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第41届中国控制会议

作者： Yizhe Jiang Weicheng Song Jie Chu Junhong Li School of Electrical Engineering Nantong University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this paper,the parameter estimation for Hammerstein-Wiener nonlinear systems with unknown delay is *** on the hierarchical identification principle and two-step identification,the maximum likelihood recursive algorithm is used to identify the parameters of the system,and the gradient descent method is used to identify the time ***,the algorithm is verified by a numerical example,and the simulation results show that the algorithm has the characteristics of fast convergence speed and high identification accuracy.

关键词： system identification Hammerstein-Wiener systems maximum likelihood algorithm gradient descent algorithm hierarchical identification

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Classification of PolSAR data with the Complex Riesz distribution

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2025年

作者： Kammoun, Rayhan Kessentini, Sameh Zine, Raoudha Univ Sfax Fac Sci Sfax Lab Probabil & Stat BP 1171 Sfax 3000 Tunisia

This article deals with unsupervised classification strategies applied to polarimetric synthetic aperture radar (PolSAR) images. We discuss the performance of the Complex Riesz distribution, which is used for the first time to classify PolSAR images. Hence, we extend the maximum likelihood (ML) and expectation-maximization (EM) algorithms to the Complex Riesz distribution. Furthermore, we derive the analytic expression of five stochastic distances (Kullback-Leibler, Bhattacharyya, R & eacute;nyi, Hellinger, and Chi-square) between Complex Riesz distributions. We assess the accuracy of the Complex Riesz EM algorithm on synthetic data generated by an extension of the Bartlett decomposition. Then, comparing the Complex Wishart and the Complex Riesz distributions on PolSAR data reveals that the latter performs better than the former. Finally, the EM algorithm for the Complex Riesz distribution serves to classify the actual data, and the discrimination potential of the five stochastic distances is discussed. These results in both experimental, ML, and EM algorithms suggest that most stochastic distances are significant, and mainly the 0. 7-order R & eacute;nyi.

关键词： Complex Riesz distribution expectation-maximization algorithm Hermitian random matrix maximum likelihood algorithm PolSAR classification stochastic distances

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maximum likelihood DOA estimation method based on matrix completion in non-uniform noise environment 14

Maximum likelihood DOA estimation method based on matrix com...

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14th International Symposium on Antennas Propagation and EM Theory

作者： Zhao Xiaomei Wu Jiajing Peng Huaiyun Chinese Res Inst Radiowave Propagat Natl key Lab Electromagnet Environm Qingdao Peoples R China

ISBN: (纸本)9798350353129;9798350353136

For the estimation of the direction of reach in non-uniform noise environment, the method is proposed by applying Matrix Completion theory (Matrix Completion, MC) combined with the Improved Monarch Butterfly Optimization algorithm based ML (ML-IMBO) method, In this paper, a matrix completion based ML-IMBO (MML-IMBO) estimation method for maximum likelihood DOA under heterogeneous noise environment is proposed. Experimental simulation results show that the proposed MML-IMBO algorithm can effectively suppress the influence of non-uniform noise and has good performance in DOA estimation.

关键词： Direction of Arrival estimation Non-uniform noise Matrix completion maximum likelihood algorithm Monarch butterfly optimization algorithm

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Spatio-temporal dynamics of land use and land cover types within the Belabo-Diang communal forest in East Cameroon

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MODELING EARTH SYSTEMS AND ENVIRONMENT 2025年第4期11卷 1-18页

作者： Ngoukwa, Guylene Zekeng, Jules Christian Tchonang, Bienvenu Leonnel Djoumbi Chimi, Cedric Djomo Kabelong, Louis-Paul-Roger Banoho Ntonmen, Amandine Yonkeu Zapfack, Louis Univ Yaounde I Fac Sci Dept Plant Biol BP 812 Yaounde Cameroon Conservat & Sustainable Nat Resources Management N POB 8554 Yaounde Cameroon Univ Douala Adv Teachers Training Sch Tech Educ Dept Forest Engn POB 1872 Douala Cameroon Copperbelt Univ Oliver R Tambo Africa Res Chair Initiat ORTARChl D Sch Nat Resources POB 21692 Kitwe Zambia Inst Rech Agr Dev IRAD POB 136 Yokadouma Cameroon

