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A New Texture Aware-Seed Demand Enhanced simple non-iterative clustering (ESNIC) Segmentation Algorithm for Efficient Land Use and Land Cover Mapping on Remote Sensing Images

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IEEE ACCESS 2024年 12卷 121208-121222页

作者： Selvaraj, Rohini Amali, D. Geraldine Bessie Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

Change detection in Land Use and Land Cover (LULC) on remote sensing images is essential for urban planning, disaster risk management, climate change monitoring, and biodiversity conservation. Precise detection of these changes is heavily impacted by the classification accuracy of the LULC types which can be improved significantly by addressing the misclassification errors arising due to similar spectral LULC types and overlapping LULC regions. This paper proposes a texture-aware and seed-demanding Enhanced simple non-iterative clustering (ESNIC) segmentation algorithm and Boundary-Specific Two-Level (BSTL) classification approach that reduces misclassification rates due to similar spectral signatures and minimizes computational redundancy. Incorporating texture features extracted through the Gray-Level Co-occurrence Matrix along with spectral information in the proposed ESNIC segmentation algorithm improves the ability to distinguish between different LULC types that share the same spectral value. The seed demanding ESNIC segmentation approach seeds are strategically placed based on the content adaptation approach rather than being uniformly distributed throughout the image which reduces segmentation time, providing a substantial advantage for large-scale land cover mapping. A BSTL classification approach that synergistically combines the Support Vector Machine's ability to effectively handle high dimensional data with the k-Nearest Neighbor's ability to handle irregular data is used. This study is assessed in terms of Overall Accuracy(OA), Producer Accuracy, User Accuracy, kappa coefficients (K), Root Mean Square Errors (RMSE), and F1 scores. Results indicate that the proposed ESNIC-BSTL (OA = 97.18%, K = 0.96 and RMSE= 0.1311) approach provides better accuracy than SNIC-SVM (94.42%, 0.92, and 0.1422) and SNIC-BSTL (95.78%, 0.94 and 0.1362).

关键词： Remote sensing images land use and land cover simple non-iterative clustering support vector machine k-nearest neighbor google earth engine

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Weakly supervised mapping of old and renewed urban areas in China during the recent two decades

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2024年 134卷

作者： Ni, Hao Yu, Le Gong, Peng Tsinghua Univ Inst Global Change Studies Dept Earth Syst Sci Minist Educ Key Lab Earth Syst Modeling Beijing 100084 Peoples R China Tsinghua Univ Dept Earth Syst Sci Minist Educ Ecol Field Stn East Asian Migratory Bi Beijing 100084 Peoples R China Tsinghua Univ Xian Inst Surveying & Mapping Joint Res Ctr Next Generat Smart Mapping Dept Earth Syst Sci Beijing 100084 Peoples R China Univ Hong Kong Dept Earth Sci Dept Geog Hong Kong Peoples R China Univ Hong Kong Inst Climate & Carbon Neutral Hong Kong Peoples R China

China has progressively elevated old city transformation and urban renewal to a policy priority, positioning them as new endogenous drivers of urban development. It raises the demand for real-time insight into the spatiotemporal distribution of old and renewed urban areas. We propose a weakly supervised mapping framework with adaptive adjustments city by city without relying on high-precision training samples. It is also convenient for variable spatial range and study period. We combined Landsat imagery during 2000-2021, LandTrendr change detection algorithm and simple non-iterative clustering image segmentation into a Threshold Voting approach. The overall accuracy and Kappa coefficient of our results are 78.37 % and 0.57, respectively, with interesting global and local patterns. Old urban areas cluster in the early developed city centers, accounting for 22.55 % nationwide, usually interspersed and surrounded with renewed urban areas (77.45 %). Our mapping framework provides an efficient and flexible scheme for ground history detection, and the related results can be applied as helpful references for urban renewal field work in China.

