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object-based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery

REMOTE SENSING OF ENVIRONMENT 2004年第3期92卷 409-413页

作者： Gitas, IZ Mitri, GH Ventura, G Aristotle Univ Thessaloniki Dept Forestry Lab Forest Management & Remote Sensing GR-54124 Thessaloniki Greece Mediterranean Agron Inst Chania Dept Environm Management GR-73100 Iraklion Greece Univ Alcala de Henares Dept Geog Madrid 28801 Spain

Due to the ability of the NOAA-AVHRR sensor to cover a wide area and its high temporal frequency, it is possible to quickly obtain a general overview of the prevailing situation over a large area of terrain and, more specifically, quickly assess the damage caused by a recent large forest fire by mapping the extent of the burned area. The aim of this work was to map a large forest fire that recently took place on the Spanish Mediterranean coast using innovative image classification techniques and low spatial resolution imagery. The methodology involved developing an object-based classification model using spectral as well as contextual object information. The burned area map resulting from the image classification was compared with the fire perimeter provided by the Catalan Environmental Department in terms of spatial overlap and size in order to determine to what extent they were compatible. Results of the comparison indicated a high degree (approximate to 90%) of spatial agreement. The total burned area of the classified image was found to be 6900 ha, compared to a fire perimeter of 6000 ha produced by the Catalan Environmental Department. It was concluded that, although the object-oriented classification approach was capable of affording very promising results when mapping a recent burn on the Spanish Mediterranean coast, the method in question required further assessment to ascertain its ability to map other burned areas in the Mediterranean. (C) 2004 Elsevier Inc. All rights reserved.

关键词： NOAA-AVHRR object-based image classification burned area mapping

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object-based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery

Object-based image classification for burned area mapping of...

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Workshop on Fire Prevention and Assessment

关键词： NOAA-AVHRR object-based image classification burned area mapping

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Automated extraction of mining-induced ground fissures using deep learning and object-based image classification

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EARTH SURFACE PROCESSES AND LANDFORMS 2024年第7期49卷 2189-2204页

作者： Huangfu, Wenchao Qiu, Haijun Cui, Peng Yang, Dongdong Liu, Ya Ullah, Mohib Kamp, Ulrich Northwest Univ Coll Urban & Environm Sci Xian 710127 Peoples R China Northwest Univ Inst Earth Surface Syst & Hazards Xian Peoples R China Northwest Univ Shaanxi Key Lab Earth Surface Syst & Environm Carr Xian Peoples R China Chinese Acad Sci Inst Mt Hazards & Environm Chengdu Peoples R China Univ Michigan Dearborn Dept Nat Sci Earth & Environm Sci Discipline Dearborn MI USA

Accurate extraction of ground fissures caused by intense coal mining has the potential to significantly improve the efficiency of environmental monitoring in mining areas. However, the extraction results using previous methods have often exhibited issues of discontinuity and substantial deviation from ground truth data, resulting in low extraction accuracy. In this study, a novel approach, ENVINet5-OBIC, for extracting ground fissures in mining areas is proposed, which integrates object-based image classification (OBIC) with the pixel-based deep learning model ENVINet5. ENVINet5-OBIC uses OBIC to segment high-resolution unmanned aerial vehicle (UAV) images across different scales, effectively considering shape, texture and correlative information between adjacent pixels. Furthermore, by utilizing homogeneous objects as building blocks, it establishes a deep learning model for the automated extraction of ground fissures. Experimental results show that ENVINet5-OBIC performs better when compared with OBIC, U-Net, PSPNet and ENVINet5 methods in terms of continuity, accuracy and error reduction. In addition, the ground fissure area extracted by ENVINet5-OBIC closely aligns with ground truth data. This study provides a more effective method for automatic extraction of ground fissures, which improves the efficiency of environmental monitoring in mining areas. This research proposes the ENVINet5-OBIC approach, which combines the deep learning model ENVINet5 with object-based image classification (OBIC). ENVINet5-OBIC has better performance in ground fissure extraction when compared with OBIC, U-Net, PSPNet and ENVINet5 methods in terms of continuity, accuracy and error reduction. image

关键词： automatic extraction deep learning ground fissure object-based image classification UAV image

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PRELIMINARY RESULTS OF EARTHQUAKE-INDUCED BUILDING DAMAGE DETECTION WITH object-based image classification 23

PRELIMINARY RESULTS OF EARTHQUAKE-INDUCED BUILDING DAMAGE DE...

