Toward High Altitude Airship Ground-Based Boresight Calibration of Hyperspectral Pushbroom Imaging Sensors

et al. 2017

Sensors

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The spatial resolution of a hyperspectral image is often coarse as the limitations on the imaging hardware. A novel super-resolution reconstruction algorithm for hyperspectral imagery (HSI) via adaptive projection onto convex sets and image blur metric (APOCS-BM) is proposed in this paper to solve these problems. Firstly, a no-reference image blur metric assessment method based on Gabor wavelet transform is utilized to obtain the blur metric of the low-resolution (LR) image. Then, the bound used in the APOCS is automatically calculated via LR image blur metric. Finally, the high-resolution (HR) image is reconstructed by the APOCS method. With the contribution of APOCS and image blur metric, the fixed bound problem in POCS is solved, and the image blur information is utilized during the reconstruction of HR image, which effectively enhances the spatial-spectral information and improves the reconstruction accuracy. The experimental results for the PaviaU, PaviaC and Jinyin Tan datasets indicate that the proposed method not only enhances the spatial resolution, but also preserves HSI spectral information well.

Section: Experiments and Resultsmentioning

confidence: 99%

Hyperspectral Imagery Super-Resolution by Adaptive POCS and Blur Metric

et al. 2017

Sensors

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“…The width of the field view for tilting image was different from the different imaging angle

, and the field of view

will be:

where the

is the length of the CCD array in the imaging system. The sensor we used in this study has 840 bands, its detective wavelength from 400 nm to 1000 nm and its imaging principle is shown in Figure 2 a, the reflected light of objects enters the entrance slit via the objective lens, and is received by the area camera after passing through the beam split module, the data achieved by this sensor has two axes, one for spatial axis, another for spectral axis, which means the data do not only have the spatial information, but also with the spectral information [ 21 ]. While sampling (shown in Figure 2 b), it should move at a constant speed and sampling a line data for an image in each sampling frequency, at the end of the scanning, the objects hyperspectral imagery data were achieved.…”

Section: Methodsmentioning

confidence: 99%

Restoration and Calibration of Tilting Hyperspectral Super-Resolution Image

et al. 2020

Sensors

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Tilting sampling is a novel sampling mode for achieving a higher resolution of hyperspectral imagery. However, most studies on the tilting image have only focused on a single band, which loses the features of hyperspectral imagery. This study focuses on the restoration of tilting hyperspectral imagery and the practicality of its results. First, we reduced the huge data of tilting hyperspectral imagery by the p-value sparse matrix band selection method (pSMBS). Then, we restored the reduced imagery by optimal reciprocal cell combined modulation transfer function (MTF) method. Next, we built the relationship between the restored tilting image and the original normal image. We employed the least square method to solve the calibration equation for each band. Finally, the calibrated tilting image and original normal image were both classified by the unsupervised classification method (K-means) to confirm the practicality of calibrated tilting images in remote sensing applications. The results of classification demonstrate the optimal reciprocal cell combined MTF method can effectively restore the tilting image and the calibrated tiling image can be used in remote sensing applications. The restored and calibrated tilting image has a higher resolution and better spectral fidelity.

“…The large pitch angles and large roll angles appear due to the plateau airflow, and the orientation changes of the airship negatively affecting the push-broom images so that they are severely distorted as depicted in Figure 17③-1. We used Mark A.Warren's method [28], our geocorrection method [29] and the methods mentioned in Section to correct the image (the results are shown in Figure 17). The image data in this test cover vegetation, building, or road areas and water areas.…”

Section: ) Hyperspectral Image Datamentioning

confidence: 99%

ASQ-MPHAAS: Multi-Payload Observation System From High Altitude Airship

Chai

2019

IEEE Sensors J.

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One of the critical advantages of a high-altitude airship (HAA) is its ability to carry multiple-task payloads for remote sensing monitoring on the plateau. We developed a multipayload observation system based on the HAA (ASQ-MPHAAS) for remote sensing monitoring of the Qinghai plateau. In this paper, we demonstrate the design, development, implementation of the ASQ-MPHAAS. The ASQ-MPHAAS includes two parts; one part is our HAA (ASQ-HAA380), and the other part is our multi-payload observation system (ASQ-MPOS-1). The ASQ-MPOS-1 consists of two hyperspectral push-broom imaging sensors, a multispectral imaging sensor, a high-resolution RGB camera, a video sensor, a Position and Orientation System (POS), and other components. The ASQ-MPOS-1 is installed under the ASQ-HAA380. We also introduce some key methods of data processing we propose to solve unique problems brought by HAA. Four real-world field experiments are presented in this paper to demonstrate that the ASQ-MPHAAS can capture different kinds of remote sensing data at a high spatial resolution of a few or dozen centimeters. Index Terms-Multi-payload observation, high altitude airship time synchronization, hyperspectral imaging, multispectral imaging. I. INTRODUCTIONM OST parts of Qinghai Province are located at altitudes over 3000-4000 m. It is challenging to observe the grassland on Qinghai plateau using common airborne platforms such as manned aircraft and UAVs. Our research group developed a new multi-payload system based on an HAA (ASQ-MPHAAS) for remote sensing monitoring of the grasslands on Qinghai plateau. The ASQ-MPHAAS includes two parts, the HAA developed by our research group, called ASQ-HAA380, and the other part is our ASQ-MPOS-1. The ASQ-HAA380 is 38 meters long and 12 meters high and can fly at an altitude of 5000m above sea level. The ASQ-MPOS-1 includes a high spatial resolution camera with Manuscript