关键词： Weakly supervised classification Old urban area Urban renewal LandTrendr simple non-iterative clustering Threshold voting

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QUANTIFYING HETEROGENEOUS ECOSYSTEM SERVICES WITH MULTI-LABEL SOFT CLASSIFICATION

QUANTIFYING HETEROGENEOUS ECOSYSTEM SERVICES WITH MULTI-LABE...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Tian, Zhihui Upchurch, John Simon, G. Austin Dubeux, Jose Zare, Alina Zhao, Chang Harley, Joel B. Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA Univ Florida Dept Soil Water & Ecosyst Sci Gainesville FL USA Univ Florida Dept Agron Gainesville FL 32611 USA

ISBN: (纸本)9798350360332;9798350360325

Understanding and quantifying ecosystem services are crucial for sustainable environmental management, conservation efforts, and policy-making. The advancement of remote sensing technology and machine learning techniques has greatly facilitated this process. Yet, ground truth labels, such as biodiversity, are very difficult and expensive to measure. In addition, more easily obtainable proxy labels, such as land use, often fail to capture the complex heterogeneity of the ecosystem. In this paper, we demonstrate how land use proxy labels can be implemented with a soft, multi-label classifier to predict ecosystem services with complex heterogeneity.

关键词： ecosystem services satellite imagery random forest soft classifier simple non-iterative clustering

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Accurate Paddy Rice Mapping Based on Phenology-Based Features and Object-Based Classification

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REMOTE SENSING 2024年第23期16卷 4406-4406页

作者： Zhang, Jiayi Gao, Lixin Liu, Miao Dong, Yingying Liu, Chongwen Casa, Raffaele Pignatti, Stefano Huang, Wenjiang Li, Zhenhai Tian, Tingting Hu, Richa Shandong Univ Sci & Technol Coll Geodesy & Geomat Qingdao 266590 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Remote Sensing & Digital Earth Beijing 100094 Peoples R China China Univ Min & Technol Sch Elect Engn Xuzhou 221000 Peoples R China Univ Tuscia Dept Agr & Forestry Sci DAFNE Via San Camillo Lellis I-01100 Viterbo Italy CNR Inst Methodol Environm Anal IMAA I-85050 Tito Italy Surveying & Mapping Geog Informat Ctr Inner Mongol Hohhot 010010 Peoples R China

Highly accurate rice cultivation distribution and area extraction are essential to food security. Moreover, Inner Mongolia, whose slogan is "from scientific rice to world rice", is an essential national rice production base. However, high-quality rice mapping products at high resolutions are still scarce around the Inner Mongolia Autonomous Region. This condition is not conducive to rational planning of farmland resources, maintaining food security, and promoting sustainable growth of the local agricultural economy. In this study, the rice backscattering intensity difference index from the vertically polarized backscatter intensity of Sentinel-1 and the phenology differential index from the spectral indices of two critical rice phenological phases of Sentinel-2 images were constructed. Other spectral features, including spectral indices, tasseled cap, and texture features, were computed using simple non-iterative clustering (SNIC) to achieve image segmentation. These variables served as input features for the random forest (RF) algorithm. Results reveal that employing the RF with the SNIC segmentation algorithm and combining it with optical and synthetic aperture radar data is an effective way to extract data on rice in mid-latitude regions. The overall accuracy and kappa coefficient are 0.98 and 0.967, correspondingly. The accuracy for rice is 0.99, as proven by empirical data. These results meet the requirements of regional rice cultivation assessment and area monitoring. Furthermore, owing to its resilience against longitude-associated influences, the model discerns rice across diverse regions and multiple years, achieving an R2 of 0.99. This capability significantly bolsters efforts to improve regional food security and the pursuit of sustainable development.

关键词： rice mapping Google Earth Engine phenology simple non-iterative clustering random forest

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Temporal assessment of forest cover dynamics in response to forest fires and other environmental impacts using AI

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2024年第10期196卷 893页

作者： Noor, Shehla Mumtaz, Rafia Khan, Muhammad Ajmal Natl Univ Sci & Technol Sch Elect Engn & Comp Sci Islamabad 44000 Pakistan

The rapid reduction of forests due to environmental impacts such as deforestation, global warming, natural disasters such as forest fires as well as various human activities is an escalating concern. The increasing frequency and severity of forest fires are causing significant harm to the ecosystem, economy, wildlife, and human safety. During dry and hot seasons, the likelihood of forest fires also increases. It is crucial to accurately monitor and analyze the large-scale changes in the forest cover to ensure sustainable forest management. Remote sensing technology helps to precisely study such changes in forest cover over a wide area over time. This research analyzes the impact of forest fires over time, identifies hotspots, and explores the environmental factors that affect forest cover change. Sentinel-2 imagery was utilized to study changes in Brunei Darussalam's forest cover area over five years from 2017 to 2022. An object-based approach, simple non-iterative clustering (SNIC), is employed to cluster the region using NDVI values and analyze the changes per cluster. The results indicate that the area of the clusters reduced where fire incidence occurred as well as the precipitation dropped. Between 2017 and 2022, the increased forest fires and decreased precipitation levels resulted in the change in cluster areas as follows: 66.11%, 69.46%, 68.32%, 73.88%, 77.27%, and 78.70%, respectively. Additionally, hotspots in response to forest fires each year were identified in the Belait district. This study will help forest managers assess the causes of forest cover loss and develop conservation and afforestation strategies.

