Mid-wave infrared (MWIR) images have been widely used to embody abundant thermal radiation geographic information. However, due to the small field of view (FOV) of MWIR imaging detectors, image mosaicking is necessary to combine multiple images with overlapping regions into a larger FOV image. Feature detection and mosaicking of MWIR images remain challenging due to their low contrast and low signal-to-noise ratio (SNR). To address this problem, we propose an effective image mosaicking method for MWIR images based on 2D Log-Gabor feature transform (LGFT) and priori coordinates. The 2D LGFT based on phase coherence used in the proposed method enables feature point detection independent of image intensity and gradient, from which more distinctive features can be extracted. Priori coordinates are used for feature point matching constraints, and topological analysis is applied for globally consistent alignment to obtain the final mosaic of MWIR images. We evaluate the proposed method on two captured MWIR image datasets under different scenarios, and the results show that our method outperforms other state-of-the-art feature extraction and matching methods, while preserving details and reducing distortion compared to commercial tools.

Push-broom hyperspectral imaging systems often suffer from stripe artifacts. The conventional methods treat the artifacts as noise and suppress narrow-stripe ones well but show limitations to wide and full-band stripe artifacts. To address the problem, this article proposes a spatial–spectral attention pyramid network (SAPN) for hyperspectral stripe restoration. First, the spatial–spectral mixed attention (SMA) module is developed to tackle the inefficiency of neighborhood representation in wide stripes, and it is mainly composed of three spatial and spectral attention (SSA) operations. Each SSA specifically combines channel and nonlocal attention (NLA) to compute spatial–spectral attention features. SMA utilizes these SSA operations to achieve different spatial–spectral attention features for multidirectional slices of hyperspectral cubes and then fuses them to establish the contextual connection between the single pixel and the global information. Furthermore, we build an efficient pyramid backbone (EPB) for stripe restoration. In EPB, multiresolution shallow pyramid features are extracted by the lightweight head module and then inferred and reconstructed by SMA and other layers from coarse to fine, and the shareable SSA layer also greatly decreases parameters. Besides, we develop an unsupervised learning strategy where SAPN generates pseudo-reference images with the aid of deep image prior (DIP) and achieve the convergent model for batch images. Experiments are carried out on the private and public hyperspectral datasets where wide stripes exist at the same and different spatial locations in all bands. Experimental results demonstrate that SAPN can obtain competitive objective metrics, and it can restore images with more realistic texture and fidelity spectra.

Automatic image stitching and high-precision photogrammetry have been active research fields. This article proposes a novel stitching method for high-precision, low-overlap thermal infrared array scanning (TIRAS) images. The approach corrects the TIRAS camera’s internal parameters, external orientation (EO) elements, and planar projection errors by calculating three homography matrices. First, we establish collinearity equations using the calibrated internal parameters and EO elements with errors and further compute the direct projection transformation model of the original image. Then, we propose a global error optimization model based on a fixed framework according to the midpoints of the alignment point pairs of the correcting image and neighboring images. The homography matrix calculated by this model can relatively correct the EO element errors in the directly projected image. Finally, we calculate the homography matrix for the planar projection error correction using the universal transverse mercartor (UTM) coordinates of geometric targets and their corresponding image coordinates and use the matrix to perform absolute error correction on the stitched wide-field image of the reprojected image. Comparing the proposed method with AutoStitch, Liu’s, and PTGui for mid-wave infrared (MWIR) image stitching results, it exhibits superior image stitching quality, without noticeable distortion or stretching, and with intact object structures. Besides, our method demonstrates outstanding positioning accuracy. Without ground control points, the optimal positioning accuracy is 0.7538 m (RMSE), less than 1 pixel. In conclusion, the proposed stitching method showcases stable, automated, and highly accurate global error optimization capabilities, and it is also suitable to splice array remote sensing images equipped with a high-accuracy POS system.

Stripe noise still affects full-spectrum airborne hyperspectral imager (FAHI) images after laboratory radiometric calibration, which seriously affects the subsequent applications of the imager. Therefore, two state-of-the-art methods, median linear correction (MLC) and Fourier transform filtering (FTF), were proposed to restore FAHI images, and the residual stripes were removed in most cases. However, these methods have their own limitations. For instance, the restored image has a slight “shadow” in cases where the high-response digital numbers (DNs) of the detector are aligned with the flight direction. This letter proposes a new method based on improved statistics to restore FAHI images. In this method, the hyperspectral image data from the adjacent flight paths is used to obtain the uniform response DNs for nearly identical low and high irradiances. Subsequently, a statistics-based MLC method is used to eliminate the stripe noise. To quantitatively evaluate the restoration results, we compared results with MLC and FTF methods. The change in mean value and mean relative deviation of the proposed method for the high-response DNs area of the image is 0.25% and 0.97%, respectively, better than that of the other two methods. The experimental results demonstrate that the proposed method is effective for removing stripe noise and preserving accurate image information of push-broom hyperspectral imagery.

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.

Following calibration, hyperspectral images remain affected by spatial dimension nonuniformity, i.e., stripe noise, due to stray light interference, slit contamination, and instrument instability. The full spectrum airborne hyperspectral imager (FAHI) is a Chinese next-generation pushbroom sensor with a spectral range covering the visible near-infrared, shortwave-infrared (SWIR), and thermal infrared regions with spectral sampling intervals of 2.34, 3, and 32 nm, respectively. However, the residual stripe noise remains in FAHI images after relative radiometric correction based on the laboratory calibration, especially in low signal-to-noise ratio bands. To solve this problem, a new technique combining image statistics and spatial filtering algorithms has been developed for FAHI image correction. In this method, image statistics are obtained to calculate the gain and offset of each pixel for image nonuniformity correction. Then, a spatial filter removes the residual stripes. This paper presents the principles of this method along with details of validation experiments and results. To validate the effectiveness of the proposed method, comparison with two destriping methods and quantitative analyses are carried out. Moreover, the method is applied to an SWIR hyperspectral image from the TianGong-1 spacecraft, yielding good results. The experimental results suggest that the proposed method is convenient and practical for improving the relative radiometric accuracy of airborne/spaceborne hyperspectral images.