Canadian Journal of Remote Sensing最新文献_第8页

An Improved Bald Eagle Search Algorithm with Deep Learning Model for Forest Fire Detection Using Hyperspectral Remote Sensing Images 基于深度学习模型的改进秃鹰搜索算法在高光谱遥感图像森林火灾探测中的应用

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-06-16 DOI: 10.1080/07038992.2022.2077709

A. Algarni, Nazik Alturki, N. Soliman, S. Abdel-Khalek, A. Mousa

{"title":"An Improved Bald Eagle Search Algorithm with Deep Learning Model for Forest Fire Detection Using Hyperspectral Remote Sensing Images","authors":"A. Algarni, Nazik Alturki, N. Soliman, S. Abdel-Khalek, A. Mousa","doi":"10.1080/07038992.2022.2077709","DOIUrl":"https://doi.org/10.1080/07038992.2022.2077709","url":null,"abstract":"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 noise removal. Besides, IBES algorithm with NASNetLarge method was utilized as a feature extractor to determine feature vectors. Finally, Firefly algorithm (FFA) with denoising autoencoder (DAE) is applied for the classification of forest fire. The design of IBES and FFA techniques helps to adjust optimally the parameters contained in the NSANetLarge and DAE models respectively. For demonstrating the better outcomes of the IBESDL-FFD approach, a wide-ranging simulation was implemented and the outcomes are examined. The results reported the better outcomes of the IBESDL-FFD technique over the existing techniques with maximum average accuracy of 93.75%.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"48 1","pages":"609 - 620"},"PeriodicalIF":2.6,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47714265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

An Experimental Study on the Effects of Noise on Endmember Extraction Methods 噪声对端元提取方法影响的实验研究

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-06-06 DOI: 10.1080/07038992.2022.2081537

Guangyi Chen, A. Krzyżak, S. Qian

引用次数: 1

Learning-Based Methods for Detection and Monitoring of Shallow Flood-Affected Areas: Impact of Shallow-Flood Spreading on Vegetation Density 基于学习的浅层洪水影响区检测和监测方法：浅层洪水扩散对植被密度的影响

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-05-13 DOI: 10.1080/07038992.2022.2072277

Mohammad Kazemi Garajeh, Qihao Weng, Vahid Hossein Haghi, Zhenlong Li, Ali Kazemi Garajeh, Behnam Salmani

{"title":"Learning-Based Methods for Detection and Monitoring of Shallow Flood-Affected Areas: Impact of Shallow-Flood Spreading on Vegetation Density","authors":"Mohammad Kazemi Garajeh, Qihao Weng, Vahid Hossein Haghi, Zhenlong Li, Ali Kazemi Garajeh, Behnam Salmani","doi":"10.1080/07038992.2022.2072277","DOIUrl":"https://doi.org/10.1080/07038992.2022.2072277","url":null,"abstract":"Abstract This study aims to investigate the impacts of shallow flood spreading on vegetation density using a time-series collection of Landsat images spanning 2012–2020. To do this, Support Vector Machine (SVM), Random Forest (RF), Classification and Regression tree (CART) and Deep Learning Convolutional Neural Network (DL-CNN) algorithms were employed for flood-affected areas mapping and monitoring. The models were trained by using 214, 235, 230, and 219 ground truth data for years 2012, 2014, 2017 and 2020 respectively. Our accuracy assessment via the area under curve (AUC) method reveals that the DL-CNN outperforms the SVM, the RF and the CART models for detecting and mapping shallow-flood-affected areas. The findings of this study further revealed significant changes in the NDVI values within a period before and after flood occurrence. While the mean values of the NDVI were estimated 0.232, 0.221, 0.213, and 0.232 for years 2012, 2014, 2017, and 2020, respectively, prior to flood spreading, these values increased up to 0.464, 0.476, 0.355 and 0.444, respectively following flood occurrence. Furthermore, physical-chemical soil properties (e.g., clay, EC, Na, and MgHCO3), have grown considerably in the study region following the flood spreading.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"48 1","pages":"481 - 503"},"PeriodicalIF":2.6,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48720757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Detection of Management Practices and Cropping Phases in Wild Lowbush Blueberry Fields Using Multispectral UAV Data 利用多光谱无人机数据检测野生低灌木蓝莓田的管理措施和种植阶段

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-05-04 DOI: 10.1080/07038992.2022.2070144

