Search Results (12)

Automatic registration of remote sensing images is an important task, which requires the establishment of appropriate correspondence between the sensed image and the reference image. Nowadays, the trend of satellite remote sensing technology is shifting towards high-resolution hyperspectral imaging technology. Ever higher revisit cycles and image resolutions require higher accuracy and real-time performance for automatic registration. The push-broom payload is affected by the push-broom stability of the satellite platform and the elevation change of ground objects, and the obtained hyperspectral image may have distortions such as stretching or shrinking at different parts of the image. In order to solve this problem, a new automatic registration strategy for remote sensing hyperspectral images based on the combination of whole and local features of the image was established, and two granularity registrations were carried out, namely coarse-grained matching and fine-grained matching. The high-resolution spatial features are first employed for detecting scale-invariant features, while the spectral information is used for matching, and then the idea of image stitching is employed to fuse the image after fine registration to obtain high-precision registration results. In order to verify the proposed algorithm, a simulated on-orbit push-broom imaging experiment was carried out to obtain hyperspectral images with local complex distortions under different lighting conditions. The simulation results show that the proposed remote sensing hyperspectral image registration algorithm is superior to the existing automatic registration algorithms. The advantages of the proposed algorithm in terms of registration accuracy and real-time performance make it have a broad prospect for application in satellite ground application systems. Full article

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. Full article

The digitization of pathology departments in hospitals around the world is now a reality. The current commercial solutions applied to digitize histopathological samples consist of a robotic microscope with an RGB-type camera attached to it. This technology is very limited in terms of information captured, as it only works with three spectral bands of the visible electromagnetic spectrum. Therefore, we present an automated system that combines RGB and hyperspectral technology. Throughout this work, the hardware of the system and its components are described along with the developed software and a working methodology to ensure the correct capture of histopathological samples. The software is integrated by the controller of the microscope, which features an autofocus functionality, whole slide scanning with a stitching algorithm, and hyperspectral scanning functionality. As a reference, the time to capture and process a complete sample with 20 regions of high biological interest using the proposed method is estimated at a maximum of 79 min, reducing the time required by a manual operator by at least three times. Both hardware and software can be easily adapted to other systems that might benefit from the advantages of hyperspectral technology. Full article

Crop phenotype observation techniques via UAV (unmanned aerial vehicle) are necessary to identify different winter wheat cultivars to better realize their future smart productions and satisfy the requirement of smart agriculture. This study proposes a UAV-based hyperspectral remote sensing system for the fine classification of different winter wheat cultivars. Firstly, we set 90% heading overlap and 85% side overlap as the optimal flight parameters, which can meet the requirements of following hyperspectral imagery mosaicking and spectral stitching of different winter wheat cultivars areas. Secondly, the mosaicking algorithm of UAV hyperspectral imagery was developed, and the correlation coefficient of stitched spectral curves before and after mosaicking reached 0.97, which induced this study to extract the resultful spectral curves of six different winter wheat cultivars. Finally, the hyperspectral imagery dimension reduction experiments were compared with principal component analysis (PCA), minimum noise fraction rotation (MNF), and independent component analysis (ICA); the winter wheat cultivars classification experiments were compared with support vector machines (SVM), maximum likelihood estimate (MLE), and U-net neural network ENVINet5 model. Different dimension reduction methods and classification methods were compared to get the best combination for classification of different winter wheat cultivars. The results show that the mosaicked hyperspectral imagery effectively retains the original spectral feature information, and type 4 and type 6 winter wheat cultivars have the best classification results with the classification accuracy above 84%. Meanwhile, there is a 30% improvement in classification accuracy after dimension reduction, the MNF dimension reduction combined with ENVINet5 classification result is the best, its overall accuracy and Kappa coefficients are 83% and 0.81, respectively. The results indicate that the UAV-based hyperspectral remote sensing system can potentially be used for classifying different cultivars of winter wheat, and it provides a reference for the classification of crops with weak intra-class differences. Full article

