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The WIPI Model Based on Multi-Scale Local Contrast Post-Processing for Infrared Small Target Detection Can. J. Remote Sens. (IF 2.6) Pub Date : 2024-03-05 Juan Chen, Lin Qiu, Zhencai Zhu, Ning Sun, Hao Huang, Wai-Hung Ip, Kai-Leung Yung
According to the infrared patch image (IPI) model theory, the infrared image background has a low rank and the target is sparse. The low-rank model can be used to separate the background and identi...
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Human Impact Land Use Layer from the Canadian Forest Service National Forest Inventory Can. J. Remote Sens. (IF 2.6) Pub Date : 2024-02-22 David A. Hill
The Human Impact Land Use Layer (HILL) dataset was developed with visual interpretation of high-resolution satellite imagery. 26,388 2 × 2 km photo plots from the Canadian National Forest Inventory...
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3D Modeling of Façade Elements Using Multi-View Images from Mobile Scanning Systems Can. J. Remote Sens. (IF 2.6) Pub Date : 2024-02-21 Abbas Salehitangrizi, Shabnam Jabari, Michael Sheng, Yun Zhang
There is a growing demand for detailed building façade models (Level-of-Detail 3: LoD 3) in a variety of applications. Despite the increasing number of papers addressing this issue in the literatur...
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Comparison of Machine Learning Inversion Methods for Salinity in the Central Indian Ocean Based on SMOS Satellite Data Can. J. Remote Sens. (IF 2.6) Pub Date : 2024-02-08 Ziyi Gong, Hongchang He, Donglin Fan, You Zeng, Zhenhao Liu, Bozhi Pan
In this paper, the central Indian Ocean (60°–95°E, 0°–37°S) has been selected as the research area, and Argo salinity data are used as the measured values. The Catboost algorithm is introduced for ...
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Estimating Tree Diameter at Breast Height (DBH) Using iPad Pro LiDAR Sensor in Boreal Forests Can. J. Remote Sens. (IF 2.6) Pub Date : 2024-01-18 Matthew Guenther, Muditha K. Heenkenda, Brigitte Leblon, Dave Morris, Jason Freeburn
Traditional Diameter at Breast Height (DBH) mensuration is labor-intensive and costly. This scoping study explored the possibility of using the Apple iPad Pro Light Detection And Ranging (LiDAR) se...
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Dense Connected Edge Feature Enhancement Network for Building Edge Detection from High Resolution Remote Sensing Imagery Can. J. Remote Sens. (IF 2.6) Pub Date : 2024-01-16 Xueyan Dong, Jiannong Cao, Weiheng Zhao
Deep-learning-based methods for building-edge-detection have been widely researched and applied in the field of image processing. However, these methods often emphasis the analysis of deep features...
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Synthetic Images for Georeferencing Camera Images in Mobile Mapping Point-clouds Can. J. Remote Sens. (IF 2.6) Pub Date : 2024-01-16 Kent Jones, Derek D. Lichti, Robert Radovanovic
Accurate three-dimensional mapping and digital twinning provides a powerful tool for effective maintenance of civil infrastructure and supports efficient future planning of new developments. Three-...
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Comprehensive Landsat-Based Analysis of Long-Term Surface Water Dynamics over Wetlands and Waterbodies in North America Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-12-21 Mohammadali Hemati, Masoud Mahdianpari, Hodjat Shiri, Fariba Mohammadimanesh
Wetlands are considered one of the most valuable ecosystems around the world and provide numerous environmental services, including water purification, flood protection, and habitat for a variety o...
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Terrestrial Snowmelt as a Precursor to Landfast Sea Ice Break-up in Hudson Bay and James Bay Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-12-07 Kaushik Gupta, Jens K. Ehn
Numerous studies have been conducted to enhance our understanding of how climate change impacts landfast ice and its break-up in spring or summer. Yet, predictions of break-up timing have proven el...