Understanding spatiotemporal land-use and vegetation dynamics is vital for sustainable management;however, translating this knowledge into effective planning remains challenging. This study addresses the gap by assessing Land Use and Land Cover (LULC) dynamics in the Belabo-Diang Communal Forest (BDCF) in eastern Cameroon, employing remote sensing and Geographic Information System (GIS) techniques. Landsat images from December 1984, December 2002, and November 2020 were utilised for supervised classification using the maximum likelihood algorithm, resulting in thematic maps of land use/land cover (LULC). Post-classification analysis, conducted at the pixel scale and LULC process insights, assessed forest loss and change trajectories in the BDCF. Results show the BDCF covers 53,213.80 hectares, with seven LULC types: Mature Secondary Forest (MSF), Young Secondary Forest (YSF), Savannah (SA), Cropland and Fallow (CF), Bare Soil (BS), permanently flooded swamp (PFS), and periodically flooded swamp (TFS). MSF declined 37.78% (1984-2002) but increased 36.10% (2002-2020). The annual change per hectare shows that MSF decreased by 319.42 ha/year, while SA, CF, and BS increased by 401.12, 351.7, and 1281.4 ha/year, respectively, resulting in a loss of 19,625.93 ha in forest formations. These findings support BDCF managers in landscape restoration and the Reducing Emissions from Deforestation and Forest Degradation (REDD +) initiative, providing insights for sustainable land management, effective conservation strategies, and mitigating climate impact.

关键词： Change trajectories Communal forest Eastern Cameroon Land use and land cover maximum likelihood algorithm Spatio-temporal dynamics

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Joint DOA and Doppler frequency estimation for MIMO Radars in the Presence of Array Model Imperfections

Joint DOA and Doppler frequency estimation for MIMO Radars i...

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Han, Shuaishuai Al-Jarrah, Mohammad A. Alsusa, Emad Univ Manchester Sch Elect & Elect Engn Manchester M13 9PL Lancs England

ISBN: (纸本)9781665491228

In this paper, the problem of joint direction of arrival (DOA) and Doppler frequency estimation with array model defects is investigated. To this end, a two-dimensional (2D) robust multiple signal classification (MUSIC) algorithm is proposed, which is a generalization of the conventional MUSIC algorithm. The introduced algorithm is based on a colocated multiple-input-multiple-output (MIMO) radar composed of uniform linear arrays, in which a mix of calibrated and uncalibrated antennas exist in its transmitter, while all the receiver antennas are assumed to be uncalibrated. To be specific, the 2D R-MUSIC algorithm adopts a separation scheme to automatically obtain the signals associated with the calibrated subset of transmitter antennas and then eliminates the effect of the signals belonging to the uncalibrated transmitting and receiving antennas. Afterward, the localization is performed by modifying the conventional algorithms in accordance with the separation scheme. In addition, a 2D Genie maximum likelihood (ML) algorithm under the assumption of precisely known errors at the receiver is derived to provide a lower bound benchmark for the introduced method. The simulation results demonstrate the superiority of the proposed algorithms relative to the conventional ones.

关键词： Array model errors DOA and Doppler frequency estimations MIMO Radar MUSIC algorithm maximum likelihood algorithm

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Monitoring forest landcover changes in the Eastern Sundarban of Bangladesh from 1989 to 2019

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ACTA GEOPHYSICA 2021年第2期69卷 561-577页

作者： Kumar, Mithun Mondal, Ismail Pham, Quoc Bao Space & Environm Res Ctr SERC Rajshahi 6205 Bangladesh Jadavpur Univ Sch Oceanog Studies Kolkata 700032 India Ton Duc Thang Univ Environm Qual Atmospher Sci & Climate Change Res Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Environm & Labour Safety Ho Chi Minh City Vietnam

The present study aims to estimate areal extent of the mangrove forest cover in the eastern Sundarban of Bangladesh from 1989 to 2019 to understand mangrove dynamic over the last 30 years. Freely available Landsat TM of 1989, 2014, and L8 OLI imagery were used to generate land use/land cover (LU/LC) map for the study area using maximum likelihood (MaxLike) algorithm. Results of previous investigations among different scientists and researchers were used to develop a conceptual background and also included in this paper to find out the causes that relate to forest cover change in the study area. Study results show that the vegetation cover of Sharankhola range in Sundarban has decreased by 0.44% over last 30 years (from 1989 to 2019). Water body has increased (1.30%) with the decrease in vegetation cover. Classified map of 2014 and 2019 shows that 2.66% vegetation cover of the study area was lost in 2014 based on 1989 while 2.22% vegetation cover was gained in 2019 based on 2014. The overall accuracy of Landsat TM (1989), TM (2014), and L8 OLI (2019) were 80%, 82.85%, and 84.28%, respectively. Its accuracy would increase if it is supplemented by extensive ground verification data and hybrid satellite data of different spectral and spatial resolution. [GRAPHICS] .

