We have focused on the optical form that is low cost while maintaining high performance for airborne application. We report the optical design as well as the alignment and test results for a push-broom imaging spectrometer. The smart architecture of the prism-grating based spectrometer ensures high uniformity and image quality. Moreover, an effective method for aligning the spectrometer is also proposed. The results of laboratory-based optical tests and a flight test confirm the easy manufacture and excellent performance. Thus, the proposed system should be suitable for use as a hyperspectral instrument that can be loaded onto airborne and unmanned aerial vehicles.

Airborne hyperspectral images are used for crop identification with a high classification accuracy because of their high spectral resolution, spatial resolution, and signal-to-noise ratio (SNR). However, the tradeoffs between the three core parameters of a hyperspectral imager (SNR, spatial resolution, and spectral resolution) should be considered for designing an efficient imaging system. Only a few reported studies on the analysis of the impact of SNR on identification accuracy are available. Further, the tradeoffs and mutual interactions among these parameters are rarely considered. In this empirical study, our aim was to understand the relationship among the core parameters and their effects on crop identification accuracy by analyzing the tradeoffs and mutual interactions among these parameters. We analyzed the hyperspectral images of a typical plain agricultural area in Xiongan, China, acquired by the newly developed sensor airborne multimodular imaging spectrometer (AMMIS). The fundamental images were transformed to form datasets with different ranges of spectral resolution, spatial resolution, and SNR using data reconstruction methods. We adopted the classification and regression tree (CART), random forest (RF), and k-nearest neighbor (kNN) classifiers, and observed the overall accuracy (OA) across the degraded hyperspectral datasets. The experimental results indicated that the OA decreased with a decreasing SNR. As the spectral resolution became coarser, the OA first increased, plateaued, and then decreased. However, the OA increased with decreasing spatial resolution. This study was performed with the goal of bridging the knowledge gap between the back-end hyperspectral sensor designing and its front-end applications.

We discuss and evaluate a long wave infrared imaging spectrometer in terms of its opto-mechanical design and analysis, alignment, testing, and calibration. The instrument is a practical airborne sensor achieving high spectral resolution and sensitive noise equivalent delta temperature. The instrument operates in the 8 to 12.5 μm spectral region with 28.85 nm spectral sampling, 1 mrad instantaneous field of view, and >40° cross track field. The instrument comprises three uniform sub-modules with identical design parameters and performances. The sub-module design is based on a refractive foreoptics feeding an all-reflective spectrometer. The optical form of the spectrometer is a double-pass reflective triplet with a flat grating, which has a fast f/2 and high optical throughput. Cryogenic optics of 100 K is implemented only for the spectrometer. Assembly and thermal deformation and focusing adjustment design are particularly considered for this low temperature. All the mirrors of the spectrometer are opto-mechanical-integrated designed and manufactured by single-point diamond turning technology. We consider the center sub-module as an example, and we present its laboratory testing results and calibration; the results indicate the instrument’s potential value in airborne sensing.

Feature representation is the key to the hyperspectral images (HSI) inpainting. Existing works mainly focus on using spectral and temporal auxiliary images to inpainting the corrupted region, which were proved to be low robustness for all bands missing and high requirements for image acquisition. In this work, we propose an end-to-end inpainting framework for hyperspectral images based on convolutional neural networks, which does not require auxiliary images and makes full use of both spectral characteristics and spatial information. For spectral characteristics, a channel attention mechanism is proposed to reduce the redundancy of hyperspectral channels and model the correlation between channels. For spatial information, a local discriminative network is able to cope with the structural continuity of the corrupted regions, and a gradient consistency loss function is proposed to maintain the texture consistency of HSIs. Experimental results in the AVIRIS Indians Pines public dataset and FCH datasets show that our proposed method can provide competitive results compared with state-of-the-art methods.