Unmanned aerial vehicle (UAV) hyperspectral remote sensing technologies have unique advantages in high-precision quantitative analysis of non-contact water surface source concentration. Improving the accuracy of non-point source detection is a difficult engineering problem. To facilitate water surface remote sensing, imaging, and spectral analysis activities, a UAV-based hyperspectral imaging remote sensing system was designed. Its prototype was built, and laboratory calibration and a joint air–ground water quality monitoring activity were performed. The hyperspectral imaging remote sensing system of UAV comprised a light and small UAV platform, spectral scanning hyperspectral imager, and data acquisition and control unit. The spectral principle of the hyperspectral imager is based on the new high-performance acousto-optic tunable (AOTF) technology. During laboratory calibration, the spectral calibration of the imaging spectrometer and image preprocessing in data acquisition were completed. In the UAV air–ground joint experiment, combined with the typical water bodies of the Yangtze River mainstream, the Three Gorges demonstration area, and the Poyang Lake demonstration area, the hyperspectral data cubes of the corresponding water areas were obtained, and geometric registration was completed. Thus, a large field-of-view mosaic and water radiation calibration were realized. A chlorophyl-a (Chl-a) sensor was used to test the actual water control points, and 11 traditional Chl-a sensitive spectrum selection algorithms were analyzed and compared. A random forest algorithm was used to establish a prediction model of water surface spectral reflectance and water quality parameter concentration. Compared with the back propagation neural network, partial least squares, and PSO-LSSVM algorithms, the accuracy of the RF algorithm in predicting Chl-a was significantly improved. The determination coefficient of the training samples was 0.84; root mean square error, 3.19 μg/L; and mean absolute percentage error, 5.46%. The established Chl-a inversion model was applied to UAV hyperspectral remote sensing images. The predicted Chl-a distribution agreed with the field observation results, indicating that the UAV-borne hyperspectral remote sensing water quality monitoring system based on AOTF is a promising remote sensing imaging spectral analysis tool for water.

In the design and research of an unmanned aerial vehicle-borne hyperspectral imager with an acousto-optic tunable filter (AOTF), the depth of the field range of the imaging objective lens is fixed, the spectral range of 400–1000nm is wide, and the AOTF crystal has different deflection angles for diffracted light of different wavelengths. These features all cause problems such as image blurring and spectral drifts. Therefore, we propose a solution using a tunable zoom lens and multi-modal spectrum registration to solve these problems. We also propose an improved image definition evaluation function using the zoom lens to obtain clear horizontal lines and blurred vertical lines and, thereby, hope to overcome image blurring in an AOTF spectrometer. The zoom parameters corresponding to the clear image in each spectral segment of the AOTF spectrometer can be determined rapidly and accurately. However, it is difficult to establish a mathematical model of the drift, using wavelength as a variable, to compensate the drift due to the clear image of each spectrum obtained using the zoom lens. The calibration method completes the registration of the spectral data cube composed of clear images, solving the problem of spectral band drift. Moreover, it has been verified through UAV-borne hyperspectral imaging that image blurring and spectral drift of the AOTF imaging spectrometer were significantly reduced, providing opportunities for the application and research of AOTF-based hyperspectral imagers in more fields, such as remote sensing.

Differences in field of view may occur during unmanned aerial remote sensing imaging applications with acousto-optic tunable filter (AOTF) spectral imagers using zoom lenses. These differences may stem from image size deformation caused by the zoom lens, image drift caused by AOTF wavelength switching, and drone platform jitter. However, they can be addressed using hyperspectral image registration. This article proposes a new coarse-to-fine remote sensing image registration framework based on feature and optical flow theory, comparing its performance with that of existing registration algorithms using the same dataset. The proposed method increases the structure similarity index by 5.2 times, reduces the root mean square error by 3.1 times, and increases the mutual information by 1.9 times. To meet the real-time processing requirements of the AOTF spectrometer in remote sensing, a development environment using VS2023+CUDA+OPENCV was established to improve the demons registration algorithm. The registration algorithm for the central processing unit+graphics processing unit (CPU+GPU) achieved an acceleration ratio of ~30 times compared to that of a CPU alone. Finally, the real-time registration effect of spectral data during flight was verified. The proposed method demonstrates that AOTF hyperspectral imagers can be used in real-time remote sensing applications on unmanned aerial vehicles.

To achieve large-scale stitching of the hyperspectral remote sensing images obtained by unmanned aerial vehicles (UAVs) equipped with an acousto-optic tunable filter spectrometer, this study proposes a method based on a feature fusion strategy and a seam-finding strategy using hyperspectral image classification. In the feature extraction stage, SuperPoint deep features from images in different spectral segments of the data cube were extracted and fused. The feature depth matcher, LightGlue, was employed for feature matching. During the data cube fusion stage, unsupervised K-means spectral classification was performed separately on the two hyperspectral data cubes. Subsequently, grayscale transformations were applied to the classified images. A dynamic programming method, based on a grayscale loss function, was then used to identify seams in the transformed images. Finally, the identified splicing seam was applied across all bands to produce a unified hyperspectral data cube. The proposed method was applied to hyperspectral data cubes acquired at specific waypoints by UAVs using an acousto-optic tunable filter spectral imager. Experimental results demonstrated that the proposed method outperformed both single-spectral-segment feature extraction methods and stitching methods that rely on seam identification from a single spectral segment. The improvement was evident in both the spatial and spectral dimensions.

Unmanned aerial vehicle (UAV) hyperspectral remote sensing imaging systems have demonstrated significant potential for water quality monitoring. However, accurately obtaining water-leaving reflectance from UAV imagery remains challenging due to complex atmospheric radiation transmission above water bodies. This study proposes a method for water-leaving reflectance inversion based on air–ground collaborative correction. A fully connected neural network model was developed using TensorFlow Keras to establish a non-linear mapping between UAV hyperspectral reflectance and the measured near-water and water-leaving reflectance from ground-based spectral. This approach addresses the limitations of traditional linear correction methods by enabling spatiotemporal synchronization correction of UAV remote sensing images with ground observations, thereby minimizing atmospheric interference and sensor differences on signal transmission. The retrieved water-leaving reflectance closely matched measured data within the 450–900 nm band, with the average spectral angle mapping reduced from 0.5433 to 0.1070 compared to existing techniques. Moreover, the water quality parameter inversion models for turbidity, color, total nitrogen, and total phosphorus achieved high determination coefficients (R² = 0.94, 0.93, 0.88, and 0.85, respectively). The spatial distribution maps of water quality parameters were consistent with in situ measurements. Overall, this UAV hyperspectral remote sensing method, enhanced by air–ground collaborative correction, offers a reliable approach for UAV hyperspectral water quality remote sensing and promotes the advancement of stereoscopic water environment monitoring.