关键词： maximum likelihood algorithm Remote sensing GIS Supervised classification Mangrove ecosystem Sundarbans

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Drone-Based Characterization of Seagrass Habitats in the Tropical Waters of Zanzibar

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REMOTE SENSING 2022年第3期14卷 680-680页

作者： Hamad, Idrissa Yussuf Staehr, Peter Anton Upadhyay Rasmussen, Michael Bo Sheikh, Mohammed Aarhus Univ Dept Ecosci Frederiksborgvej 399 DK-4000 Roskilde Denmark State Univ Zanzibar Trop Res Ctr Oceanog Environm & Nat Resources TRO POB 146 Zanzibar Tanzania

Unmanned automatic systems (UAS) are increasingly being applied as an alternative to more costly time-consuming traditional methods for mapping and monitoring marine shallow-water ecosystems. Here, we demonstrate the utility of combining aerial drones with in situ imagery to characterize the habitat conditions of nine shallow-water seagrass-dominated areas on Unguja Island, Zanzibar. We applied object-based image analysis and a maximum likelihood algorithm on the drone images to derive habitat cover maps and important seagrass habitat parameters: the habitat composition;the seagrass species;the horizontal- and depth-percent covers, and the seascape fragmentation. We mapped nine sites covering 724 ha, categorized into seagrasses (55%), bare sediment (31%), corals (9%), and macroalgae (5%). An average of six seagrass species were found, and 20% of the nine sites were categorized as "dense cover" (40-70%). We achieved high map accuracy for the habitat types (87%), seagrass (80%), and seagrass species (76%). In all nine sites, we observed clear decreases in the seagrass covers with depths ranging from 30% at 1-2 m, to 1.6% at a 4-5 m depth. The depth dependency varied significantly among the seagrass species. Areas associated with low seagrass cover also had a more fragmented distribution pattern, with scattered seagrass populations. The seagrass cover was correlated negatively (r(2) = 0.9, p < 0.01) with sea urchins. A multivariate analysis of the similarity (ANOSIM) of the biotic features, derived from the drone and in situ data, suggested that the nine sites could be organized into three significantly different coastal habitat types. This study demonstrates the high robustness of drones for characterizing complex seagrass habitat conditions in tropical waters. We recommend adopting drones, combined with in situ photos, for establishing a suite of important data relevant for marine ecosystem monitoring in the Western Indian Ocean (WIO).

关键词： UAS remote sensing shallow-water ecosystems seascape fragmentation image classification maximum likelihood algorithm aquatic monitoring

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Land cover mapping using Sentinel-1 SAR and Landsat 8 imageries of Lagos State for 2017

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2020年第1期27卷 66-74页

作者： Makinde, Esther Oluwafunmilayo Oyelade, Edward Oluwasegun Univ York Environm Dept York N Yorkshire England Univ Lagos Fac Engn Dept Surveying & Geoinformat Lagos Nigeria

For several years, Landsat imageries have been used for land cover mapping analysis. However, cloud cover constitutes a major obstacle to land cover classification in coastal tropical regions including Lagos State. In this work, a land cover appearance for Lagos State is examined using Sentinel-1 synthetic aperture radar (SAR) and Land Satellite 8 (Landsat 8) imageries. To this aim, a Sentinel-1 SAR dual-pol (VV+VH) Interferometric Wide swath mode (IW) data orbit for 2017 and a Landsat 8 Operational Land Imager (OLI) for 2017 over Lagos State were acquired and analysed. The Sentinel-1 imagery was calibrated and terrain corrected using a SRTM 3Sec DEM. maximum likelihood classification algorithm was performed. A supervised pixel-based imagery classification to classify the dataset using training points selected from RGB combination of VV and VH polarizations was applied. Accuracy assessment was performed using test data collected from high-resolution imagery of Google Earth to determine the overall classification accuracy and Kappa coefficient. The Landsat 8 was orthorectified and maximum likelihood classification algorithm also performed. The results for Sentinel-1 include an RGB composite of the imagery, classified imagery, with overall accuracy calculated as 0.757, while the kappa value was evaluated to be about 0.719. Also, the Landsat 8 includes a RBG composite of the imagery, classified imagery, but an overall accuracy of 0.908 and a kappa value of 0.876. It is concluded that Sentinel 1 SAR result has been effectively exploited for producing acceptable accurate land cover map of Lagos State with relevant advantages for areas with cloud cover. In addition, the Landsat 8 result reported a high accuracy assessment values with finer visual land cover map appearance.

