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Urban Area Mapping Using Multitemporal SAR images in Combination with Self-Organizing Map Clustering and object-based image analysis

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REMOTE SENSING 2023年第1期15卷 122-122页

作者： Amitrano, Donato Di Martino, Gerardo Iodice, Antonio Riccio, Daniele Ruello, Giuseppe Italian Aerosp Res Ctr Via Maiorise snc I-81043 Capua Italy Univ Napoli Federico II Dept Elect Engn & Informat Technol Via Claudio 21 I-80125 Naples Italy

Mapping urban areas from space is a complex task involving the definition of what should be considered as part of an urban agglomerate beyond the built-up features, thus modelling the transition of a city into the surrounding landscape. In this paper, a new technique to map urban areas using multitemporal synthetic aperture radar data is presented. The proposed methodology exploits innovative RGB composites in combination with self-organizing map (SOM) clustering and object-based image analysis. In particular, the clustered product is used to extract a coarse urban area map, which is then refined using object-based processing. In this phase, Delaunay triangulation and the spatial relationship between the identified urban regions are used to model the urban-rural gradient between a city and the surrounding landscape. The technique has been tested in different scenarios representative of structurally different cities in Italy and Germany. The quality of the obtained products is assessed by comparison with the Urban Atlas of the European Environmental Agency, showing good agreement with the adopted reference data despite their different taxonomies.

关键词： synthetic aperture radar urban area object-based image analysis self-organizing maps classification mapping

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Using object-based image analysis to detect laughing gull nests

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GISCIENCE & REMOTE SENSING 2021年第8期58卷 1497-1517页

作者： Martini, Benjamin F. Miller, Douglas A. USDA Wildlife Serv New York NY 10278 USA Penn State Univ Dept Ecosyst Sci & Management University Pk PA 16802 USA

Remote sensing has long been used to study wildlife;however, manual methods of detecting wildlife in aerial imagery are often time-consuming and prone to human error, and newer computer vision techniques have not yet been extensively applied to wildlife surveys. We used the object-based image analysis (OBIA) software eCognition to detect laughing gull (Leucophaeus atricilla) nests in Jamaica Bay as part of an ongoing monitoring effort at the John F. Kennedy International Airport. Our technique uses a combination of high resolution 4-band aerial imagery captured via manned aircraft with a multispectral UltraCam Falcon M2 camera, LiDAR point cloud data, and land cover data derived from a bathymetric LiDAR point cloud to classify and extract laughing gull nests. Our ruleset uses the site (topographic position of nest objects), tone (spectral characteristic of nest objects), shape, size, and association (nearby objects commonly found with the objects of interest that help identify them) elements of image interpretation, as well as NDVI and a sublevel object examination to classify and extract nests. The ruleset achieves a producer's accuracy of 98% as well as a user's accuracy of 65% and a kappa of 0.696, indicating that it extracts a majority of the nests in the imagery while reducing errors of commission to only 35% of the final results. The remaining errors of commission are difficult for the software to differentiate without also impacting the number of nests successfully extracted and are best addressed by a manual verification of output results as part of a semi-automated workflow in which the OBIA is used to complete the initial search of the imagery and the results are then systematically verified by the user to remove errors. This eliminates the need to manually search entire sets of imagery for nests, resulting in a much more efficient and less error prone methodology than previous unassisted image interpretation techniques. Because of the extensibility of OBI

关键词： Feature extraction laughing gull Leucophaeus atricilla object-based image analysis eCognition wildlife surveying

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Three-dimensional building change detection using object-based image analysis (case study: Tehran)

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APPLIED GEOMATICS 2021年第3期13卷 325-332页

作者： Tabib Mahmoudi, Fatemeh Hosseini, Sharareh Shahid Rajaee Teacher Training Univ Civil Engn Fac Dept Geomat Engn Tehran Iran

Natural disasters such as earthquakes and floods together with the urban sprawl conducted by increasing the population make multi-temporal changes in building areas. Destruction, buildings' renovation, and constructing new buildings are the main changes of the urban areas that should be detected to update three-dimensional city models. The results of performing three-dimensional changes detecting of high altitude objects such as buildings are more close to reality than the two-dimensional methods. In this study, a three-dimensional changes detection method is proposed based on digital elevation models (DEMs). In the first step of this proposed method, the normalized digital surface model (nDSM) is generated for timely datasets. Then, object-based image analysis is utilized by performing segmentation followed by the structural classification of DEMs. Differencing and comparing the multi-temporal classification maps as the third step of the proposed algorithm led to analyzing the occurred changes. The obtained results are evaluated in an urban area in Tehran, Iran, in a 9-year time interval. These results represent -9.7% decreasing rate in low-rise buildings and also -1.37% decreases in the ground. Moreover, the class of high-rise buildings increased for +16.4% which conforms to making new constructions in addition to the renovation of low-rise buildings. According to the area analyzing of the changes, 4.8% of the investigated study area has new constructions, 3.05% has buildings' renovation, and 3.89% has destruction in that 9-year period.