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23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS)

作者： Sabuncu, A. Avci, Z. D. Uca Sunar, F. Bogazici Univ Kandilli Observ Istanbul Turkey Bogazici Univ Earthquake Res Inst Geodesy Dept Istanbul Turkey Istanbul Tech Univ Dept Astronaut Engn Fac Aeronaut & Astronaut Istanbul Turkey Istanbul Tech Univ Geomat Engn Dept Istanbul Turkey

Earthquakes are the most destructive natural disasters, which result in massive loss of life, infrastructure damages and financial losses. Earthquake-induced building damage detection is a very important step after earthquakes since earthquake induced building damage is one of the most critical threats to cities and countries in terms of the area of damage, rate of collapsed buildings, the damage grade near the epicenters and also building damage types for all constructions. Van-Ercis (Turkey) earthquake (Mw=7.1) was occurred on October 23th, 2011;at 10:41 UTC (13:41 local time) centered at 38.75 N 43.36 E that places the epicenter about 30 kilometers northern part of the city of Van. It is recorded that, 604 people died and approximately 4000 buildings collapsed or seriously damaged by the earthquake. In this study, high-resolution satellite images of Van-Ercis, acquired by Quickbird-2 ((C) Digital Globe Inc.) after the earthquake, were used to detect the debris areas using an object-based image classification. Two different land surfaces, having homogeneous and heterogeneous land covers, were selected as case study areas. As a first step of the object-based image processing, segmentation was applied with a convenient scale parameter and homogeneity criterion parameters. As a next step, condition based classification was used. In the final step of this preliminary study, outputs were compared with streetview/ortophotos for the verification and evaluation of the classification accuracy.

关键词： Ercis-Van Earthquake object-based image classification Building Damage Detection

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A Study on the object-based High-Resolution Remote Sensing image classification of Crop Planting Structures in the Loess Plateau of Eastern Gansu Province

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REMOTE SENSING 2024年第13期16卷 2479页

作者： Yang, Rui Qi, Yuan Zhang, Hui Wang, Hongwei Zhang, Jinlong Ma, Xiaofang Zhang, Juan Ma, Chao Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Key Lab Remote Sensing Gansu Prov Lanzhou 730000 Peoples R China

The timely and accurate acquisition of information on the distribution of the crop planting structure in the Loess Plateau of eastern Gansu Province, one of the most important agricultural areas in Western China, is crucial for promoting fine management of agriculture and ensuring food security. This study uses multi-temporal high-resolution remote sensing images to determine optimal segmentation scales for various crops, employing the estimation of scale parameter 2 (ESP2) tool and the Ratio of Mean Absolute Deviation to Standard Deviation (RMAS) model. The Canny edge detection algorithm is then applied for multi-scale image segmentation. By incorporating crop phenological factors and using the L1-regularized logistic regression model, we optimized 39 spatial feature factors-including spectral, textural, geometric, and index features. Within a multi-level classification framework, the Random Forest (RF) classifier and Convolutional Neural Network (CNN) model are used to classify the cropping patterns in four test areas based on the multi-scale segmented images. The results indicate that integrating the Canny edge detection algorithm with the optimal segmentation scales calculated using the ESP2 tool and RMAS model produces crop parcels with more complete boundaries and better separability. Additionally, optimizing spatial features using the L1-regularized logistic regression model, combined with phenological information, enhances classification accuracy. Within the OBIC framework, the RF classifier achieves higher accuracy in classifying cropping patterns. The overall classification accuracies for the four test areas are 91.93%, 94.92%, 89.37%, and 90.68%, respectively. This paper introduced crop phenological factors, effectively improving the extraction precision of the shattered agricultural planting structure in the Loess Plateau of eastern Gansu Province. Its findings have important application value in crop monitoring, management, food security and other relat

关键词： Gaofen images object-based image classification random forest convolutional neural network crop classification

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A method on mapping the distribution of photovoltaic power stations in complex terrain regions

