Robust registration for remote sensing images by combining and localizing feature- and area-based methods

Feng, Ruitao; Du, Qingyun; Li, Xinghua; Shen, Huanfeng

doi:10.1016/j.isprsjprs.2019.03.002

Cited by 87 publications

(43 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Area-based matching methods directly utilize intensity-based information to match images or regions. This type of matching method is widely used to optimize the coarse registration of remote sensing images due to the superiority in accuracy [24]. The adopted similarity measure is a decisive component of area-based methods.…”

Section: Fine Registration Using Area-based Methodsmentioning

confidence: 99%

Robust Fine Registration of Multisensor Remote Sensing Images Based on Enhanced Subpixel Phase Correlation

Kang

Song

et al. 2020

Sensors

View full text Add to dashboard Cite

Automatic fine registration of multisensor images plays an essential role in many remote sensing applications. However, it is always a challenging task due to significant radiometric and textural differences. In this paper, an enhanced subpixel phase correlation method is proposed, which embeds phase congruency-based structural representation, L1-norm-based rank-one matrix approximation with adaptive masking, and stable robust model fitting into the conventional calculation framework in the frequency domain. The aim is to improve the accuracy and robustness of subpixel translation estimation in practical cases. In addition, template matching using the enhanced subpixel phase correlation is integrated to realize reliable fine registration, which is able to extract a sufficient number of well-distributed and high-accuracy tie points and reduce the local misalignment for coarsely coregistered multisensor remote sensing images. Experiments undertaken with images from different satellites and sensors were carried out in two parts: tie point matching and fine registration. The results of qualitative analysis and quantitative comparison with the state-of-the-art area-based and feature-based matching methods demonstrate the effectiveness and reliability of the proposed method for multisensor matching and registration.

show abstract

Section: Fine Registration Using Area-based Methodsmentioning

confidence: 99%

Robust Fine Registration of Multisensor Remote Sensing Images Based on Enhanced Subpixel Phase Correlation

Kang

Song

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The DTW is used as it aligns the sensor data such as voice, action, or other measurements [1,20]. However, for image or text data, other alignment methods can be more suitable [21,22]. In the introduced method, given…”

Section: Proposed Methodsmentioning

confidence: 99%

Data Augmentation with Suboptimal Warping for Time-Series Classification

Kamycki

Kapuściński

Oszust

2019

Sensors

View full text Add to dashboard Cite

In this paper, a novel data augmentation method for time-series classification is proposed. In the introduced method, a new time-series is obtained in warped space between suboptimally aligned input examples of different lengths. Specifically, the alignment is carried out constraining the warping path and reducing its flexibility. It is shown that the resultant synthetic time-series can form new class boundaries and enrich the training dataset. In this work, the comparative evaluation of the proposed augmentation method against related techniques on representative multivariate time-series datasets is presented. The performance of methods is examined using the nearest neighbor classifier with the dynamic time warping (NN-DTW), LogDet divergence-based metric learning with triplet constraints (LDMLT), and the recently introduced time-series cluster kernel (NN-TCK). The impact of the augmentation on the classification performance is investigated, taking into account entire datasets and cases with a small number of training examples. The extensive evaluation reveals that the introduced method outperforms related augmentation algorithms in terms of the obtained classification accuracy.

show abstract

“…The main urban areas often cannot be covered in only one image due to the limitations of the imaging swath width or imaging mechanism. Stitching or mosaicking multiple images from different periods or sensors is often a necessary order to capture the full target area [14,[53][54][55]. Image registration [56] is usually required before the mosaicking of multiple images.…”

Section: Related Workmentioning

confidence: 99%

Generating High-Quality and High-Resolution Seamless Satellite Imagery for Large-Scale Urban Regions

Feng

et al. 2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Urban geographical maps are important to urban planning, urban construction, land-use studies, disaster control and relief, touring and sightseeing, and so on. Satellite remote sensing images are the most important data source for urban geographical maps. However, for optical satellite remote sensing images with high spatial resolution, certain inevitable factors, including cloud, haze, and cloud shadow, severely degrade the image quality. Moreover, the geometrical and radiometric differences amongst multiple high-spatial-resolution images are difficult to eliminate. In this study, we propose a robust and efficient procedure for generating high-resolution and high-quality seamless satellite imagery for large-scale urban regions. This procedure consists of image registration, cloud detection, thin/thick cloud removal, pansharpening, and mosaicking processes. Methodologically, a spatially adaptive method considering the variation of atmospheric scattering, and a stepwise replacement method based on local moment matching are proposed for removing thin and thick clouds, respectively. The effectiveness is demonstrated by a successful case of generating a 0.91-m-resolution image of the main city zone in Nanning, Guangxi Zhuang Autonomous Region, China, using images obtained from the Chinese Beijing-2 and Gaofen-2 high-resolution satellites.

show abstract

Robust registration for remote sensing images by combining and localizing feature- and area-based methods

Cited by 87 publications

References 57 publications

Robust Fine Registration of Multisensor Remote Sensing Images Based on Enhanced Subpixel Phase Correlation

Robust Fine Registration of Multisensor Remote Sensing Images Based on Enhanced Subpixel Phase Correlation

Data Augmentation with Suboptimal Warping for Time-Series Classification

Generating High-Quality and High-Resolution Seamless Satellite Imagery for Large-Scale Urban Regions

Contact Info

Product

Resources

About