scholarly journals Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3929 ◽  
Author(s):  
Grigorios Tsagkatakis ◽  
Anastasia Aidini ◽  
Konstantina Fotiadou ◽  
Michalis Giannopoulos ◽  
Anastasia Pentari ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Large Body ◽  
Super Resolution ◽  
Imaging Systems ◽  
Learning Approaches ◽  
Language Understanding ◽  
Learning Tasks ◽  
Trade Offs ◽  
Sensing Platforms

Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language understanding. In remote sensing, a large body of research has been devoted to the application of deep learning for typical supervised learning tasks such as classification. Less yet equally important effort has also been allocated to addressing the challenges associated with the enhancement of low-quality observations from remote sensing platforms. Addressing such channels is of paramount importance, both in itself, since high-altitude imaging, environmental conditions, and imaging systems trade-offs lead to low-quality observation, as well as to facilitate subsequent analysis, such as classification and detection. In this paper, we provide a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution, denoising, restoration, pan-sharpening, and fusion, among others. In addition to the detailed analysis and comparison of recently presented approaches, different research avenues which could be explored in the future are also discussed.

A literature review of localization methods and emergence of deep learning in single-molecule localization microscopy

2020 ◽  
Author(s):  
Anish Mukherjee
Keyword(s):  
Deep Learning ◽  
Single Molecule ◽  
Super Resolution ◽  
Point Sources ◽  
Learning Approaches ◽  
Localization Algorithm ◽  
Localization Microscopy ◽  
Trade Offs ◽  
Emitter Localization ◽  
Single Molecule Localization Microscopy

The quality of super-resolution images largely depends on the performance of the emitter localization algorithm used to localize point sources. In this article, an overview of the various techniques which are used to localize point sources in single-molecule localization microscopy are discussed and their performances are compared. This overview can help readers to select a localization technique for their application. Also, an overview is presented about the emergence of deep learning methods that are becoming popular in various stages of single-molecule localization microscopy. The state of the art deep learning approaches are compared to the traditional approaches and the trade-offs of selecting an algorithm for localization are discussed.

scholarly journals Super-resolution for Ocean Bathymetric Maps Using Deep Learning Approaches : A Comparison and Validation

2021 ◽  
Vol 32 (1) ◽  
pp. 3-13
Author(s):  
Mitsuko HIDAKA ◽  
Daisuke MATSUOKA ◽  
Tatsu KUWATANI ◽  
Junji KANEKO ◽  
Takafumi KASAYA ◽  
...  
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Learning Approaches

scholarly journals Evolution and application of digital technologies to predict crop type and crop phenology in agriculture

2021 ◽  
Author(s):  
A B Potgieter ◽  
Yan Zhao ◽  
Pablo J Zarco-Tejada ◽  
Karine Chenu ◽  
Yifan Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Crop Production ◽  
Cropping Systems ◽  
Digital Technologies ◽  
Learning Approaches ◽  
Data Systems ◽  
Crop Phenology ◽  
Agricultural Industry ◽  
Sensing Platforms

Abstract The downside risk of crop production affects the entire supply chain of the agricultural industry nationally and globally. This also has a profound impact on food security, and thus livelihoods, in many parts of the world. The advent of high temporal, spatial and spectral resolution remote sensing platforms, specifically during the last five years, and the advancement in software pipelines and cloud computing have resulted in the collating, analysing and application of “BIG DATA” systems, especially in agriculture. Furthermore, the application of traditional and novel computational and machine learning approaches is assisting in resolving complex interactions, to reveal components of eco-physiological systems that were previously deemed either “too difficult” to solve or “unseen”. In this review, digital technologies encompass mathematical, computational, proximal- and remote sensing technologies. Here, we review the current state of digital technologies and their application in broad acre cropping systems globally and in Australia. More specifically, we discuss the advances in (i) remote sensing platforms, (ii) machine learning approaches to discriminate between crops, and (iii) the prediction of crop phenological stages from both sensing and crop simulation systems for major Australian winter crops. An integrated solution is proposed to allow accurate development, validation and scalability of predictive tools for crop phenology mapping at within-field scales, across extensive cropping areas.

scholarly journals Deep learning approaches for neural decoding across architectures and recording modalities

2020 ◽  
Author(s):  
Jesse A Livezey ◽  
Joshua I Glaser
Keyword(s):  
Deep Learning ◽  
Scientific Development ◽  
Cognitive State ◽  
Learning Approaches ◽  
Neural Decoding ◽  
New Wave ◽  
Neural Signals ◽  
Learning Tasks ◽  
Wide Range ◽  
Deep Networks

Abstract Decoding behavior, perception or cognitive state directly from neural signals is critical for brain–computer interface research and an important tool for systems neuroscience. In the last decade, deep learning has become the state-of-the-art method in many machine learning tasks ranging from speech recognition to image segmentation. The success of deep networks in other domains has led to a new wave of applications in neuroscience. In this article, we review deep learning approaches to neural decoding. We describe the architectures used for extracting useful features from neural recording modalities ranging from spikes to functional magnetic resonance imaging. Furthermore, we explore how deep learning has been leveraged to predict common outputs including movement, speech and vision, with a focus on how pretrained deep networks can be incorporated as priors for complex decoding targets like acoustic speech or images. Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks, and we point out areas for future scientific development.