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RENEWABLE ENERGY 2025年 247卷

作者： Mao, Hongzhi Liu, Weili Chen, Xie Tian, Zhiyong Zarrella, Angelo Luo, Yongqiang Fan, Jianhua Wu, Wentao Huazhong Univ Sci & Technol Sch Environm Sci & Engn Hubei Key Lab Multimedia Pollut Cooperat Control Y 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China Univ Padua Dept Ind Engn Via Venezia 1 I-35131 Padua Italy Tech Univ Denmark Dept Civil & Mech Engn Brovej 118 DK-2800 Lyngby Denmark Univ Canterbury Dept Civil & Nat Resources Engn Christchurch New Zealand

With the goal of achieving carbon neutrality, the number of new photovoltaic (PV) installations worldwide has surged in recent years. Accurate statistics on the area and distribution of centralized solar PV installations are essential. Due to land use constraints and poverty alleviation policies, an increasing number of PV stations are being installed in mountainous areas in China. The fragmented distribution of these PV stations presents challenges for remote sensing identification. This study proposes a method based on object-based image Analysis (OBIA) and the Cascade Random Forest Classifier, utilizing Sentinel-1/2 imagery. The method is capable of extracting PV stations across diverse terrains, including mountains, plateaus, and plains. Specifically, five different scenarios with varying feature combinations (including spectral, index, geometric, and texture features from Sentinel-1 and Sentinel-2) were compared. A second-level random forest classifier, trained on false-positive samples, was employed to enhance the model's robustness. The results indicate that the spectral features of Sentinel-1, along with index and geometric features, positively influence identification accuracy, while texture features showed less impact. Independent test results demonstrated an overall classification accuracy of 99.9904 %, with a producer accuracy of 92.73 % and a user accuracy of 81.38 % for PV stations in a complete city region. Finally, a 2024 map of centralized PV installations was created for five cities west of Beijing, where the proportion of total PV area in mountainous regions is 45 %. This method represents the first approach capable of identifying PV stations in complete regions with complex terrain, including mountains, without the need for manual post-processing.

关键词： Photovoltaic stations object-based image classification Sentinel-1/2 Complex terrains

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Spatiotemporal changes of glacier and seasonal snow fluctuations over the Namcha Barwa-Gyala Peri massif using object-based classification from Landsat time series

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2021年 177卷 21-37页

作者： Guo, Shanchuan Du, Peijun Xia, Junshi Tang, Pengfei Wang, Xin Meng, Yaping Wang, Hua Nanjing Univ Sch Geog & Ocean Sci Jiangsu Prov Key Lab Geog Informat Sci & Technol Minist Nat ResourcesKey Lab Land Satellite Remot Nanjing 210023 Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Jiangsu Peoples R China RIKEN Ctr Adv Intelligence Project AIP Geoinformat Unit Tokyo 1030027 Japan Nyingchi Meteorol Serv Nyingchi 860000 Tibet Peoples R China

The glacier environment in the Namcha Barwa-Gyala Peri (NBGP) massif is regarded as one of the most sensitive areas to climate change, yet the change estimates remain inadequate due to the limited knowledge of the complex debris-covered glacier environment and the lack of available remote sensing data caused by continuously cloudy weather. To examine the changes of the complex glacial environment, a multiple hierarchical object- and rule-based classification (MHORC) scheme is proposed by combining Landsat time series images and topographic data. object-based image analysis (OBIA) is introduced due to its capabilities of handling data contextually and hierarchically and using multi-source data jointly so that multiple hierarchical rule-based classification can be developed to distinguish the objects with similar spectrum such as debris-covered glaciers and their surrounding terrain. All the available Landsat images from 1987 to 2019 are filtered by the cloud cover condition (less than 10%) and accordingly used in the MHORC to obtain spatial and temporal information of the glacier and snow over the NBGP massif. The images collected in the snow-free season are used to estimate the glacier extent state and the historical changes, and those in the snow-covered are used to estimate the snow cover fluctuation using harmonic analysis. The results show that the NBGP contains 65 glaciers(>= 0.1 km(2)) with a total area of 462.7 km(2), among which valley glaciers account for 21.5% in number but cover an area of 354.2 km(2), more than 76% of the total area. An accelerating reduction trend of the glacier area is confirmed as the area change velocity is -0.62 km(2) yr(-1) between 1999 and 2003 and reach -2.95 km(2) yr(-1) between 2003 and 2015. The seasonal snow cover area shows a reduction in the secular trend and an increase in fluctuation amplitude. Both glacier and seasonal snow display heterogeneity in the spatial and temporal patterns. Such heterogeneous dynamics might be r

