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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Molin, Ricardo D., Jr. Rosa, Rafael A. S. Bayer, Fabio M. Pettersson, Mats I. Machado, Renato ITA BR-12228900 Sao Jose Dos Campos SP Brazil Visiona Tecnol Espacial SA BR-12247016 Sao Jose Dos Campos SP Brazil Univ Fed Santa Maria BR-97105900 Santa Maria RS Brazil Blekinge Inst Technol BTH SE-37179 Karlskrona Sweden

ISBN: (纸本)9781538691540

This paper presents an incoherent change detection algorithm (CDA) for synthetic aperture radar (SAR) images based on logistic regression. The input data consists of a set of 24 SAR images acquired in a test site in northern Sweden [1]. Subsets of these images are trained based on pixel amplitude, flight heading and neighboring features such as local mean, standard deviation and skewness. The proposed method intends to explore the advantadges from both pixel- and object-based approaches, while evaluating multiple features in amplitude only SAR images. Preliminary results based on K-fold cross validation have shown that the proposed CDA achieves good performance when compared to the results presented in [1].

关键词： change detection algorithm Logistic Regression SAR images

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Surface Soil Moisture Estimation from Time Series of RADARSAT Constellation Mission Compact Polarimetric Data for the Identification of Water-Saturated Areas

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REMOTE SENSING 2024年第14期16卷 2664页

作者： Zakharov, Igor Kohlsmith, Sarah Hornung, Jon Charbonneau, Francois Bobby, Pradeep Howell, Mark C CORE Ottawa ON K2K 2A4 Canada Suncor Energy Serv Inc Calgary AB T2P 3E3 Canada Canada Ctr Mapping & Earth Observat CCMEO Canada Ctr Remote Sensing CCRS 580 Booth St Ottawa ON K1A 0E4 Canada C CORE St John NF A1B 3X5 Canada

Soil moisture is one of the main factors affecting microwave radar backscatter from the ground. While there are other factors that affect backscatter levels (for instance, surface roughness, vegetation, and incident angle), relative variations in soil moisture can be estimated using space-based, medium resolution, multi-temporal synthetic aperture radar (SAR). Understanding the distribution and identification of water-saturated areas using SAR soil moisture can be important for wetland mapping. The SAR soil moisture retrieval algorithm provides a relative assessment and requires calibration over wet and dry periods. In this work, relative soil moisture indicators are derived from a time series of the RADARSAT Constellation Mission (RCM) SAR compact polarimetric (CP) data over reclaimed areas of an oil sands mine in Alberta, Canada. An evaluation of the soil moisture product is performed using in situ measurements showing agreement from June to September. The surface scattering component of m-chi CP decomposition and the RL SAR products demonstrated a good agreement with the field data (low RMSE values and a perfect alignment with field-identified wetlands).

关键词： soil moisture wetland synthetic aperture radar (SAR) RCM change detection algorithm oil sands reclamation

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Testing the detection of large, secretive snakes using camera traps

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WILDLIFE SOCIETY BULLETIN 2023年第1期47卷

作者： Walkup, Danielle K. Ryberg, Wade A. Pierce, Josh B. Smith, Emlyn Childress, James East, Forrest Pierce, Brian L. Brown, Price Fielder, Corey M. Hibbitts, Toby J. Texas A&M Univ Nat Resources Inst AgriLife Water & Nat Resources Bldg1001 Holleman College Stn TX 77840 USA US Forest Serv USDA Southern Res Stn 506 Hayter St Nacogdoches TX 75965 USA US Forest Serv USDA 5325 Highway 8 Bentley LA 71407 USA 100 Renfro Dr Lufkin TX 75901 USA Texas A&M Univ Nat Resources Inst Dept Ecol & Conservat Biol Biodivers Res & Teaching Collect College Stn TX 77843 USA Texas A&M Univ Wildlife Fisheries & Ecol Sci Bldg2258 TAMU College Stn TX 77843 USA Texas A&M Univ Nat Resources Inst 1001 Holleman Dr East College Stn TX 77840 USA