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From Stationary to Mobile: Unleashing the Full Potential of Terrestrial LiDAR through Sensor Integration Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-11-27 Hamdy Elsayed, Ahmed Shaker
This paper discusses a comprehensive methodology for transforming a static LiDAR (Light Detection and Ranging) system into a mobile mapping system. The initial step involves integrating various sen...
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Melt Season Arctic Sea Ice Type Separability Using Fully and Compact Polarimetric C- and L-Band Synthetic Aperture Radar Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-10-31 Aikaterini Tavri, Randall Scharien, Torsten Geldsetzer
Sea ice mapping using Synthetic Aperture Radar (SAR) in the melt season poses challenges, due to wet snow and melt ponds complicating sea ice type separability. To address this, we analyzed fully p...
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Object-Based Image Analysis (OBIA) and Machine Learning (ML) Applied to Tropical Forest Mapping Using Sentinel-2 Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-10-16 Clovis Cechim Junior, Hideo Araki, Rodrigo de Campos Macedo
The purpose of this research was to distinguish and estimate natural forest areas at Paraná, Brazil. Forest plantations (Silviculture) and natural forests have high annual vegetative vigor, as well...
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Radarsat Constellation Mission Derived Winter Glacier Velocities for the St. Elias Icefield, Yukon/Alaska: 2022 and 2023 Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-10-10 Wesley Van Wychen, Courtney Bayer, Luke Copland, Erika Brummell, Christine Dow
Here we use high resolution (5 m) Radarsat Constellation Mission (RCM) imagery acquired in winters 2022 and 2023 to determine motion across glaciers of the St. Elias Icefield in Yukon/Alaska. Our r...
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Using Remote Sensing to Address Green Heritage of the City of Marrakech, Morocco Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-10-04 Karima Mazirh, Said El Goumi, Mounsif Ibnoussina, Omar Witam, Mohamed Nocairi, Rachida Kasimi, Salah Er-Raki
Climate change and rapid urbanization have significant impact on green spaces and natural resources in African countries. To investigate this impact in the city of Marrakech, this study develops re...
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Deformation Retrievals for North America and Eurasia from Sentinel-1 DInSAR: Big Data Approach, Processing Methodology and Challenges Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-09-27 Sergey V. Samsonov, Wanpeng Feng
A fully automated processing system for measuring long-term ground deformation time series and deformation rates frame-by-frame using DInSAR processing technique was developed at the Canada Center ...
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Multi-Source Remote Sensing Based Modeling of Vegetation Productivity in the Boreal: Issues & Opportunities Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-09-19 Ramon Melser, Nicholas C. Coops, Michael A. Wulder, Chris Derksen
Understanding the processes driving terrestrial vegetation productivity dynamics in boreal ecosystems is critical for accurate assessments of carbon dynamics. Monitoring these dynamics typically re...
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A Novel Classification Method for PolSAR Image Combining the Deep Learning Model and Adaptive Boosting of Shallow Classifiers Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-09-15 Yan Duan, Shaojie Bai, Limin Liu, Guangwei Wang
Abstract Polarimetric synthetic aperture radar (PolSAR) images are classified mainly according to the backscattering information of ground objects. For regions with complex backscattering information, misclassification is easy to occur, which leads to challenges in improving the classification accuracy of the PolSAR image. Given this situation, this paper combines the Deep Learning Model and traditional
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An Algorithmic Approach towards Remote Sensing Imagery Data Restoration Using Guided Filters in Real-Time Applications Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-09-15 Prabhishek Singh, Manoj Diwakar, Debjani Ghosh, Ankit Vidyarthi, Deepak Gupta, Punit Gupta
The images captured from SAR sensors are inherently weakened by speckle noise. The SAR image processing community targeted this problem with many feature-based filters. Since SAR images are low-con...