The unmanned aerial vehicle (UAV)-borne hyperspectral imaging system has the advantages of high spatial resolution, flexible operation, under-cloud flying, and easy cooperation with ground synchronous tests. Because this platform often flies under clouds, variations in solar illumination lead to irradiance inconsistency between different rows of hyperspectral images (HSIs). This inconsistency causes errors in radiation correction. In addition, due to the accuracy limitations of the GPS/inertial measurement unit (IMU) and irregular changes in flight platform speed and attitude, HSIs have deformation and drift, which is harmful to the geometric correction and stitching accuracy between flight strips. Consequently, radiation and geometric error limit further applications of large-scale hyperspectral data. To address the above problems, we proposed an integrated solution to acquire and correct UAV-borne hyperspectral images that consist of illumination data acquisition, radiance and geometric correction, HSI, multispectral image (MSI) registration, and multi-strip stitching. We presented an improved three-parameter empirical model based on the illumination correction factor, and it showed that the accuracy of radiation correction considering illumination variation improved, especially in some low signal-to-noise ratio (SNR) bands. In addition, the error of large-scale HSI stitching was controlled within one pixel. Full article

Remote-sensing developments such as UAVs heighten the need for hyperspectral image stitching techniques that can obtain information on a large area through various parts of the same scene. State-of-the-art approaches often suffer from accumulation errors and high computational costs for large-scale hyperspectral remote-sensing images. In this study, we aim to generate high-precision hyperspectral panoramas with less spatial and spectral distortion. We introduce a new stitching strategy and apply it to hyperspectral images. The stitching framework was built as follows: First, a single band obtained by signal-to-noise ratio estimation was chosen as the reference band. Then, a feature-matching method combining the SuperPoint and LAF algorithms was adopted to strengthen the reliability of feature correspondences. Adaptive bundle adjustment was also designed to eliminate misaligned artifact areas and occasional accumulation errors. Lastly, a spectral correction method using covariance correspondences is proposed to ensure spectral consistency. Extensive feature-matching and image-stitching experiments on several hyperspectral datasets demonstrate the superiority of our approach over the state of the art. Full article

This paper proposes a systematic image mosaicking methodology to produce hyperspectral image for environment monitoring using an emerging UAV-based push-broom hyperspectral imager. The suitability of alternative methods in each step is assessed by experiments of an urban scape, a river course and a forest study area. First, the hyperspectral image strips were acquired by sequentially stitching the UAV images acquired by push-broom scanning along each flight line. Next, direct geo-referencing was applied to each image strip to get initial geo-rectified result. Then, with ground control points, the curved surface spline function was used to transform the initial geo-rectified image strips to improve their geometrical accuracy. To further remove the displacement between pairs of image strips, an improved phase correlation (IPC) and a SIFT and RANSAC-based method (SR) were used in image registration. Finally, the weighted average and the best stitching image fusion method were used to remove the spectral differences between image strips and get the seamless mosaic. Experiment results showed that as the GCPs‘ number increases, the mosaicked image‘s geometrical accuracy increases. In image registration, there exists obvious edge information that can be accurately extracted from the urban scape and river course area; comparative results can be achieved by the IPC method with less time cost. However, for the ground objects with complex texture like forest, the edges extracted from the image is prone to be inaccurate and result in the failure of the IPC method, and only the SR method can get a good result. In image fusion, the best stitching fusion method can get seamless results for all three study areas. Whereas, the weighted average fusion method was only useful in eliminating the stitching line for the river course and forest areas but failed for the urban scape area due to the spectral heterogeneity of different ground objects. For different environment monitoring applications, the proposed methodology provides a practical solution to seamlessly mosaic UAV-based push-broom hyperspectral images with high geometrical accuracy and spectral fidelity. Full article

The miniaturization of hyperspectral cameras has opened a new path to capture spectral information. One such camera, called the hybrid linescan camera, requires accurate control of its movement. Contrary to classical linescan cameras, where one line is available for every band in one shot, the latter asks for multiple shots to fill a line with multiple bands. Unfortunately, the reconstruction is corrupted by a parallax effect, which affects each band differently. In this article, we propose a two-step procedure, which first reconstructs an approximate datacube in two different ways, and second, performs a corrective warping on each band based on a multiple homography framework. The second step combines different stitching methods to perform this reconstruction. A complete synthetic and experimental comparison is performed by using geometric indicators of reference points. It appears throughout the course of our experimentation that misalignment is significantly reduced but remains non-negligible at the potato leaf scale. Full article