关键词： Forest fire Machine learning Remote sensing Deforestation simple non-iterative clustering

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Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors

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GISCIENCE & REMOTE SENSING 2021年第6期58卷 914-928页

作者： Shafizadeh-Moghadam, Hossein Khazaei, Morteza Alavipanah, Seyed Kazem Weng, Qihao Tarbiat Modares Univ Dept Water Engn & Management Tehran Iran Univ Tehran Fac Geog Dept Remote Sensing & GIS Tehran Iran Indiana State Univ Ctr Urban & Environm Change Dept Earth & Environm Syst Terre Haute IN 47809 USA

Mapping the distribution and type of land use and land cover (LULC) is essential for watershed management. The Tigris-Euphrates basin is a transboundary region in the Middle East shared between six countries, but a recent fine-scale LULC map of the area is lacking. Using Landsat-8 time series, a 30-m resolution LULC map was produced for the Tigris-Euphrates basin. In total, 1184 Landsat scenes were processed within the Google Earth Engine (GEE). For the collection of ground truth data, differential manifestations of green cover were considered by dividing the study area into five climatic regions and the training samples were taken from each sub-region. To account for the temporal variation of LULC types, six two-month interval composite layers, including the spectral and thermal bands of Landsat-8, texture and spectral indices, as well as topographic factors were created for the target year 2019. Image segmentation and classification were performed using the simple non-iterative clustering (SNIC) and Random Forest (RF) algorithms, respectively. A computationally effective parallel processing approach was developed, which created a number of tiles and sub-tiles and a bulk command was converted into smaller parallel commands. The generated LULC map showed a satisfactory overall accuracy of 91.7%, with the highest User's accuracy in water and wetland, and the lowest in rainfed crop and rangeland and the highest Producer's accuracy in water and barren areas, and the lowest in garden and rangeland. This study highlights the necessity of using multi-temporal data for LULC mapping, in particular, multi-temporal NDVI, for the separation of different green cover types in arid and semi-arid environment.

关键词： Land use and land cover simple non-iterative clustering multi-temporal NDVI topographic data arid and semi-arid region mapping climate zones

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Limited-Samples-Based Crop Classification Using a Time-Weighted Dynamic Time Warping Method, Sentinel-1 Imagery, and Google Earth Engine

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REMOTE SENSING 2023年第4期15卷 1112页

作者： Xiao, Xingyuan Jiang, Linlong Liu, Yaqun Ren, Guozhen Shandong Univ Sci & Technol Coll Geodesy & Geomatics Qingdao 266590 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Reg Sustainable Dev Modeling Beijing 100101 Peoples R China Shandong Ruizhi Flight Control Technol Co Ltd Qingdao 266500 Peoples R China

Reliable crop type classification supports the scientific basis for food security and sustainable agricultural development. However, it still lacks a limited-samples-based crop classification method which is labor- and time-efficient. To this end, we used the Google Earth Engine (GEE) and Sentinel-1A/B SAR time series to develop eight types of crop classification strategies based on different sampling methods of central and scattered, different perspectives of object-based and pixel-based, and different classifiers of the Time-Weighted Dynamic Time Warping (TWDTW) and Random Forest (RF). We carried out 30-times classifications with different samples for each strategy to classify the crop types at the North Dakota-Minnesota border in the U.S. We then compared their classification accuracies and assessed the accuracy sensitivity to sample size. The results found that the TWDTW generally performed better than RF, especially for small-sample classification. Object-based classifications had higher accuracies than pixel-based classifications, and the object-based TWDTW had the highest accuracy. RF performed better in scattered sampling than the central sampling strategy. TWDTW performed better than RF in distinguishing soybean and dry bean with similar curves. The accuracies improved for all eight classification strategies with increasing sample size, and TWDTW was more robust, while RF was more sensitive to sample size change. RF required many more samples than TWDTW to achieve satisfactory accuracy, and it performed better than TWDTW when the sample size exceeded 50. The accuracy comparisons indicated that the TWDTW has stronger temporal and spatial generalization capabilities and has high potential applications for early, historical, and limited-samples-based crop type classification. The findings of our research are worthwhile contributions to the methodology and practices of crop type classification as well as sustainable agricultural development.