C. Marty, Siddhartha Khare, Sergio Rossi, J. Lafond, M. Boivin, M. Paré

{"title":"Detection of Management Practices and Cropping Phases in Wild Lowbush Blueberry Fields Using Multispectral UAV Data","authors":"C. Marty, Siddhartha Khare, Sergio Rossi, J. Lafond, M. Boivin, M. Paré","doi":"10.1080/07038992.2022.2070144","DOIUrl":"https://doi.org/10.1080/07038992.2022.2070144","url":null,"abstract":"Abstract Normalized difference vegetation index (NDVI) and normalized difference red-edge index (NDRE) are vegetation indices commonly used in agriculture to provide information on crop’s growth and health. Here, we investigated the sensitivity of both indices to management practices in lowbush blueberry fields. Images of the experimental plots were collected with a multispectral camera installed on an unmanned aerial vehicle. Both NDVI and NDRE values were significantly higher in fertilized plots than in controls (0.88 ± 0.03 vs. 0.79 ± 0.03 for NDVI, and 0.37 ± 0.01 vs. 0.33 ± 0.01 for NDRE) due to fertilization effect on vegetative growth. The increase was higher under mineral than organic fertilization during the two first phases of the cropping system (by ∼0.3 and ∼0.2 for NDVI and NDRE, respectively). NDRE was not affected by thermal pruning and fungicide application but was negatively correlated with Septoria infection level. NDVI was more strongly correlated with stem length than NDRE, but unlike NDRE, NDVI was not impacted by the development of reproductive shoots during the harvest phases. Overall, the results indicate that although both index values are correlated, their sensitivity to changes in canopy characteristics differs depending on the cropping phase. Further research must be conducted to relate these indices to blueberry’s nutrient status.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"48 1","pages":"469 - 480"},"PeriodicalIF":2.6,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48510718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Quantitative Inversion Modeling Method for Grading Deerni Copper Deposits Based on Visible and Near-Infrared Hyperspectral Data 基于可见光和近红外高光谱数据的Deerni铜矿分级定量反演建模方法

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-04-21 DOI: 10.1080/07038992.2022.2059755

Jingli Wang, Jingxiang Gao, Jiaqi Huang, Qinghui Qi, Xinqi Mao, Wang Cao, Ruibo Ding, Yachun Mao

{"title":"Quantitative Inversion Modeling Method for Grading Deerni Copper Deposits Based on Visible and Near-Infrared Hyperspectral Data","authors":"Jingli Wang, Jingxiang Gao, Jiaqi Huang, Qinghui Qi, Xinqi Mao, Wang Cao, Ruibo Ding, Yachun Mao","doi":"10.1080/07038992.2022.2059755","DOIUrl":"https://doi.org/10.1080/07038992.2022.2059755","url":null,"abstract":"Abstract Quantitative metal grade inversion based on hyperspectral data is an effective approach to achieve the real-time in situ determination of ore body grades and has the advantages of low cost compared with traditional chemical analysis methods. However, the redundant nature of hyperspectral data and the parameter-limiting nature of machine learning algorithms reduce the modeling accuracy and precision, resulting in severe limitations on the application of hyperspectral techniques for the grade inversion of Deerni copper ore bodies. In this paper, we first obtained visible-NIR hyperspectral data for 190 ore samples using a spectrometer and determined the copper content of the sample set using chemical analysis; then, we processed the raw hyperspectral data using three dimensionality reduction algorithms and optimized a BP neural network based on an evolutionary algorithm. Finally, a Deerni copper grade inversion model was established using the hyperspectral data before and after dimensionality reduction, and the inversion accuracy and precision was compared and analyzed with that obtained by the BP neural network, the random forest and the variable hidden layer nodes models. The combination of the LLE dimensionality reduction algorithm and the optimized BP neural network algorithm achieves the highest modeling precision, with an R 2 of 0.950.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"48 1","pages":"592 - 608"},"PeriodicalIF":2.6,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43230629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Oil Spill Candidate Detection Using a Conditional Random Field Model on Simulated Compact Polarimetric Imagery 基于条件随机场模型的紧凑偏振成像候选漏油检测

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-04-20 DOI: 10.1080/07038992.2022.2055534