关键词： Three-dimensional change detection Normalized DSM object-based image analysis Segmentation

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object-based image analysis approach for vessel detection on optical and radar images

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JOURNAL OF APPLIED REMOTE SENSING 2019年第1期13卷

作者： Aiello, Martina Vezzoli, Renata Gianinetto, Marco Politecn Milan Dept Architecture Built Environm & Construct Engn Milan Italy

Commercial satellites for Earth observation can integrate conventional positioning and tracking systems for monitoring legal and illegal activities by sea, in order to effectively detect and prevent events threatening human life and environment. This study describes an object-oriented approach to detect vessels combining high- and medium-resolution optical and radar images. Once detected, the algorithm estimates their position, length, and heading and assigns a speed range. Tests are done using WorldView-2, QuickBird, GeoEye-1, Sentinel-2A, COSMO-SkyMed, and Sentinel-1 data imaged in several test sites including China, Australia, Italy, Hong Kong, and the western Mediterranean Sea. Validation of results with data from the automatic identification system shows that the estimates for length and heading have R-2 = 0.85 and R-2 = 0.92, respectively. Tests for evaluating speed from Sentinel-2 time-lag image displacement show encouraging results, with 70% of estimates' residuals within +/- 2 m/s. Finally, our method is compared to the state-of-the-art search for unidentified maritime object (SUMO), provided by the European Commission's Joint Research Centre. Finally, our method is compared to the state-of-the-art SUMO. Tests with Sentinel-1 data show similar results in terms of correct detections. Nevertheless, our method returns a smaller number of false alarms compared to SUMO. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： optical images synthetic aperture radar images object-based image analysis ship detection

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Comparison of pixel- and object-based image analysis for tea plantation mapping using hyperspectral Gaofen-5 and synthetic aperture radar data

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JOURNAL OF APPLIED REMOTE SENSING 2020年第4期14卷

作者： Chen, Yujia Tian, Shufang China Univ Geosci Beijing Sch Earth Sci & Resources Beijing Peoples R China

Accurately mapping tea plantation distribution is crucial to environmental protection and sustainable development. Hyperspectral and synthetic aperture radar (SAR) data have recently been widely used in land cover classification, but their ability to extract tea plantation regions still needs to be confirmed. Compared with traditional pixel-based image analysis (PBIA), object-based image analysis (OBIA) for tea plantation mapping is more worthy of implementation. This study explored the performance of Gaofen-5 (GF-5) and copolarized SAR data for tea plantation mapping using pixel- and object-based support vector machine algorithms in Wuyishan, China. Comparison of PBIA and OBIA demonstrated the significant differences in visual effect and classification accuracy. The object-based classifications especially offered a more contiguous depiction with fewer speckles of tea plantations than pixel-based classifications did. Moreover, object-based classifications improved overall accuracy (OA) between 1.7% and 7.9% in all scenarios when compared to pixel-based classifications. As for datasets, classifications using only GF-5 data obtained an OA of over 85%, while fusing images decreased classification accuracy due to the lower separability between tea and forest, showcasing that the fusion of hyperspectral and SAR data does not guarantee the improvement of classification accuracy. The integration of GF-5, horizontal transmit and horizontal receive (HH), and vertical transmit and vertical receive (VV) polarized data outperformed other data combinations in both pixel- and object-based classifications and achieved the highest OA (95.58%) in OBIA, with a 98.13% producer accuracy and 93.34% user accuracy of tea plantations. The results indicated OBIA could overcome the shortcomings of the PBIA and effectively improve the mapping accuracy of tea plantation, and OBIA integrating GF-5, HH, and VV polarized data could play a distinguished role in tea plantation mapping. This work pr

关键词： tea plantation mapping hyperspectral copolarized data image fusion object-based image analysis

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Detailed Lacustrine Calving Iceberg Inventory from Very High Resolution Optical imagery and object-based image analysis

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REMOTE SENSING 2020年第11期12卷 1807-1807页

作者： Podgorski, Julian Petlicki, Michal Polish Acad Sci Inst Geophys Ul Ksiecia Janusza 64 PL-01452 Warsaw Poland Ctr Estudios Cient Av Prat 514 Valdivia 5110466 Chile

In the field of iceberg and glacier calving studies, it is important to collect comprehensive datasets of populations of icebergs. Particularly, calving of lake-terminating glaciers has been understudied. The aim of this work is to present an object-based method of iceberg detection and to create an inventory of icebergs located in a proglacial lagoon of San Quintin glacier, Northern Patagonia Icefield, Chile. This dataset is created using high-resolution WorldView-2 imagery and a derived DEM. We use it to briefly discuss the iceberg size distribution and area-volume scaling. Segmentation of the multispectral imagery produced a map of objects, which were classified with use of Random Forest supervised classification algorithm. An intermediate classification product was corrected with a ruleset exploiting contextual information and a watershed algorithm that was used to divide multiple touching icebergs into separate objects. Common theoretical heavy-tail statistical distributions were tested as descriptors of iceberg area and volume distributions. Power law models were proposed for the area-volume relationship. The proposed method performed well over the open lake detecting correctly icebergs in all size bands except 5-15 m(2) where many icebergs were missed. A section of the lagoon with ice melange was not reliably mapped due to uniformity of the area in the imagery and DEM. The precision of the DEM limited the scaling effort to icebergs taller than 1.7 m and larger than 99 m(2), despite the inventory containing icebergs as small as 4 m(2). The work demonstrates viability of object-based analysis for lacustrine iceberg detection and shows that the statistical properties of iceberg population at San Quintin glacier match those of populations produced by tidewater glaciers.