Novel technologies, such as camera traps, have expanded the opportunities for species detection, especially for rare species. Corresponding changes in data processing must occur to handle the large volume of data gathered from technology like camera traps. Automated image data processing, usually by running images through different types of computer algorithms, is an overarching goal to reduce the number of images that researchers must manually review. However, differences in camera trap setups and species characteristics can make automatic processing a challenge. Here, we evaluated the detection accuracy and efficiency of a time-lapse triggered camera trapping technique combined with a pixel change detection algorithm as part of a monitoring program for a translocated population of the rare and federally threatened Louisiana Pinesnake (Pituophis ruthveni). We paired 5 cameras with automated pit tag readers to collect observations of P. ruthveni. We evaluated an image dataset of 1,500,187 images, collected over 7 months, both manually (i.e., researchers looking at each individual picture to determine snake presence) and automatically using a change detection algorithm. There were 18 P. ruthveni observations recorded by the tag readers, 7 of which occurred while a paired camera was not operational. Ten of the tag reader P. ruthveni observations were captured by the paired camera trap, with an additional P. ruthveni observation from a paired camera trap not recorded by the tag reader. There were 132 snake observations of 13 additional species and 18 observations of unknown snakes from the camera traps. The algorithm reduced the number of images reviewers evaluated by an average of 78.5% per camera (range = 37.3-98.7%) but had a 54.5% success rate at detecting observations of P. ruthveni (47.1% for individual images), and a slightly lower 48.9% success rate detecting other large snakes. Large snakes were 4 times more likely to be flagged by the algorithm than small sna

关键词： camera trap change detection algorithm conservation monitoring detection Louisiana Pinesnake Pituophis ruthveni

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Extracting Moving Objects More Accurately: A CDA Contour Optimizer

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第12期31卷 4840-4849页

作者： Gao, Fei Li, Yunyang Lu, Shufang Zhejiang Univ Technol Coll Comp Sci & Technol Lab Graph & Image Proc Hangzhou 310023 Peoples R China

In the area of change detection, there were a rare number of optimization methods. Most of the optimization methods that are used by change detection are morphological transformation or median filtering, which cannot best optimize change detection algorithm. In this paper, a general post-processing algorithm for change detection is proposed. We believe that some problems cannot be avoided in the area of change detection such as 1) region of moving object generated by change detection is slightly larger than the ground-truth and 2) there are always some disjoint and small regions that are independent from the moving objects. To address the problem, our method can optimize the change detection algorithm bases on the idea of edge detection, which can remove the wrong edge or pixel. In the experiments, more than 20 change detection algorithms that include the best algorithm in *** are selected. Most of these change detection algorithms are optimized by the proposed method on PWC, Precision, and FMeasure, where, our optimized algorithm named FgSegNet_v2 is better than all other algorithms in the CDnet. The best-optimized margin of PWC is 0.64, and the fast speed is 548FPS on CPU. Our approach can better resolve the afore-mentioned problems that cannot be avoided and is general and fast. The experiments can be reproduced with C++ on Github https://***/walty19950301/CDA-contour-optimizer.

关键词： change detection algorithms Image edge detection Feature extraction Unsupervised learning Supervised learning Semantics Optical filters Post-processing algorithm optimization method change detection algorithm edge detection motion detection

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Novel Piecewise Distance Based on Adaptive Region Key-Points Extraction for LCCD With VHR Remote-Sensing Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷

作者： Lv, Zhiyong Zhong, Pingdong Wang, Wei You, Zhenzhen Benediktsson, Jon Atli Shi, Cheng Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China State Key Lab Rail Transit Engn Informatizat FSDI Xian 710043 Peoples R China Univ Iceland Comp Engn IS-107 Reykjavik Iceland

Land cover change detection (LCCD) with very high-resolution remote-sensing images (VHR_RSIs) is important in observing surface change on Earth. However, pseudo-changes usually reduce the accuracy of the detection map. In this article, a novel piecewise distance based on adaptive region key-points extraction called sparse key-point distance (SKPD) is developed to measure the change magnitude between the bitemporal VHR_RSIs for LCCD. The proposed approach consists of three steps. First, an adaptive region generation algorithm is promoted for exploring spatial-contextual information. Then, the adaptive region around each pixel is sparsely represented with the box-whisker plot theory, and the adaptive region is converted into a sparse key-point vector. Finally, a piecewise distance is defined to measure the change magnitude between the bitemporal images. While the entire VHR_RSIs are scanned and the proposed SKPD method proceeds on a pixel-by-pixel basis, a change magnitude image (CMI) can be generated and a binary threshold method can be applied on the CMI to obtain a change detection map. Experimental results based on four pairs of real VHR_RSIs and four state-of-the-art methods effectively demonstrated the superiority of the proposed approach for achieving LCCD with VHR_RSIs, such as the improvements for the four datasets are 5.25%, 14.76%, 18.13%, and 22.24%, respectively, in terms of overall accuracy.