Saeid Taleghanidoozdoozan, Linlin Xu, David A Clausi

{"title":"Oil Spill Candidate Detection Using a Conditional Random Field Model on Simulated Compact Polarimetric Imagery","authors":"Saeid Taleghanidoozdoozan, Linlin Xu, David A Clausi","doi":"10.1080/07038992.2022.2055534","DOIUrl":"https://doi.org/10.1080/07038992.2022.2055534","url":null,"abstract":"Abstract Although the compact polarimetric (CP) synthetic aperture radar (SAR) mode of the RADARSAT Constellation Mission (RCM) offers new opportunities for oil spill candidate detection, there has not been an efficient machine learning model explicitly designed to utilize this new CP SAR data for improved detection. This paper presents a conditional random field model based on the Wishart mixture model (CRF-WMM) to detect oil spill candidates in CP SAR imagery. First, a “Wishart mixture model” (WMM) is designed as the unary potential in the CRF-WMM to address the class-dependent information of oil spill candidates and oil free water. Second, we introduce a new similarity measure based on CP statistics designed as a pairwise potential in the CRF-WMM model so that pixels with strong spatial connections have the same class label. Finally, we investigate three different optimization approaches to solve the resulting maximum a posterior (MAP) problem, namely iterated conditional modes (ICM), simulated annealing (SA), and graph cuts (GC). The results show that our proposed CRF-WMM model can delineate oil spill candidates better than the traditional CRF approaches, and that the GC algorithm provides the best optimization.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"48 1","pages":"425 - 440"},"PeriodicalIF":2.6,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45990614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Object-Oriented Unsupervised Change Detection Based on Neighborhood Correlation Images and k-Means Clustering for the Multispectral and High Spatial Resolution Images 基于邻域相关图像和k均值聚类的多光谱高空间分辨率图像的面向对象无监督变化检测

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-04-20 DOI: 10.1080/07038992.2022.2056434

Lidong Zou, Muyi Li, S. Cao, Feng Yue, Xiufang Zhu, Yizhan Li, Zaichun Zhu

{"title":"Object-Oriented Unsupervised Change Detection Based on Neighborhood Correlation Images and k-Means Clustering for the Multispectral and High Spatial Resolution Images","authors":"Lidong Zou, Muyi Li, S. Cao, Feng Yue, Xiufang Zhu, Yizhan Li, Zaichun Zhu","doi":"10.1080/07038992.2022.2056434","DOIUrl":"https://doi.org/10.1080/07038992.2022.2056434","url":null,"abstract":"Abstract An unsupervised change-detection problem is formulated as a binary classification problem corresponding to the change and no change areas. This paper proposes a novel unsupervised object-oriented change detection method based on neighborhood correlation images (NCIs) and k-means clustering for high-resolution remote sensing images. We tested our proposed method in two study areas of Beijing with RapidEye images and compared it with three other popular change detection methods based on different images: change vector analysis (CVA), principal component analysis (PCA), and multivariate alteration detection (MAD). The results indicate that our method has the highest overall accuracy (90.80% in Shunyi District, Beijing and 90.40% in Daxing District, Beijing) and Kappa coefficient (0.7922 in Shunyi District, Beijing and 0.7796 in Daxing District, Beijing). In addition, the McNemar test indicates that our method is robust and stable across different study areas. We concluded that the object-oriented NCIs method outperforms traditional difference images (CVA, PCA, and MAD) in unsupervised change detection. The experimental results demonstrate the effectiveness of the proposed approach in solving the problem of unsupervised change detection for high-resolution images.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"48 1","pages":"441 - 451"},"PeriodicalIF":2.6,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49155687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Comparison between Sentinel-2 and Landsat 8 OLI Satellite Images for Soil Salinity Distribution Mapping Using a Deep Learning Convolutional Neural Network 基于深度学习卷积神经网络的Sentinel-2与Landsat 8 OLI卫星影像土壤盐分分布图比较

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-04-18 DOI: 10.1080/07038992.2022.2056435

Mohammad Kazemi Garajeh, T. Blaschke, Vahid Hossein Haghi, Qihao Weng, Khalil Valizadeh Kamran, Zhenlong Li