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Assessing the Parameterization of RADARSAT-2 Dual-polarized ScanSAR Scenes on the Accuracy of a Convolutional Neural Network for Sea Ice Classification: Case Study over Coronation Gulf, Canada Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-09-12 Benoit Montpetit, Benjamin Deschamps, Joshua King, Jason Duffe
Abstract Arctic amplification has many impacts on sea-ice extent, thickness, and flux. It becomes critical to monitor sea-ice conditions at finer spatio-temporal resolution. We used a simple convolutional neural network (CNN) on the RADARSAT-2 dual-polarized ScanSAR wide archive available over Coronation Gulf, Canada, to assess which SAR parameter improves model performances to classify sea ice from
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Multiscale Cascaded Network for the Semantic Segmentation of High-Resolution Remote Sensing Images Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-09-11 Xiaolu Zhang, Zhaoshun Wang, Anlei Wei
Abstract As remote sensing images have complex backgrounds and varying object sizes, their semantic segmentation is challenging. This study proposes a multiscale cascaded network (MSCNet) for semantic segmentation. The resolutions employed with respect to the input remote sensing images are 1, 1/2, and 1/4, which represent high, medium, and low resolutions. First, 3 backbone networks extract features
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Assessing the Performance of Satellite-Based Models for Crop Yield Estimation in the Canadian Prairies Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-09-05 Jumi Gogoi, Nathaniel K. Newlands, Zia Mehrabi, Nicholas C. Coops, Navin Ramankutty
Abstract Timely monitoring of crop production using a remote sensing-based approach offers promise toward enhancing food security. Statistical models developed using satellite data typically employ a single vegetation index from a single sensor for yield estimation. With the increasing availability of satellite datasets, there is now an opportunity to investigate the potential of available vegetation
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Novel Approach to Wind Retrieval from Sentinel-1 SAR in Tropical Cyclones Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-09-05 Xianbin Zhao, Weizeng Shao, Mengyu Hao, Xingwei Jiang
Abstract The strong winds in tropical cyclones (TCs) are commonly retrieved from cross-polarized SAR images using a geophysical model function (GMF). However, the accuracy of wind retrieval in cross-polarization is significantly reduced at the edges of sub-swaths. In this study, a novel approach to TC wind retrieval from VV polarized SAR images is proposed based on using the azimuthal cutoff wavelength
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Spectral–Spatial Features Exploitation Using Lightweight HResNeXt Model for Hyperspectral Image Classification Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-09-04 Dhirendra Prasad Yadav, Deepak Kumar, Anand Singh Jalal, Ankit Kumar, Surbhi Bhatia Khan, Thippa Reddy Gadekallu, Arwa Mashat, Areej A. Malibari
Abstract Hyperspectral image classification is vital for various remote sensing applications; however, it remains challenging due to the complex and high-dimensional nature of hyperspectral data. This paper introduces a novel approach to address this challenge by leveraging spectral and spatial features through a lightweight HResNeXt model. The proposed model is designed to overcome the limitations
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Hyperspectral Image Classification Based on the Gabor Feature with Correlation Information Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-08-25 Jianshang Liao, Liguo Wang, Genping Zhao
Abstract Gabor filter is widely used to extract spatial texture features of hyperspectral images (HSI) for HSI classification; however, a single Gabor filter cannot obtain the complete image features. In the paper, we propose an HSI classification method that combines the Gabor filter (GF) and domain-transformation standard convolution (DTNC) filter. First, we use the Gabor filter to extract spatial
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Black and Odorous Water Detection of Remote Sensing Images Based on Improved Deep Learning Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-08-11 Jianjun Huang, Jindong Xu, Qianpeng Chong, Ziyi Li
Abstract Black and odorous water seriously affects the ecological balance of rivers and the health of people living nearby. Satellite remote sensing technology with its advantages of a large range, long-time series, low cost, and high efficiency, has provided a new area for water quality detection. Much archived remote sensing satellite data can be further processed and used as a data source for black