关键词： Data mining Shape Remote sensing Mathematical models Training Size measurement Shape measurement change detection algorithm feature extraction image recognition

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Retrieval of Sub-Kilometric Relative Surface Soil Moisture With Sentinel-1 Utilizing Different Backscatter Normalization Factors

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Maslanka, William Morrison, Keith White, Kevin Verhoef, Anne Clark, Joanna Univ Reading Dept Geog & Environm Sci Reading RG6 6AB Berks England Univ Reading Dept Meteorol Reading RG6 6AB Berks England

Spatiotemporal distribution of soil moisture is important for hydrometeorological and agricultural applications. There is growing interest in monitoring soil moisture in relation to soil- and land-based natural flood management (NFM), to understand the soil's ability, via land-use and management changes, and to delay the arrival of flood peaks in nearby watercourses. This article monitors relative surface soil moisture (rSSM) across the Thames Valley, U.K., using Sentinel-1 data, and the Vienna University of Technology (TU-Wien) change detection algorithm, with a novel exploration of monthly and annual normalization factors and spatial averaging. Two pairs of normalization factors are introduced to remove impacts from varying local incidence angles through direct and multiple regression slopes. The spatiotemporal distribution of rSSM values at various spatial resolutions (1000, 500, 250, and 100 m) is assessed. Comparisons with in situ soil moisture data from the COSMOS-UK network show that, while general temporal trends agree, the difference in effective depth of measurements, coupled with vegetation impacts during the growing season, makes comparison with soil moisture observations difficult. Temporal rSSM trends can be retrieved at spatial resolutions down to 100 m, and the rSSM RMSE was found to decrease as the spatial resolution increases. The vegetation effects upon the rSSM are further explored by comparing the two dominant land cover types: Arable and Horticulture, and Improved Grassland. It was found that, while the rSSM retrieval for these land covers was possible, and the general soil moisture trend is clear, overlying vegetation during the summer artificially increased the rSSM values.

关键词： Soil moisture Backscatter Radar Satellite broadcasting Moisture Remote sensing Vegetation mapping change detection algorithm River Thames Sentinel-1 soil moisture synthetic aperture radar (SAR)

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Retrieval of Surface Soil Moisture From Sentinel-1 Time Series for Reclamation of Wetland Sites

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2020年 13卷 3569-3578页

作者： Zakharov, Igor Kapfer, Mark Hornung, Jon Kohismith, Sarah Puestow, Thomas Howell, Mark Henschel, Michael D. C CORE Ottawa ON K2K 2A4 Canada Suncor Energy Serv Inc Calgary AB T2P 3E3 Canada C CORE St John NF A1B 3X5 Canada

Soil moisture is a key factor in the reclamation of wetland habitats. Understanding the distribution and relative amount of water can be critical in reintroducing trees and grasses to disturbed soils. Soil moisture is also one of the main factors affecting microwave radar backscatter from the ground;while there are other factors determining backscatter levels (for instance, surface roughness, vegetation, and incident angle), relative variations in soil moisture can be estimated using space-based, high resolution, multitemporal synthetic aperture radar (SAR). In this work, relative soil moisture indicators are derived from a time series of Sentinel-1 SAR data over previously mined oil sands in Alberta, Canada. The algorithm provides a relative assessment of soil moisture and requires calibration over wet and dry periods. An evaluation of the soil moisture product is validated using in situ measurements at multiple sites with observations showing agreement from May to August. Comparisons with precipitation records show that SAR derived surface soil moisture is influenced by discreet precipitation events;that is, rainfall that is coincident with the satellite observation reduces the effectiveness of the measurement. The resulting algorithm controls for rain events by including local weather records to adjust estimates based on the known precipitation.