scholarly journals Remote Sensing Applications in Sugarcane Cultivation: A Review

2021 ◽  
Vol 13 (20) ◽  
pp. 4040
Author(s):  
Jaturong Som-ard ◽  
Clement Atzberger ◽  
Emma Izquierdo-Verdiguier ◽  
Francesco Vuolo ◽  
Markus Immitzer
Keyword(s):  
Remote Sensing ◽  
Large Body ◽  
Growth Conditions ◽  
Learning Approaches ◽  
Small Areas ◽  
Environmental Threats ◽  
Sugarcane Crop ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
Sentinel 2

A large number of studies have been published addressing sugarcane management and monitoring to increase productivity and production as well as to better understand landscape dynamics and environmental threats. Building on existing reviews which mainly focused on the crop’s spectral behavior, a comprehensive review is provided which considers the progress made using novel data analysis techniques and improved data sources. To complement the available reviews, and to make the large body of research more easily accessible for both researchers and practitioners, in this review (i) we summarized remote sensing applications from 1981 to 2020, (ii) discussed key strengths and weaknesses of remote sensing approaches in the sugarcane context, and (iii) described the challenges and opportunities for future earth observation (EO)-based sugarcane monitoring and management. More than one hundred scientific studies were assessed regarding sugarcane mapping (52 papers), crop growth anomaly detection (11 papers), health monitoring (14 papers), and yield estimation (30 papers). The articles demonstrate that decametric satellite sensors such as Landsat and Sentinel-2 enable a reliable, cost-efficient, and timely mapping and monitoring of sugarcane by overcoming the ground sampling distance (GSD)-related limitations of coarser hectometric resolution data, while offering rich spectral information in the frequently recorded data. The Sentinel-2 constellation in particular provides fine spatial resolution at 10 m and high revisit frequency to support sugarcane management and other applications over large areas. For very small areas, and in particular for up-scaling and calibration purposes, unmanned aerial vehicles (UAV) are also useful. Multi-temporal and multi-source data, together with powerful machine learning approaches such as the random forest (RF) algorithm, are key to providing efficient monitoring and mapping of sugarcane growth, health, and yield. A number of difficulties for sugarcane monitoring and mapping were identified that are also well known for other crops. Those difficulties relate mainly to the often (i) time consuming pre-processing of optical time series to cope with atmospheric perturbations and cloud coverage, (ii) the still important lack of analysis-ready-data (ARD), (iii) the diversity of environmental and growth conditions—even for a given country—under which sugarcane is grown, superimposing non-crop related radiometric information on the observed sugarcane crop, and (iv) the general ill-posedness of retrieval and classification approaches which adds ambiguity to the derived information.

scholarly journals Edge Loss for Remote Sensing Image Super-Resolution

2021 ◽  
Author(s):  
Jiaoyue Li ◽  
Weifeng Liu ◽  
Kai Zhang ◽  
Baodi Liu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Super Resolution ◽  
Ground Truth ◽  
Remote Sensing Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Sensing Applications ◽  
Image Super Resolution ◽  
Edge Based

Remote sensing image super-resolution (SR) plays an essential role in many remote sensing applications. Recently, remote sensing image super-resolution methods based on deep learning have shown remarkable performance. However, directly utilizing the deep learning methods becomes helpless to recover the remote sensing images with a large number of complex objectives or scene. So we propose an edge-based dense connection generative adversarial network (SREDGAN), which minimizes the edge differences between the generated image and its corresponding ground truth. Experimental results on NWPU-VHR-10 and UCAS-AOD datasets demonstrate that our method improves 1.92 and 0.045 in PSNR and SSIM compared with SRGAN, respectively.

scholarly journals Recognition of Image-Based Plant Leaf Diseases Using Deep Learning Classification Models

2021 ◽  
Vol 20 (5) ◽  
Author(s):  
Sakshi Takkar ◽  
Anuj Kakran ◽  
Veerpal Kaur ◽  
Manik Rakhra ◽  
Manish Sharma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Agricultural Land ◽  
Super Resolution ◽  
Modern Technology ◽  
Primary Objective ◽  
Plant Diseases ◽  
Learning Approaches ◽  
Agricultural Field ◽  
Detection And Identification ◽  
The Individual

Plant diseases are spread by a variety of pests, weeds, and pathogens and may have a devastating effect on agriculture, if not handled in a timely manner. Farmers face umpteen challenges from a proper water supply, untimely rain, storage facilities, and several plant diseases. Crops disease is the primary threat and it causes enormous loss to farmers in terms of production and finance. Identifying the disease from several hectares of agricultural land is a very difficult practice even with the presence of modern technology. Accurate and rapid illness prediction for early illness treatment to crops minimizes economical loss to the individual and further proves to be productive for healthy crops. Many studies use modern deep learning approaches to improve the accuracy and performance of object detection and identification systems. The suggested method notifies farmers of different agricultural illnesses, prompting them to take further essential precautions before the disease spreads to the whole agricultural field. The primary objective of this study is to detect the illnesses as soon as they begin to spread on the leaves of the plants. Super-Resolution Convolutional Neural Network (SRCNN) and Bicubic models are employed in the system to identify healthy and diseased leaves with an accuracy of 99.175 % and 99.156 % respectively.

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