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The Evolution of Remote Sensing Education in Canada’s Universities and Colleges: Decades of Innovation and Expansion Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-07-26 Ellsworth LeDrew, Robert Ryerson
Abstract During the rapid development of remote sensing technology and applications in the 1970’s in Canada, the Canadian Advisory Committee on Remote Sensing conducted a nation-wide review of relevant activities in post-secondary teaching and research. This was updated in the 1980’s. Similar reviews were solicited for the radar community in 2009 by the Canadian Space Agency and for the Geospatial
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Large-Scale LoD2 Building Modeling using Deep Multimodal Feature Fusion Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-07-24 Faezeh Soleimani Vostikolaei, Shabnam Jabari
Abstract In today’s rapidly urbanizing world, accurate 3D city models are crucial for sustainable urban management. The existing technology for 3D city modeling still relies on an extensive amount of manual work and the provided solutions may vary depending on the urban structure of different places. According to the CityGML3 standard of 3D city modeling, in LoD2, the roof structures need to be modeled
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Observations of Thin First Year Sea Ice Using a Suite of Surface Radar, LiDAR, and Drone Sensors Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-07-06 Dustin Isleifson, Madison L. Harasyn, David Landry, David Babb, Elvis Asihene
Abstract Arctic sea ice is rapidly transitioning into a perennial first year ice pack and this is being observed with satellite remote sensing. Satellite image interpretation requires accurate knowledge of the physical conditions and how they give rise to the microwave scattering response that is present within a single image pixel. This study addresses this issue through a focused remote sensing study
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Comparative Analysis of Empirical and Machine Learning Models for Chla Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-06-06 Amir M. Chegoonian, Nima Pahlevan, Kiana Zolfaghari, Peter R. Leavitt, John-Mark Davies, Helen M. Baulch, Claude R. Duguay
Abstract Remote retrieval of near-surface chlorophyll-a (Chla) concentration in small inland waters is challenging due to substantial optical interferences of various water constituents and uncertainties in the atmospheric correction (AC) process. Although various algorithms have been developed to estimate Chla from moderate-resolution terrestrial missions (∼10–60 m), the production of both accurate
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Characterizing Tree Species in Northern Boreal Forests Using Multiple-Endmember Spectral Mixture Analysis and Multi-Temporal Satellite Imagery Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-06-01 Jurjen Van der Sluijs, Derek R. Peddle, Ronald J. Hall
Abstract Northern boreal forests are characterized by open stands whereby trees, understory background, and shadow are all significant components of the spectral response within a pixels’ spatial footprint. To overcome this mixed pixel problem, accurate spectral characterization of these (endmember) components is necessary for spectral mixture analysis (SMA) to generate forest classifications at the
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Passive Microwave Sea Ice Edge Displacement Error over the Eastern Canadian Arctic for the period 2013-2021 Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-05-26 Armina Soleymani, Nastaran Saberi, K. Andrea Scott
Abstract In this study, sea ice edge derived from three passive microwave (PM) algorithms, ARTIST sea ice (ASI), enhanced NASA Team 2 (NT2), and Bootstrap (BT), are compared to those derived from the daily Canadian Ice Service charts over a primarily seasonal ice zone in the eastern Canadian Arctic for 2013–2021. To determine the ice edge error, we introduced an edge-length-based displacement measure
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UAV-SfM and Geographic Object-Based Image Analysis for Measuring Multi-Temporal Planimetric and Volumetric Erosion of Arctic Coasts Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-05-18 Andrew Clark, Brian J. Moorman, Dustin Whalen
Abstract Monitoring and quantifying the rapid changes along Arctic coasts is becoming increasingly important as above average warming in the Arctic is contributing to increasing rates of erosion leading to dramatic impacts on coastal ecosystems and communities. Understanding the impacts of Arctic coastal erosion on the climate system across large coastal scales requires improvements in measurement
