An IHS-Based Pan-Sharpening Method for Spectral Fidelity Improvement Using Ripplet Transform and Compressed Sensing

Yang, Chen; Zhan, Qiwen; Liu, Huimin; Ma, Ruiqi

doi:10.3390/s18113624

Cited by 27 publications

(10 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The accuracy of all six monthly LST patterns are evaluated using STD, bias [61] and correlation coefficient (CC) [33,62]. The detailed assessments of monthly LST patterns are reported in Table 6.…”

Section: Resultsmentioning

confidence: 99%

How Do the Multi-Temporal Centroid Trajectories of Urban Heat Island Correspond to Impervious Surface Changes: A Case Study in Wuhan, China

Yang

Zhan

Gao

et al. 2019

IJERPH

Self Cite

View full text Add to dashboard Cite

Conspicuous expansion and intensification of impervious surfaces accompanied by rapid urbanization are widely recognized to have exerted evident impacts on the urban thermal environment. Investigating the spatially and temporally varying relationships between Land Surface Temperature (LST) and impervious surfaces (IS) at multiple scales is of great significance for steering IS expansion and intensification. This study proposes an analytical framework to investigate the spatiotemporal variations of LST and its responses to IS in Wuhan, China at both city scale and sub-region scale. The summer LST patterns in 2002–2017 are extracted by Multi-Task Gaussian Process (MTGP) model from raw 8-day synthesized MODerate-resolution Imaging Spectroradiometer (MODIS) LST data. At the city scale, the weighted center of LST (LSTWC) and impervious surface fraction (ISFWC), multi-temporal trajectories and coupling indicators are utilized to comprehensively examine the spatial and temporal dynamics of LST and IS within Wuhan. At the sub-region scale, urban heat island ratio index (URI), impervious surfaces contribution index (ISCI) and sprawl rate are introduced for further quantifying the relationships of LST and IS. The results reveal that IS and hot thermal landscapes expanded by 407.43 km2 and 255.82 km2 in Wuhan in 2002–2017 at city scale. The trajectories of LSTWCs and ISFWCs are visually coherent and both heading to southeast direction in general. At the sub-region scale, the specific cardinal directions with the highest ISCI variations are examined to be the exact directions of ISFWC trajectories in 2002–2017. The results reveal that the spatiotemporal variations of LST and IS are highly correlated at both city and sub-region scales within Wuhan, thus testifying the significance of steering IS expansion and renewal for controlling urban thermal environment deterioration.

show abstract

Section: Resultsmentioning

confidence: 99%

How Do the Multi-Temporal Centroid Trajectories of Urban Heat Island Correspond to Impervious Surface Changes: A Case Study in Wuhan, China

Yang

Zhan

Gao

et al. 2019

IJERPH

Self Cite

View full text Add to dashboard Cite

show abstract

“…To modify the former method, a generalized IHS [15], in which the near-infrared (NIR) band was incorporated successfully, was developed and applied to IKONOS imagery. Furthermore, an IHS-Based pan-sharpening method for spectral fidelity improvement using Ripplet transform and compressed sensing is proposed [51], which implements discrete ripplet transform to obtain multi-scale sub-images and compressed sensing to reconstruct the intensity component. And outperforms five advanced methods on WorldView-2, Pleiades and Triplesat datasets.…”

Section: A Component Substitution (Cs)mentioning

confidence: 99%

A Remote-Sensing Image Pan-Sharpening Method Based on Multi-Scale Channel Attention Residual Network