{"title":"A Comparison between Sentinel-2 and Landsat 8 OLI Satellite Images for Soil Salinity Distribution Mapping Using a Deep Learning Convolutional Neural Network","authors":"Mohammad Kazemi Garajeh, T. Blaschke, Vahid Hossein Haghi, Qihao Weng, Khalil Valizadeh Kamran, Zhenlong Li","doi":"10.1080/07038992.2022.2056435","DOIUrl":"https://doi.org/10.1080/07038992.2022.2056435","url":null,"abstract":"Abstract In this paper, we aim to compare the suitability of Sentinel-2 and Landsat 8 OLI images for detecting and mapping soil salinity distribution (SSD) using a deep learning convolutional neural network (DL-CNN) approach. We first identified and selected six SSD predisposing variables to train the models. These variables are the normalized difference vegetation index (NDVI), land use, soil types, geomorphology, land surface temperature, and evaporation rate. Next, we collected 219 ground control points from the top 20 cm of the soil surface and randomly divided them into training (70%) and validation (30%) datasets. We then evaluated the different activation, loss/cost, and optimization functions and, finally, employed ReLu, Cross-Entropy, and Adam as the most effective activation function, loss/cost function, and optimizer, respectively. The results showed that the Sentinel-2 image (94.78% overall accuracy and a Kappa of 93.14%) is more suitable for detecting and mapping SSD than the Landsat 8 OLI image (91.45% overall accuracy and a Kappa of 90.45%). Our findings also demonstrated that the DL-CNN approach can support fast and reliable image analysis and classification. As such, this research is a promising step toward understanding, controlling, and managing the complex mechanisms of soil salinization.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"48 1","pages":"452 - 468"},"PeriodicalIF":2.6,"publicationDate":"2022-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44179418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Leveraging Google Earth Engine and Semi-Supervised Generative Adversarial Networks to Assess Initial Burn Severity in Forest 利用谷歌地球引擎和半监督生成对抗网络评估森林初始烧伤严重程度

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-04-04 DOI: 10.1080/07038992.2022.2054405

Guangyi Wang, Youmin Zhang, Wen-Fang Xie, Y. Qu

{"title":"Leveraging Google Earth Engine and Semi-Supervised Generative Adversarial Networks to Assess Initial Burn Severity in Forest","authors":"Guangyi Wang, Youmin Zhang, Wen-Fang Xie, Y. Qu","doi":"10.1080/07038992.2022.2054405","DOIUrl":"https://doi.org/10.1080/07038992.2022.2054405","url":null,"abstract":"Abstract Mapping and monitoring initial burn severity is a critical aspect of forest management and landscape restoration. Recently, supervised learning has achieved great success in evaluating the post-fire forest condition, but plenty of labeled samples are needed to carry out training for supervised learning. However, due to the limitation of accessible labeled data, it is often arduous to put supervised learning into practice in the remote sensing field. In this paper, a novel semi-unsupervised image classification framework by using generative adversarial networks under the Google Earth Engine (GEE) platform is proposed to undertake the burn severity assessment with limited samples. The generative model can produce additional adversarial samples that look like the real samples; therefore, the discriminative model gains better classification capabilities in burn severity assessment with the extra training data provided by the generator. The detailed evaluation and comparative experiments are done in the context of post-fire assessment on eleven forest fires in northern California in the United States. The experimental results demonstrate that our approach significantly outperforms the two other most well-known methods.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"48 1","pages":"411 - 424"},"PeriodicalIF":2.6,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43212872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Study on Elimination Algorithms for Line Segment Mismatches 线段不匹配的消除算法研究

IF 2.6 4区地球科学

Canadian Journal of Remote Sensing Pub Date : 2022-04-01 DOI: 10.1080/07038992.2022.2052032

Chang Li, Wenqi Jia, D. Wei

{"title":"Study on Elimination Algorithms for Line Segment Mismatches","authors":"Chang Li, Wenqi Jia, D. Wei","doi":"10.1080/07038992.2022.2052032","DOIUrl":"https://doi.org/10.1080/07038992.2022.2052032","url":null,"abstract":"Abstract Image matching is a key step for remotely sensed image registration and digital elevation model (DEM) generation. Compared with point matching, few studies have focused on line matching for images, especially elimination algorithm of mismatched line segments. Therefore, this work systematically studies elimination algorithms of line segment mismatches by combining 2 transformation models (i.e., affine and homography) with 2 M-estimators or 2 sample consensus methods (i.e., random sample consensus, RANSAC, and least median of squares, LMedS). The main idea is as follows. After line segments are extracted and matched, the proposed algorithms can automatically remove mismatched line segments based on an error function of line segment. Aerial images with panchromatic bands and standard false color synthesis were selected for testing. Experiments were performed to compare different combinations of these models and methods and to quantitatively evaluate the performance of the algorithms in terms of accuracy and run time. The results show that the proposed algorithm can be effectively applied to automatically eliminate mismatched line segments, and among all combinations the homography model with LMedS performs the best. The algorithm can also ensure and control the quality of line segment matching from stereo pairs.","PeriodicalId":48843,"journal":{"name":"Canadian Journal of Remote Sensing","volume":"48 1","pages":"400 - 410"},"PeriodicalIF":2.6,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47016926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0