amounts of data in spectral imagery increase acquisition, storing and processing costs. Compressive spectral imaging (CSI) methods allow the reconstruction of spatial and spectral information from a small set of random projections. The single pixel camera is a low cost optical architecture which enables the compressive acquisition of spectral images. Traditional CSI reconstruction methods obtain a sparse approximation of the underlying spatial and spectral information, however the complexity of these algorithms increases in proportion to the dimensionality of the data. This work proposes a multiresolution (MR) CSI reconstruction approach from single pixel camera measurements that exploits spectral similarities between pixels to group them in super-pixels such that the total number of unknowns in the inverse problem is reduced. Specifically, two different types of super-pixels are considered: rectangular and irregular structures. Simulation and experimental results show that the proposed MR scheme improves reconstruction quality in up to 6dB of PSNR and reconstruction time in up to 90% with respect to the traditional full resolution reconstructions.