关键词： Glacial environment changes object-based image classification Landsat time series Google Earth Engine The Namcha Barwa-Gyala Peri massif

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Evaluation of Automatic Building Detection Approaches Combining High Resolution images and LiDAR Data

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REMOTE SENSING 2011年第6期3卷 1188-1210页

作者： Hermosilla, Txomin Ruiz, Luis A. Recio, Jorge A. Estornell, Javier Univ Politecn Valencia Geoenvironm Cartog & Remote Sensing Res Grp Valencia 46022 Spain

In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered.

关键词： building detection LiDAR high spatial resolution imagery object-based image classification

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Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery

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JOURNAL OF APPLIED REMOTE SENSING 2018年第2期12卷 025010-1-025010-21页

作者： Fu, Tengyu Ma, Lei Li, Manchun Johnson, Brian A. Nanjing Univ Jiangsu Prov Key Lab Geog Informat Sci & Technol Nanjing Jiangsu Peoples R China Nanjing Univ Sch Geog & Oceanog Sci Nanjing Jiangsu Peoples R China Inst Global Environm Strategies Nat Resources & Ecosyst Serv Area Hayama Kanagawa Japan

Convolutional neural network (CNN) has shown great success in computer vision tasks, but their application in land-use type classifications within the context of object-based image analysis has been rarely explored, especially in terms of the identification of irregular segmentation objects. Thus, a blocks-based object-based image classification (BOBIC) method was proposed to carry out end-to-end classification for segmentation objects using CNN. Specifically, BOBIC takes advantage of CNN to automatically extract complex features from the original image data, thereby avoiding the uncertainty caused by the manual extraction of features in OBIC. Additionally, OBIC compensates for the shortcomings of CNN whereby it is difficult to delineate a clear right boundary for ground objects at the pixel level. Using three high-resolution test images, the proposed BOBIC was compared with support vector machine (SVM) and random forest (RF) classifiers, and then, the effect of image blocks and mixed objects on classification accuracy was evaluated for the proposed BOBIC. Compared with conventional SVM and RF classifiers, the inclusion of CNN improved the OBIC classification performance substantially (5% to 10% increases in overall accuracy), and it also alleviated the effect derived from mixed objects. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

关键词： object-based image classification convolutional neural network multiresolution segmentation irregular segmented object image block

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Attentive Spatial Temporal Graph CNN for Land Cover Mapping From Multi Temporal Remote Sensing Data

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IEEE ACCESS 2021年 9卷 23070-23082页

作者： Censi, Alessandro Michele Ienco, Dino Gbodjo, Yawogan Jean Eudes Pensa, Ruggero Gaetano Interdonato, Roberto Gaetano, Raffaele Univ Montpellier UMR TETIS INRAE F-34000 Montpellier France Univ Turin Dept Comp Sci I-10124 Turin Italy CIRAD UMR TETIS F-34090 Montpellier France

Satellite image time series (SITS) collected by modern Earth Observation (EO) systems represent a valuable source of information that supports several tasks related to the monitoring of the Earth surface dynamics over large areas. A main challenge is then to design methods able to leverage the complementarity between the temporal dynamics and the spatial patterns that characterize these data structures. Focusing on land cover classification (or mapping) tasks, the majority of approaches dealing with SITS data only considers the temporal dimension, while the integration of the spatial context is frequently neglected. In this work, we propose an attentive spatial temporal graph convolutional neural network that exploits both spatial and temporal dimensions in SITS. Despite the fact that this neural network model is well suited to deal with spatio-temporal information, this is the first work that considers it for the analysis of SITS data. Experiments are conducted on two study areas characterized by different land cover landscapes and real-world operational constraints (i.e., limited labeled data due to acquisition costs). The results show that our model consistently outperforms all the competing methods obtaining a performance gain, in terms of F-Measure, of at least 5 points with respect to the best competing approaches on both benchmarks.

关键词： Time series analysis Task analysis Satellites image segmentation Data models Earth Convolutional neural networks Spatial temporal graph convolutional neural network attention-based neural network object-based image classification satellite image time series land cover classification deep learning

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