关键词： Sentinel-1 SAR time series sampling strategy time-weighted dynamic time warping simple non-iterative clustering Google Earth Engine object-based crop classification

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High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images

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REMOTE SENSING 2021年第6期13卷 1148-1148页

作者： Yang, Lingbo Wang, Limin Abubakar, Ghali Abdullahi Huang, Jingfeng Zhejiang Univ Inst Appl Remote Sensing & Informat Technol Hangzhou 310058 Peoples R China Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Beijing 100081 Peoples R China

High-resolution crop mapping is of great significance in agricultural monitoring, precision agriculture, and providing critical information for crop yield or disaster monitoring. Meanwhile, medium resolution time-series optical and synthetic aperture radar (SAR) images can provide useful phenological information. Combining high-resolution satellite data and medium resolution time-series images provides a great opportunity for fine crop mapping. simple non-iterative clustering (SNIC) is a state-of-the-art image segmentation algorithm that shows the advantages of efficiency and high accuracy. However, the application of SNIC in crop mapping based on the combination of high-resolution and medium-resolution images is unknown. Besides, there is still little research on the influence of the superpixel size (one of the key user-defined parameters of the SNIC method) on classification accuracy. In this study, we employed a 2 m high-resolution GF-1 pan-sharpened image and 10 m medium resolution time-series Sentinel-1 C-band Synthetic Aperture Radar Instrument (C-SAR) and Sentinel-2 Multispectral Instrument (MSI) images to carry out rice mapping based on the SNIC method. The results show that with the increase of the superpixel size, the classification accuracy increased at first and then decreased rapidly after reaching the summit when the superpixel size is 27. The classification accuracy of the combined use of optical and SAR data is higher than that using only Sentinel-2 MSI or Sentinel-1 C-SAR vertical transmitted and vertical received (VV) or vertical transmitted and horizontal received (VH) data, with overall accuracies of 0.8335, 0.8282, 0.7862, and 0.7886, respectively. Meanwhile, the results also indicate that classification based on superpixels obtained by SNIC significantly outperforms classification based on original pixels. The overall accuracy, producer accuracy, and user accuracy of SNIC superpixel-based classification increased by 9.14%, 17.16%, 27.35% and 1.

关键词： high-resolution simple non-iterative clustering superpixel-based classification superpixel size multi-source

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Aluminum alloy microstructural segmentation method based on simple noniterative clustering and adaptive density-based spatial clustering of applications with noise

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JOURNAL OF ELECTRONIC IMAGING 2019年第3期28卷 033035-033035页

作者： Zhang, Shiyue Chen, Dali Liu, Shixin Zhang, Pengyuan Zhao, Wei Northeastern Univ Coll Informat Sci & Engn Inst Artificial Intelligence & Robots Shenyang Liaoning Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Liaoning Peoples R China Shandong Nanshan Aluminum Ind Co Ltd Res Inst Yantai Peoples R China

We propose an unsupervised segmentation method based on simple non-iterative clustering (SNIC) and adaptive density-based spatial clustering of applications with noise (DBSCAN). The method is not sensitive to parameter settings. And cluster parameter suitable for each image can be automatically calculated. SNIC superpixel segmentation is applied in achieving over-segmented images to solve the problem of the image resolution being too high. Then, adaptive DBSCAN clustering is proposed to cluster the over-segmented superpixel blocks to solve the problem of over-segmentation and manual adjustment of DBSCAN parameters. Finally, k-means and connected regions are used for postprocessing to remove the shadow superpixel blocks from the clustered image and to ensure the integrity of a single microstructure. The effectiveness of this method is proved by many experiments. Based on this method, we provide a fast labeling method to help experts quickly label metallographic images. (C) 2019 SPIE and IS&T

关键词： aluminum alloy microstructure unsupervised segmentation simple non-iterative clustering density-based spatial clustering of applications with noise adaptive parameter

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