关键词： Sentinel-1 Landsat 8 Synthetic aperture radar (SAR) imagery Remote sensing maximum likelihood algorithm Land cover Lagos State

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GIS and remote sensing Based Analysis of Land use and Land cover Change in the Upper Anger watershed, Western Ethiopia

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Geology, Ecology, and Landscapes 2023年第2期9卷

作者： Moisa, Mitiku Badasa Busha Hinkosa, Lachisa Negasa, Geda Feyera Olika, Gamtesa Ijigu, Tekalign Emiru Wedajo, Yonas Name Gurmessa, Mengistu Muleta Deribew, Kiros Tsegay Gemeda, Dessalegn Obsi Department of Agricultural Engineering Faculty of Technology Wollega University Shambu Campus Shambu Ethiopia Department of Natural Resources Management Faculty of Resources Management and Economics Wollega University Shambu Campus Shambu Ethiopia Department of Fisheries and Aquatic Sciences Faculty of Agriculture Wollega University Shambu Campus Shambu Ethiopia Department of Geography and Environmental Studies Raya University Maichew Ethiopia Department of Natural Resource Management College of Agriculture and Veterinary Medicine Jimma University Jimma Ethiopia

Anthropogenic land use and land cover (LULC) change was the most serious problem in worldwide. This study attempted to investigate spatiotemporal LULC changes in the Upper Anger watershed, Western Ethiopia. In the present study, Landsat TM of 1990, Landsat ETM⁺ of 2003, and Landsat OLI/TIRS of 2020 were used to analyze the LULC change and Normalized Difference Vegetation Index (NDVI). The supervised classification with maximum likelihood algorithm was employed for classification of the LULC. The result shows that the agricultural land was increased by 707.4 km², whereas forest land was decreased by 179.9 km². Consequently, agricultural land and bare land were increased with rate of 23.6 km²/year and 0.5 km2/year, while grassland and forest cover decreased by rate of 18.2 km²/year and 6 km²/year, respectively, from 1990 to 2020. The maximum NDVI value was declined with 0.19 from 1990 to 2020 due to expansion of agricultural land, bare land, and settlement in the study area. About 530 km² of grassland and 241.5 km² of forest cover were converted into other land cover classes between 1990 and 2020. Thus, appropriate land use planning and environmental education should be promoted to minimize the rapid loss of forests and grasslands in the study area. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON).

关键词： Landsat images maximum likelihood algorithm NDVI supervised classification upper anger watershed

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Analyzing Wetland Dynamics Using Geospatial Techniques: A Case of Abay Choman and Jimma Geneti Watershed, Horo Guduru Wollega Zone, Western Ethiopia

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AIR SOIL AND WATER RESEARCH 2023年第1期16卷 1-11页

作者： Moisa, Mitiku Badasa Bulto, Tadesse Weyuma Werku, Birhanu Chalchisa Berkessa, Yifru Waktole Chebo, Abdella Kosa Negash, Daniel Assefa Gemeda, Dessalegn Obsi Wollega Univ Shambu Campus Nekemte Ethiopia Kotebe Univ Educ Addis Ababa Ethiopia Jimma Univ Jimma Ethiopia Wollega Univ Fac Technol Dept Agr Engn Shambu Campus Shambu Ethiopia

Wetland ecosystems are one of the most important areas that provides different ecosystems services as well as habitat for plant and animal species. In spite of multipurpose, wetland ecosystems are under threats. This study attempts to analyze wetland dynamics of Abay Choman and Jimma Geneti watershed in Horo Guduru Wollega Zone, Western Ethiopia using geospatial techniques. The land use land cover (LULC), the Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI) were investigated using Landsat 5 TM of 1991, Landsat 7ETM+ of 2003, and OLI/TIRS of 2021. In the present study, the LULC was classified using a supervised classification method with maximum likelihood algorithm. The red and infrared bands of Landsat imagery from three different time periods were used to calculate NDVI, while the NDWI was estimated using the green and near infrared (NIR) bands of multispectral Landsat images. Results show that wetland ecosystem in the study area decreased by about 125.2 km(2) (8.8%) with the rate of 4.2 km(2)/year. In contrast, agricultural land increased by 223.4 km(2) with the rate of 7.4 km(2)/year between 1991 and 2021. About 66.7 km(2) wetland was converted to cultivated land whereas 29.3 and 24.7 km(2) of grassland and shrubs land were converted into cultivated land. As a result, the maximum NDVI and NDWI values were decreased between 1991 and 2021. To minimize the rapid loss of wetland and water bodies in the study area, proper land use planning and environmental education should be promoted.

关键词： Land use land cover wetland maximum likelihood algorithm normalized difference vegetation index normalized difference water index Abay Choman watershed

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