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Attributing a Causal Agent and Assessing the Severity of Non-Stand Replacing Disturbances in a Northern Hardwood Forest using Landsat-Derived Vegetation Indices Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-04-10 Alexandre Morin-Bernard, Alexis Achim, Nicholas C. Coops
Abstract Non-stand-replacing disturbances are major drivers of northern hardwood forest dynamics, but are more challenging to characterize using satellite imagery than stand-replacing events. This study proposes a hurdle approach in which disturbance causal agents are first attributed to permanent sample plots that were either partially harvested, had sustained damage from an ice storm or remained
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Sensitivity Analysis of Parameters of U-Net Model for Semantic Segmentation of Silt Storage Dams from Remote Sensing Images Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-03-06 Jingwei Hou, Bo Hou, Moyan Zhu, Ji Zhou, Qiong Tian
Abstract Building silt storage dams is an important measure to control soil erosion. Sensitivity analysis of the parameters in a deep learning model is the premise of extracting high-precision silt storage dams from high-resolution remote sensing (RS) images. In this study, watershed features of Hulu River and Lanni River in the Loess Plateau, China, are extracted using a geographic information system
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Water Bottom and Surface Classification Algorithm for Bathymetric LiDAR Point Clouds of Very Shallow Waters Can. J. Remote Sens. (IF 2.6) Pub Date : 2023-02-15 Hyejin Kim, Jaehoon Jung, Jaebin Lee, Gwangjae Wie
Abstract The absence of accurate point classification limits the effective use of airborne bathymetric LiDAR (ABL) data for coastal zone mapping. In this study, we propose a classification approach using a custom waveform decomposition technique with the pseudo-waveform generated from ABL point cloud data. Initially, the input point clouds were organized into a 2D grid. Next, the points that fall into
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Automated Forest Harvest Detection With a Normalized PlanetScope Imagery Time Series Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-12-19 Levi Keay, Christopher Mulverhill, Nicholas C. Coops, Grant McCartney
Abstract The advent of CubeSat constellations is revolutionizing the ability to observe Earth systems through time. The improved spatial and temporal resolutions from these data could assist in tracking forest harvesting by forest management companies or government organizations interested in monitoring the sustainable management of forest resources. However, differing characteristics of individual
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A New U-Net Based Convolutional Neural Network for Estimating Caribou Lichen Ground Cover from Field-Level RGB Images Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-11-30 Julie Lovitt, Galen Richardson, Krishan Rajaratnam, Wenjun Chen, Sylvain G. Leblanc, Liming He, Scott E. Nielsen, Ashley Hillman, Isabelle Schmelzer, André Arsenault
Abstract High-quality ground-truth data are critical for developing reliable Earth Observation (EO) based geospatial products. Conventional methods of collecting these data are either subject to an unknown amount of human error and bias or require extended time in the field to complete (i.e., point-intercept assessments). Digital photograph classification (DPC) may address these drawbacks. In this
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Multi-Source Mapping of Forest Susceptibility to Spruce Budworm Defoliation Based on Stand Age and Composition across a Complex Landscape in Maine, USA Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-11-16 Rajeev Bhattarai, Parinaz Rahimzadeh-Bajgiran, Aaron Weiskittel
Abstract Spruce budworm (Choristoneura fumiferana; SBW) outbreaks in the northeastern USA and Canada are recurring phenomena leading to large-scale mortality of spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.) forests as susceptibility to SBW is primarily determined by the availability of host species and their maturity. Our study examined several satellite remote sensing (Sentinel-1 C-band
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Advances in Hyperspectral Remote Sensing for Earth Monitoring and Mapping Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-10-30 Gautam Srivastava, K. Shankar
Published in Canadian Journal of Remote Sensing: Journal canadien de télédétection (Vol. 48, No. 5, 2022)
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Toward Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-10-20 Jørgen A. Agersborg, Luigi T. Luppino, Stian Normann Anfinsen, Jane Uhd Jepsen