The field of view (FOV) of pushbroom hyperspectral imager is limited by the compromise of the detector scale and requirements of spatial resolution. Combining imagers along the sampling direction effectively expands its FOV and improves the imaging efficiency. Due to the small overlapping area between the adjacent imagers, stitching the images using traditional methods need a large amount of ground control points (GCPs) or additional strips, which reduce the efficiency of both image acquisition and processing. This paper proposed a new method to precisely stitch images acquired from multiple pushbroom imagers. First, the relative orientation model was built based on the homonymy points to calculate the relative relationship between the adjacent imagers. Then rigorous geometric imaging model was adopted to generate a seamless stitching image. Simulation data was used to verify the accuracy of the method and to quantitatively analyze the effect of different error sources. Results show that the stitching accuracy is better than two pixels. Overall, this method provides a novel solution for stitching airborne multiple pushbroom images, to generate the seamless stitching image with wide FOV. Full article

Crop stand count and uniformity are important measures for making proper field management decisions to improve crop production. Conventional methods for evaluating stand count based on visual observation are time consuming and labor intensive, making it difficult to adequately cover a large field. The overall goal of this study was to evaluate cotton emergence at two weeks after planting using unmanned aerial vehicle (UAV)-based high-resolution narrow-band spectral indices that were collected using a pushbroom hyperspectral imager flying at 50 m above ground. A customized image alignment and stitching algorithm was developed to process hyperspectral cubes efficiently and build panoramas for each narrow band. The normalized difference vegetation index (NDVI) was calculated to segment cotton seedlings from soil background. A Hough transform was used for crop row identification and weed removal. Individual seedlings were identified based on customized geometric features and used to calculate stand count. Results show that the developed alignment and stitching algorithm had an average alignment error of 2.8 pixels, which was much smaller than that of 181 pixels from the associated commercial software. The system was able to count the number of seedlings in seedling clusters with an accuracy of 84.1%. Mean absolute percentage error (MAPE) in estimation of crop density at the meter level was 9.0%. For seedling uniformity evaluation, the MAPE of seedling spacing was 9.1% and seedling spacing standard deviation was 6.8%. Results showed that UAV-based high-resolution narrow-band spectral images had the potential to evaluate cotton emergence. Full article

A hyperspectral image (HSI) contains fine and rich spectral information and spatial information of ground objects, which has great potential in applications. It is also widely used in precision agriculture, marine monitoring, military reconnaissance and many other fields. In recent years, a convolutional neural network (CNN) has been successfully used in HSI classification and has provided it with outstanding capacity for improving classification effects. To get rid of the bondage of strong correlation among bands for HSI classification, an effective CNN architecture is proposed for HSI classification in this work. The proposed CNN architecture has several distinct advantages. First, each 1D spectral vector that corresponds to a pixel in an HSI is transformed into a 2D spectral feature matrix, thereby emphasizing the difference among samples. In addition, this architecture can not only weaken the influence of strong correlation among bands on classification, but can also fully utilize the spectral information of hyperspectral data. Furthermore, a 1 × 1 convolutional layer is adopted to better deal with HSI information. All the convolutional layers in the proposed CNN architecture are composed of small convolutional kernels. Moreover, cascaded composite layers of the architecture consist of 1 × 1 and 3 × 3 convolutional layers. The inputs and outputs of each composite layer are stitched as the inputs of the next composite layer, thereby accomplishing feature reuse. This special module with joint alternate small convolution and feature reuse can extract high-level features from hyperspectral data meticulously and comprehensively solve the overfitting problem to an extent, in order to obtain a considerable classification effect. Finally, global average pooling is used to replace the traditional fully connected layer to reduce the model parameters and extract high-dimensional features from the hyperspectral data at the end of the architecture. Experimental results on three benchmark HSI datasets show the high classification accuracy and effectiveness of the proposed method. Full article

The scale invariant feature transform (SIFT) is a widely used interest operator for supporting tasks such as 3D matching, 3D scene reconstruction, panorama stitching, image registration and motion tracking. Although SIFT is reported to be robust to disparate radiometric and geometric conditions in visible light imagery, using the default input parameters does not yield satisfactory results when matching imagery acquired at non-overlapping wavelengths. In this paper, optimization of the SIFT parameters for matching multi-wavelength image sets is documented. In order to integrate hyperspectral panoramic images with reference imagery and 3D data, corresponding points were required between visible light and short wave infrared images, each acquired from a slightly different position and with different resolutions and geometric projections. The default SIFT parameters resulted in too few points being found, requiring the influence of five key parameters on the number of matched points to be explored using statistical techniques. Results are discussed for two geological datasets. Using the SIFT operator with optimized parameters and an additional outlier elimination method, allowed between four and 22 times more homologous points to be found with improved image point distributions, than using the default parameter values recommended in the literature. Full article