Lyu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Pan-sharpening is a significant task that aims to generate high spectral-and spatial-resolution remote-sensing image by fusing multi-spectral (MS) and panchromatic (PAN) image. The conventional approaches are insufficient to protect the fidelity both in spectral and spatial domains. Inspired by the robust capability and outstanding performance of convolutional neural networks (CNN) in natural image super-resolution tasks, CNN-based pan-sharpening methods are worthy of further exploration. In this paper, a novel pan-sharpening method is proposed by introducing a multi-scale channel attention residual network (MSCARN), which can represent features accurately and reconstruct a pan-sharpened image comprehensively. In MSCARN, the multi-scale feature extraction blocks comprehensively extract the coarse structures and high-frequency details. Moreover, the multi-residual architecture guarantees the consistency of feature learning procedure and accelerates convergence. Specifically, we introduce a channel attention mechanism to recalibrate the channel-wise features by considering interdependencies among channels adaptively. The extensive experiments are implemented on two real-datasets from GaoFen series satellites. And the results show that the proposed method performs better than the existing methods both in full-reference and no-reference metrics, meanwhile, the visual inspection displays in accordance with the quantitative metrics. Besides, in comparison with pan-sharpening by convolutional neural networks (PNN), the proposed method achieves faster convergence rate and lower loss.

show abstract

“…It has several advantages over other types of multiscale and multidirectional transforms. The ripplet transform is used in various applications such as remote sensing, 20,21 ultrasound medial image analysis, 22 image compression, 23 image segmentation, 24 image retrieval, 25 image fusion, 26 and gesture recognition 27 . The limitations of other higher order wavelet transforms are overcome by ripplet transform.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for thermal crack detection and quantification in structural concrete using ripplet transform

Andrushia

Anand

2020

Struct Control Health Monit

View full text Add to dashboard Cite

Fire accidents in the concrete structures affect the safety of human beings and structural components. Due to higher temperatures, thermal cracks are conceived in concrete elements. Crack identification, localization, and quantification are the three major components in the damage assessment system. This paper aims to detect thermal cracks of fire-affected concrete structures using ripplet transform-based computer vision method. Initially, the concrete images are decomposed with discrete ripplet transform (DRT). The low frequency subbands are approximated with column filtering-based gray level difference metric. It removes the uneven concrete background. Ripplet coefficient variance parameter (RCVP) is used to differentiate the crack pixels from noisy pixels which is used to eliminate the noises. Finally, in the reconstructed image, the major and minor thermal cracks are detected. The obtained thermal cracks are quantified with the crack properties of length, width, area, and perimeter. The novelty of the proposed method relays on the usage of ripplet transform for the detection of thermal cracks for different concrete grades and different durations of temperature profile. The experimental results are compared with four transform domain state-of-the-method crack detection methods. The proposed method yields better results in terms of the accuracy and average execution time. K E Y W O R D S crack detection, crack properties, discrete ripplet transform, temperature, thermal crack 1 | INTRODUCTION Many automatic methods are being developed to monitor the structural health. The automatic methods are developed with the objective of reducing the manpower requirement and to perform effective analysis, so as to minimize the human errors in the concrete structures. Periodical evaluation is done through automatic systems to identify the damage of concrete structures. Assessment of damages in various infrastructure is possible through structural health monitoring (SHM) approaches. 1,2 Cracks are present in all type of concrete structures. Concrete structures are often affected by stress, environmental factors, heavy loading, and aging. In extreme cases, the damages to concrete structures may result in loss of the life of occupants. Identification and quantification of cracks are paramount to evaluate the present conditions of structures and to take necessary actions for retrofitting the structures.

show abstract

An IHS-Based Pan-Sharpening Method for Spectral Fidelity Improvement Using Ripplet Transform and Compressed Sensing

Cited by 27 publications

References 69 publications

How Do the Multi-Temporal Centroid Trajectories of Urban Heat Island Correspond to Impervious Surface Changes: A Case Study in Wuhan, China

How Do the Multi-Temporal Centroid Trajectories of Urban Heat Island Correspond to Impervious Surface Changes: A Case Study in Wuhan, China

A Remote-Sensing Image Pan-Sharpening Method Based on Multi-Scale Channel Attention Residual Network

A novel approach for thermal crack detection and quantification in structural concrete using ripplet transform

Contact Info

Product

Resources

About