Abstract Several generic methods have recently been developed for change detection in heterogeneous remote sensing data, such as images from synthetic aperture radar (SAR) and multispectral radiometers. However, these are not well-suited to detect weak signatures of certain disturbances of ecological systems. To resolve this problem we propose a new approach based on image-to-image translation and
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Multifractal and Long-Term Memory of Impervious Surface Spatial Patterns in a Coastal City in China Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-10-20 Qin Nie, Kai Shi, Xuewen Wu
Abstract An understanding of the multifractal and long-range dependence of impervious surfaces (IS) spatiotemporal patterns is helpful for regional environmental assessment and urban planning. Linear spectral-mixture analysis has been applied to compute IS in the coastal city of Xiamen, China, based on Landsat TM/OLI/TIRS images, and then the long-term trends and multifractal characteristics of IS
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Mapping Dominant Boreal Tree Species Groups by Combining Area-Based and Individual Tree Crown LiDAR Metrics with Sentinel-2 Data Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-10-13 Martin Queinnec, Nicholas C. Coops, Joanne C. White, Verena C. Griess, Naomi B. Schwartz, Grant McCartney
Abstract Airborne light detection and ranging (LiDAR) data are increasingly used to inform sustainable forest management practices. Information about species composition is needed for a range of applications; however, commonly used area-based summaries of LiDAR data are limited to accurately differentiate tree species. The objective of this study was to map dominant species groups across a large (>580
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Monitoring Crops Using Compact Polarimetry and the RADARSAT Constellation Mission Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-09-26 Laura Dingle Robertson, Heather McNairn, Xianfeng Jiao, Connor McNairn, Samuel O. Ihuoma
Abstract The RADARSAT Constellation Mission (RCM) can acquire imagery in Compact Polarimetric (CP) mode. With this new mode, and the increased revisit with three satellites, RCM can contribute to operational crop monitoring at national scales. The four Stokes (S0, S1, S2 and S3) and three m-chi decomposition (surface, double bounce, volume) parameters were used to identify crops (pasture/forage, barley
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Optimal and Fully Connected Deep Neural Networks Based Classification Model for Unmanned Aerial Vehicle Using Hyperspectral Remote Sensing Images Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-09-21 Manar Ahmed Hamza, Jaber S. Alzahrani, Amal Al-Rasheed, Reem Alshahrani, Mohammad Alamgeer, Abdelwahed Motwakel, Ishfaq Yaseen, Mohamed I. Eldesouki
Abstract Unmanned Aerial Vehicle (UAV) is treated as an effective technique for gathering high resolution aerial images. The UAV based aerial image collection is highly preferred due to its inexpensive and effective nature. However, automatic classification of aerial images poses a major challenging issue in the design of UAV, which could be handled by the deep learning (DL) models. This study designs
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Hyperspectral Image Classification Based on Novel Hybridization of Spatial-Spectral-Superpixelwise Principal Component Analysis and Dense 2D-3D Convolutional Neural Network Fusion Architecture Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-09-19 Debaleena Datta, Pradeep Kumar Mallick, Deepak Gupta, Gyoo-Soo Chae
Abstract We propose a hybridized technique named Spatial-Spectral-Superpixelwise PCA-based Dense 2D-3D CNN Fusion Architecture (3SPCA-D-2D-3D-CNN), that deals with the detailed and complex study of dimensionality reduction and classification of Hyperspectal images (HSI). Our work is 2-fold: At first (1), 3SPCA is applied on raw HSI that adopts superpixels-based local reconstruction to filter the images
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Soil Moisture and Soil Depth Retrieval Using the Coupled Phase-Amplitude Behavior of C-Band Radar Backscatter in the Presence of Sub-Surface Scattering Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-09-15 Keith Morrison, Wolfgang Wagner
Abstract In low-moisture regimes, strongly-reflecting bedrock underlying soil could provide a dominant return. This offers a novel opportunity to retrieve both the volumetric moisture fraction (mv) and depth (d) of a soil layer using a differential phase. A radar wave traversing the overlying soil slows in response to moisture state; moisture dynamics are thus recorded as variations in travel time—captured
