Illumination-invariant image matching for autonomous UAV localisation based on optical sensing

Wan, Xue; Liu, Jianguo; Yan, Hongshi; Morgan, Gareth

doi:10.1016/j.isprsjprs.2016.05.016

Cited by 46 publications

(26 citation statements)

References 30 publications

(28 reference statements)

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“…Geometric constraints based on pixel distance to correct matches are employed for mismatch removal at repetitive image regions. Considering variation of illumination between UAV and satellite images, illuminationinvariant image matching is proposed based on phase correlation to match the on-board UAV image sequences to a pre-installed reference satellite images for UAV localization and navigation [84].…”

Section: Low-altitude Uav Image Matchingmentioning

confidence: 99%

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Xiang

Xia

Zhang

2019

IEEE Geosci. Remote Sens. Mag.

149

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The past few decades have witnessed the great progress of unmanned aircraft vehicles (UAVs) in civilian fields, especially in photogrammetry and remote sensing. In contrast with the platforms of manned aircraft and satellite, the UAV platform holds many promising characteristics: flexibility, efficiency, highspatial/temporal resolution, low cost, easy operation, etc., which make it an effective complement to other remote-sensing platforms and a cost-effective means for remote sensing. Considering the popularity and expansion of UAV-based remote sensing in recent years, this paper provides a systematic survey on the recent advances and future prospectives of UAVs in the remote-sensing community. Specifically, the main challenges and key technologies of remote-sensing data processing based on UAVs are discussed and summarized firstly. Then, we provide an overview of the widespread applications of UAVs in remote sensing. Finally, some prospects for future work are discussed. We hope this paper will provide remotesensing researchers an overall picture of recent UAV-based remote sensing developments and help guide the further research on this topic.

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Section: Low-altitude Uav Image Matchingmentioning

confidence: 99%

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Xiang

Xia

Zhang

2019

IEEE Geosci. Remote Sens. Mag.

149

View full text Add to dashboard Cite

show abstract

“…In the frequency domain, the most popular similarity measure is phase correlation (PC), which can quickly estimate the translations between images based on the Fourier shift theorem (Bracewell and Bracewell, 1986). Nowadays, PC has been extended to account for scale and rotation changes (Reddy and Chatterji, 1996), and also applied to remote sensing image registration (Wan et al, 2016;Wan et al, 2015). Compared with the spatial measures, its main advantage is the high computational efficiency (Wong and Orchard, 2008).…”

Section: Related Workmentioning

confidence: 99%

Fast and Robust Matching for Multimodal Remote Sensing Image Registration

Bruzzone

Shan

et al. 2019

IEEE Trans. Geosci. Remote Sensing

268

104

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Declarations of interest: none1 Abstract: While image registration has been studied in remote sensing community for decades, registering multimodal data [e.g., optical, light detection and ranging (LiDAR), synthetic aperture radar (SAR), and map] remains a challenging problem because of significant nonlinear intensity differences between such data. To address this problem, we present a novel fast and robust matching framework integrating local descriptors for multimodal registration. In the proposed framework, a local descriptor (such as Histogram of Oriented Gradient (HOG), Local Self-Similarity or Speeded-Up Robust Feature) is first extracted at each pixel to form a pixel-wise feature representation of an image. Then we define a similarity measure based on the feature representation in frequency domain using the Fast Fourier Transform (FFT) technique, followed by a template matching scheme to detect control points between images. We also propose a novel pixel-wise feature representation using orientated gradients of images, which is named channel features of orientated gradients (CFOG). This novel feature is an extension of the pixel-wise HOG descriptor, and outperforms that both in matching performance and computational efficiency. The major advantages of the proposed framework include (1) structural similarity representation using the pixel-wise feature description and (2) high computational efficiency due to the use of FFT. Moreover, we design an automatic registration system for very large-size multimodal images based on the proposed framework. Experimental results obtained on many different types of multimodal images show the superior matching performance of the proposed framework with respect to the state-of-the-art methods and the effectiveness of the designed system, which show very good potential large-size image registration in real applications.

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“…In the simulated experiments, the normalized correlation coefficient (NCC), 48,49 coefficient of determination (R 2 ), root mean square error (RMSE), SNR, and PSNR 50 between the smoothed spectrum and standard vegetation spectrum were calculated to evaluate the denoising results. For measured canopy spectra of winter wheat, NCC was used to estimate the waveform similarity of the spectra before and after denoising, to assess the results qualitatively, whereas wheat biophysical and biochemical parameters were retrieved using different hyperspectral vegetation indices derived from denoised spectra to quantitatively assess the results.…”

Section: Evaluation Criteriamentioning

confidence: 99%

Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters

Zhang

et al. 2019

J. Appl. Rem. Sens.

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Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters,"Abstract. Spectral noise causes distorted spectra, shifting the central wavelength and thus reducing the accuracy of surface parameter retrieval. A hybrid method combining mathematical-morphology and wavelet-transform (WB)-based filters was used to remove spectral noise. First, a generalized mathematical-morphology (GM) filter was used to remove large-amplitude noise, and then the processed spectra were smoothed using the WT-based filter to remove smallamplitude noise. The simulated noise spectrum and 76 measured canopy spectra for winter wheat were denoised with three filters: the combination filter (CF), GM, and WT. In the simulated experiments, five evaluation indices were calculated to evaluate the denoising effects. For measured spectra, qualitative analyses were performed based on spectral characteristics. Quantitative evaluations were conducted by deriving various vegetation indices from denoised spectra to retrieve wheat's biophysical and biochemical parameters. The results indicated that the CF removed both large-and small-amplitude noise efficiently, improving signal-to-noise ratio and peak signal-to-noise ratio of simulated noise spectrum and retrieval accuracy of leaf water content (LWC) significantly. Meanwhile, it better maintained the waveform and smoothness of spectrum, improving the retrieval accuracies of leaf area index and chlorophyll data slightly. The coefficient of determination (R 2 ) of developed model between the modified normalized difference water index and LWC was improved from 0.428 to 0.622 using the CF, 0.555 using the GM, and 0.549 using the WT. The R 2 and root mean square error between the measured and retrieval LWC were improved from 0.364 and 0.027 to 0.611 and 0.018 using the CF, whereas the corresponding values were 0.504 and 0.022 for the GM, and 0.478 and 0.023 for the WT. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Illumination-invariant image matching for autonomous UAV localisation based on optical sensing

Cited by 46 publications

References 30 publications

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

Fast and Robust Matching for Multimodal Remote Sensing Image Registration

Denoising vegetation spectra by combining mathematical-morphology and wavelet-transform-based filters

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