关键词： change detection algorithm oil sands reclamation Sentinel-1 soil moisture synthetic aperture radar (SAR)

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Soil Moisture Estimation at 500m using Sentinel-1: application to Tunisian sites

Soil Moisture Estimation at 500m using Sentinel-1: applicati...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Foucras, Myriam Zribi, Mehrez Baghdadi, Nicolas INRAE CESBIO CNRS IRDUPSCNES F-31401 Toulouse 9 France Univ Montpellier UMR TETIS INRAE F-34093 Montpellier 5 France

ISBN: (纸本)9781728163741

This article aims to propose an estimation of the soil moisture at a spatial resolution of 0.5 km and 6 days temporal resolution using Sentinel-1 data. The proposed approach is based on the change detection technique applied over a series of four-year measurements. The algorithm output, a moisture index is between 0 and 1, 0 for the driest soils, 1 for the wettest soils. The methodology is tested on different sites in Central Tunisia. Soil moisture estimations are compared to soil moisture in-situ measurements. Results show a good correlation between estimation and true measurements, in particular for the African regions.

关键词： change detection algorithm Sentinel Soil moisture

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Soil Moisture Estimation at 500m using Sentinel-1: application to African sites 5

Soil Moisture Estimation at 500m using Sentinel-1: applicati...

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5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Foucras, Myriam Zribi, Mehrez Kallel, Abdelaziz IRD CNRS CESBIO UPSCNESINRAE F-31401 Toulouse 9 France CRNS Sfax Tunisia

ISBN: (纸本)9781728175133

This paper proposes a change detection approach for the estimation of soil water content, at a spatial resolution of 0.5 km and a temporal resolution of 6 days. The algorithm proposes a soil moisture index between 0 and 1. 0 corresponds to the driest context, 1 corresponds to the wettest context. The approach is being tested on different study sites with sentinel-1 radar data. Unlike the classic change detection approach, the algorithm takes into account the effects of land use, vegetation development and the seasonal context. Results show a good correlation between satellite estimations and true measurements for the African studied regions.

关键词： change detection algorithm Sentinel Soil moisture

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Passive/active microwave soil moisture change disaggregation using SMAPVEX12 data

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JOURNAL OF HYDROLOGY 2019年 574卷 1085-1098页

作者： Fang, Bin Lakshmi, Venkat Jackson, Thomas J. Bindlish, Rajat Colliander, Andreas Univ Virginia Dept Engn Syst & Environm Charlottesville VA 22904 USA ARS USDA Beltsville MD 20705 USA NASA Goddard Space Flight Ctr Greenbelt MD 20771 USA CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91109 USA

The SMAPVEX12 (Soil Moisture Active Passive (SMAP) Validation Experiment 2012) experiment was conducted during June-July 2012 in Manitoba, Canada with the goal of collecting remote sensing data and ground measurements for the development and testing of soil moisture retrieval algorithms under varying vegetation and soil conditions for the SMAP satellite. The aircraft based soil moisture data provided by the passive/active microwave sensor PALS (Passive and Active L-band System) has a nominal spatial resolution of 1600 m. However, this resolution is not compatible with agricultural, meteorological and hydrological studies that require high spatial resolutions and this issue can be solved by soil moisture disaggregation. The soil moisture disaggregation algorithm integrates radiometer soil moisture retrievals and high-resolution radar observations and it can provide soil moisture estimates at a finer scale than the radiometer data alone. In this study, a change detection algorithm was used for disaggregation of coarse resolution passive microwave soil moisture retrievals with radar backscatter coefficients obtained from the higher spatial resolution UAVSAR (Unmanned Air Vehicle Synthetic Aperture Radar) at crop field scale. The accuracy of the disaggregated change in soil moisture was evaluated using ground based soil moisture measurements collected during SMAPVEX12 campaign. The results showed that soil moisture spatial variabilities were better characterized by the disaggregated change in soil moisture estimates at 5 m/800 m resolution as well as a good agreement between disaggregated estimates and in situ measurements. It also showed that VWC (Vegetation Water Content) did not have a big impact on disaggregation algorithm performance, with R-2 of the disaggregated results ranging 0.628-0.794. The 5 m and 800 m resolution disaggregated soil moisture did no show significant difference in statistical performance variables.

关键词： SMAP Microwave soil moisture disaggregation Radar backscatter change detection algorithm

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