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Ship Detection in SAR Images via Cross-Attention Mechanism Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-09-14 Yilong Lv, Min Li
Abstract Deep learning has been widely applied to ship detection in Synthetic Aperture Radar (SAR) images. Unlike optical images, the current object detection methods have the problem of weak feature representation due to the low object resolution in SAR images. In addition, disturbed by chaotic noise, the features of classification and location are prone to significant differences, resulting in classification
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SAR Polarimetric Phase Differences in Wetlands: Information and Mis-Information Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-09-06 F. J. Ahern, B Brisco, M. J. Battaglia, L. Bourgeau-Chavez, D. Atwood, K. Murnaghan
Abstract We have previously reported anomalous polarimetric decomposition results from SAR observations of wetlands. This is caused by the abrupt change in the phase difference between the HH and VV backscatter that occurs around the Brewster angle of the emergent vegetation. We have now developed and implemented a model for backscattering from wetlands that features a cylinder emergent from a water
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Greenhouses Detection in Guanzhong Plain, Shaanxi, China: Evaluation of Four Classification Methods in Google Earth Engine Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-09-06 Caihong Gao, Qifan Wu, Miles Dyck, Lei Fang, Hailong He
Abstract Greenhouses used for agricultural production have been expanding around the world because it significantly increases crop yield. Meanwhile, it brings a series of environmental problems that should be considered in agricultural planning and management. The advent of the Google Earth Engine (GEE) cloud platform makes remote sensing image processing more convenient and efficient. It has been
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Hyperspectral Imagery Denoising Using Minimum Noise Fraction and Video Non-Local Bayes Algorithms Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-09-06 Guang Yi Chen, Adam Krzyzak, Shen-En Qian
Abstract Hyperspectral imagery (HSI) denoising is a popular research topic in remote sensing. In this paper, we propose a novel method for HSI denoising by performing Minimum Noise Fraction (MNF) to the original HSI data cube, thresholding the noisy output bands with the Video Non-Local Bayes (VNLB) algorithm, and then conducting the inverse MNF transform to obtain the denoised data cube. Our experiments
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Temporal Variation in Surface Bidirectional Reflectance of the Railroad Valley Vicarious Calibration Test Site in Nevada Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-09-05 Nicole Byford, Craig A. Coburn
Abstract Spectral reflectance-based vicarious calibration (VicCal) requires accurate characterization of the bidirectional reflectance distribution function (BRDF) of the ground-based target. Railroad Valley (RRV) Playa, Nevada, has been used as a VicCal test site since 1995 as it is large, appears stable over time, and has a reasonably consistent surface. This study presents the results of a diurnal
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Downscaling CLDAS Soil Moisture Product by Integrating Sentinel-1 and Sentinel-2 Data over Agricultural Area Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-08-25 Hongzhang Ma, Shuyi Sun, Zhaowei Wang, Yandi Jiang, Sumei Liu
Abstract Soil Moisture (SM) plays a key role in the energy exchange between the atmosphere and the land surface. Most of the SM products retrieved from satellite remote sensing data are not suitable for drought monitoring and irrigation management in smart agriculture applications due to their coarse spatial resolution. We propose an SM downscaling method named Water Cloud Change Detection (WCCD) that
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Shallow Water Bathymetry Retrieval by Optical Remote Sensing Based on Depth-Invariant Index and Location Features Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-08-19 Jinshan Zhu, Fei Yin, Jian Qin, Jiawei Qi, Zhaoyu Ren, Peng Hu, Jingyu Zhang, Xueqing Zhang, Ruifu Wang
Abstract At present, most machine learning bathymetry retrieval models use the band reflectance as the inversion feature only, without considering features related to the water substrate and pixel spatial correlation. In this study, in addition to band reflectance, two features, Depth-Invariant Index (DII) and pixel location, are taken into account. Two machine learning algorithms, Random Forest (RF)
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A Method for Fully Automatic Building Footprint Extraction From Remote Sensing Images Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-08-08 John Xiong, Ting Chen, Minjie Wang, Jianjun He, Lanying Wang, Zhiyong Wang
