Pan-sharpening via deep metric learning

Xing, Yinghui; Wang, Min; Yang, Shuyuan; Jiao, Licheng

doi:10.1016/j.isprsjprs.2018.01.016

Cited by 60 publications

(23 citation statements)

References 45 publications

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“…Although there are studies categorized above in different disciplines on deep metric learning, it is possible to find more studies carried out by researchers in other disciplines where some issues regarding music similarity [60], crowdedness regression [61], similar region search [62], volumetric image recognition [63], instance segmentation [64], edge detection [65], pan-sharpening [66], and so on were examined. Therefore, deep metric learning can be claimed to offer invaluable contributions to the literature, due to its high performance in different fields.…”

Section: Deep Metric Learning Problemsmentioning

confidence: 99%

Deep Metric Learning: A Survey

2019

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Metric learning aims to measure the similarity among samples while using an optimal distance metric for learning tasks. Metric learning methods, which generally use a linear projection, are limited in solving real-world problems demonstrating non-linear characteristics. Kernel approaches are utilized in metric learning to address this problem. In recent years, deep metric learning, which provides a better solution for nonlinear data through activation functions, has attracted researchers' attention in many different areas. This article aims to reveal the importance of deep metric learning and the problems dealt with in this field in the light of recent studies. As far as the research conducted in this field are concerned, most existing studies that are inspired by Siamese and Triplet networks are commonly used to correlate among samples while using shared weights in deep metric learning. The success of these networks is based on their capacity to understand the similarity relationship among samples. Moreover, sampling strategy, appropriate distance metric, and the structure of the network are the challenging factors for researchers to improve the performance of the network model. This article is considered to be important, as it is the first comprehensive study in which these factors are systematically analyzed and evaluated as a whole and supported by comparing the quantitative results of the methods.

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Section: Deep Metric Learning Problemsmentioning

confidence: 99%

Deep Metric Learning: A Survey

2019

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“…Yao et al [164] presented a variant of U-Net, the pansharpening U-Net, to enhance resolution of multispectral images, in which the inputs are low-resolution multispectral and panchromatic images, and the outputs are highresolution multispectral images. Xing et al [165] trained an ensemble of SSAE to perform pansharpening of low-resolution panchromatic images. In their method, low-resolution images from different satellites are first divided into a large number of training image patches and are then clustered according to their shallow geometric structures.…”

Section: Spatial and Temporal Data Fusionmentioning

confidence: 99%

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

Sun

Scanlon

2019

Environ. Res. Lett.

304

129

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Big Data and machine learning (ML) technologies have the potential to impact many facets of environment and water management (EWM). Big Data are information assets characterized by high volume, velocity, variety, and veracity. Fast advances in high-resolution remote sensing techniques, smart information and communication technologies, and social media have contributed to the proliferation of Big Data in many EWM fields, such as weather forecasting, disaster management, smart water and energy management systems, and remote sensing. Big Data brings about new opportunities for data-driven discovery in EWM, but it also requires new forms of information processing, storage, retrieval, as well as analytics. ML, a subdomain of artificial intelligence (AI), refers broadly to computer algorithms that can automatically learn from data. ML may help unlock the power of Big Data if properly integrated with data analytics. Recent breakthroughs in AI and computing infrastructure have led to the fast development of powerful deep learning (DL) algorithms that can extract hierarchical features from data, with better predictive performance and less human intervention. Collectively Big Data and ML techniques have shown great potential for data-driven decision making, scientific discovery, and process optimization. These technological advances may greatly benefit EWM, especially because (1) many EWM applications (e.g. early flood warning) require the capability to extract useful information from a large amount of data in autonomous manner and in real time, (2) EWM researches have become highly multidisciplinary, and handling the ever increasing data volume/types using the traditional workflow is simply not an option, and last but not least, (3) the current theoretical knowledge about many EWM processes is still incomplete, but which may now be complemented through data-driven discovery. A large number of applications on Big Data and ML have already appeared in the EWM literature in recent years. The purposes of this survey are to (1) examine the potential and benefits of data-driven research in EWM, (2) give a synopsis of key concepts and approaches in Big Data and ML, (3) provide a systematic review of current applications, and finally (4) discuss major issues and challenges, and recommend future research directions. EWM includes a broad range of research topics. Instead of attempting to survey each individual area, this review focuses on areas of nexus in EWM, with an emphasis on elucidating the potential benefits of increased data availability and predictive analytics to improving the EWM research.

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“…A range of cost functions and correlation analyses have been used in previous studies to assess spectral and spatial fusion image. However, due to the lack of reference images at high resolution, most fusion image assessments have been carried out at low resolution and use original images [16][17][18]20,39]. A few studies have used ground objects to assess fusion images directly, but most have focused on the classification of different ground objects [13,40,41], ranging into qualitative remote sensing (RS).…”

Section: Assessment Of the Fusion Image Via Wheat Lai Predictionmentioning

confidence: 99%

Generating Red-Edge Images at 3 M Spatial Resolution by Fusing Sentinel-2 and Planet Satellite Products

Jiang

Guo

et al. 2019

Remote Sensing

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High-resolution satellite images can be used to some extent to mitigate the mixed-pixel problem caused by the lack of intensive production, farmland fragmentation, and the uneven growth of field crops in developing countries. Specifically, red-edge (RE) satellite images can be used in this context to reduce the influence of soil background at early stages as well as saturation due to crop leaf area index (LAI) at later stages. However, the availability of high-resolution RE satellite image products for research and application globally remains limited. This study uses the weight-and-unmixing algorithm as well as the SUPer-REsolution for multi-spectral Multi-resolution Estimation (Wu-SupReME) approach to combine the advantages of Sentinel-2 spectral and Planet spatial resolution and generate a high-resolution RE product. The resultant fused image is highly correlated (R2 > 0.98) with Sentinel-2 image and clearly illustrates the persistent advantages of such products. This fused image was significantly more accurate than the originals when used to predict heterogeneous wheat LAI and therefore clearly illustrated the persistence of Sentinel-2 spectral and Planet spatial advantage, which indirectly proved that the fusion methodology of generating high-resolution red-edge products from Planet and Sentinel-2 images is possible. This study provided method reference for multi-source data fusion and image product for accurate parameter inversion in quantitative remote sensing of vegetation.

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Pan-sharpening via deep metric learning

Cited by 60 publications

References 45 publications

Deep Metric Learning: A Survey

Deep Metric Learning: A Survey

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

Generating Red-Edge Images at 3 M Spatial Resolution by Fusing Sentinel-2 and Planet Satellite Products

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