Abstract Automatically mapping building footprints has a wide range of applications in many fields. In recent years, the automatic building extraction methods based on deep learning show an absolute advantage over the traditional image segmentation methods due to its high speed and high precision. However, the building footprint extracted by deep learning is just an irregular building mask. There is
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Monitoring Water Turbidity in a Temperate Floodplain Using UAV: Potential and Challenges Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-08-08 Savannah Bussières, Christophe Kinnard, Maxime Clermont, Stéphane Campeau, Daphney Dubé-Richard, Pierre-André Bordeleau, Alexandre Roy
Abstract The Lake Saint-Pierre (LSP) is a wide (≈300 km2) and shallow (≈3 m) lake created through a widening of the St. Lawrence River. Each spring, freshet makes it the largest floodplain in the province of Quebec. Agricultural practices in the littoral increase the water turbidity, which deteriorate the habitat’s quality of many fish species. However, measuring spatio-temporal turbidity patterns
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Road Boundary, Curb and Surface Extraction from 3D Mobile LiDAR Point Clouds in Urban Environment Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-07-29 Na Wang, Zhenwei Shi, Zhaoxu Zhang
Abstract According to the spatial structure characteristics of road curbs and road surfaces, a robust method for automatic extraction of road boundaries, road curbs and road surfaces was proposed using mobile laser scanning (MLS) point cloud data. Firstly, ground filtering was performed to separate ground points and non-ground points according to the angle between the normal vector of the point cloud
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Estimating Biophysical Parameters of Native Grasslands Using Spectral Data Derived from Close Range Hyperspectral and Satellite Data Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-06-29 Thiago Frank, Anne Smith, Bill Houston, Xiaohui Yang, Xulin Guo
Abstract Estimating biophysical parameters of native grassland enables management changes that affect ecological processes and economic benefits. Although multiple hyperspectral studies were focused on native grasslands, just a few compare data at different scales and among ecoregions. In this study, we compared data collected at different spectral and spatial scales and among Canadian Prairie ecoregions
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Intelligent Rider Optimization Algorithm with Deep Learning Enabled Hyperspectral Remote Sensing Imaging Classification Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-06-27 Ashit Kumar Dutta, Majed Alsanea, Basit Qureshi, Faisal Yousef Alghayadh, Abdul Rahaman Wahab Sait
Abstract Hyperspectral imaging (HSI) can be attained by the use of high resolution optical sensors and it comprises several spectral bands of the identical remote sensing target and is treated as a three-dimension (3D) dataset. Recently, deep learning (DL) techniques are gained important attention among research communities for image classification. In this aspect, this study develops an intelligent
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Intelligent Sine Cosine Optimization with Deep Transfer Learning Based Crops Type Classification Using Hyperspectral Images Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-06-22 José Escorcia-Gutierrez, Margarita Gamarra, Melitsa Torres-Torres, Natasha Madera, Juan C. Calabria-Sarmiento, Romany F. Mansour
Abstract Hyperspectral Remote Sensing (HRS) is an emergent, multidisciplinary paradigm with several applications, which are developed on the basis of material spectroscopy, radiative transfer, and imaging spectroscopy. HRS plays a vital role in agriculture for crops type classification and soil prediction. The recently developed artificial intelligence techniques can be used for crops type classification
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An Improved Bald Eagle Search Algorithm with Deep Learning Model for Forest Fire Detection Using Hyperspectral Remote Sensing Images Can. J. Remote Sens. (IF 2.6) Pub Date : 2022-06-16 Abeer D. Algarni, Nazik Alturki, Naglaa F. Soliman, Sayed Abdel-Khalek, Abd Allah A. Mousa
Abstract This paper presents an improved Bald Eagle Search Algorithm with Deep Learning model for forest fire detection (IBESDL-FFD) technique using hyperspectral images (HSRS). The major intention of the IBESDL-FFD technique is to identify the presence of forest fire in the HSRS images. To achieve this, the IBESDL-FFD technique involves data pre-processing in two stages namely data augmentation and