关键词： object-based image analysis WorldView-2 glacier calving lacustrine calving Northern Patagonia Icefield iceberg power law random forest

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object-based image analysis: a review of developments and future directions of automated feature detection in landscape archaeology

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ARCHAEOLOGICAL PROSPECTION 2019年第2期26卷 155-163页

作者： Davis, Dylan S. Penn State Univ Dept Anthropol University Pk PA 16802 USA

object-based image analysis (OBIA) is a method of assessing remote sensing data that uses morphometric and spectral parameters simultaneously to identify features in remote sensing imagery. Over the past 10-15 years, OBIA methods have been introduced to detect archaeological features. Improvements in accuracy have been attained by using a greater number of morphometric variables and multiple scales of analysis. This article highlights the developments that have occurred in the application of OBIA within archaeology and argues that OBIA is both a useful and necessary tool for archaeological research. Additionally, I discuss future research paths using this method. Some of the suggestions put forth here include: pushing for multifaceted research designs utilizing OBIA and manual interpretation, using OBIA methods for directly studying landscape settlement patterns, and increasing data sharing of methods between researchers.

关键词： automated feature extraction landscape analysis machine learning object-based image analysis pattern recognition remote sensing

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Integrating spectral variability and spatial distribution for object-based image analysis using curve matching approaches

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2020年 169卷 320-336页

作者： Tang, Yunwei Qiu, Fang Jing, Linhai Shi, Fan Li, Xiao Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci 9 South Rd Beijing 100094 Peoples R China Univ Texas Dallas Geospatial Informat Sci 800 West Campbell Rd Richardson TX 75080 USA

object-based image analysis (OBIA) has been widely used to classify high spatial resolution (HSR) imagery. In a traditional OBIA, object-level statistical summaries such as mean values are usually used for classification. This implies that the spectral values within objects follow a Gaussian distribution. However, the pixel values in an object do not necessarily conform to a Gaussian distribution because of within object spectral heterogeneity. Consequently, these statistical summaries may misrepresent the features of the object. This shortcoming is addressed in this paper by integrating both the spectral variability and the spatial distribution of the pixels within objects to improve the traditional object-based image classification. The spectral variability is represented by histograms of the pixel values in the object, and the spatial distribution is characterized by the binary spatial covariogram of these pixels. To construct a binary spatial covariogram, a principal component analysis (PCA) is first applied to compress multiple bands into one, and the Otsu thresholding is then performed to generate a binary map reflecting the spatial configuration of the pixels. Spatial covariance is then computed for this binary map and plotted with different lag distances to derive the binary spatial covariogram. Our proposed model utilizing curves composed of the spectral histograms and binary spatial covariogram (referred to as the His-Cov model) are then used for classification based on curve matching approaches. The integration of spectral variability and spatial distribution of the pixels in the object produced superior results to curve matching approaches based on spectral variability alone and to traditional OBIA based on spectral and spatial features of the objects when classifying complex land use types in urban environments.

关键词： object-based image analysis Curve matching His-Coy Spatial covariogram Land cover/land use classification High resolution

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High-resolution broad-scale mapping of soil parent material using object-based image analysis (OBIA) of LiDAR elevation data

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CATENA 2020年第0期188卷 104422-000页

作者： Prince, Antoine Franssen, Jan Lapierre, Jean-Francois Maranger, Roxane Univ Montreal Geog Dept 520 Chemin Cote St Catherine Montreal PQ H2V 2B8 Canada Univ Montreal Biol Sci Dept Pavillon Marie Victorin Montreal PQ H3C 3J7 Canada

Precise mapping of subsurface properties, especially soil parent material (SPM), is critical to understanding most geomorphic and hydrologic processes. Because spatial variability of the subsurface properties is highly correlated to the morphology of the earth's surface, DEMs and aerial/satellite imagery have been used successfully for lithological mapping. However, most of the studies using this approach have used either coarse-resolution DEMs or covered limited areas. LiDAR-derived topographic attributes may provide an opportunity for finer-resolution mapping of subsurface properties at much larger spatial scales. Here, we present an approach that uses object-based image analysis (OBIA) - the process of clustering pixels into homogeneous objects - paired with a classification tree (CT) algorithm to map SPM at 1 m resolution and at a regional spatial scale using LiDAR-derived topographic attributes. Combining OBIA of topographic attributes with the CT method proved to be an efficient approach for mapping of SPM at fine resolution and broad scales. Using field-acquired SPM samples, we found that this approach was able to explain 73.5% of the variance of three SPM classes (i.e., bedrock, glacial till, sands) using only four common topographic metrics (i.e., slope, topographic wetness index, terrain ruggedness index, multi-resolution topographic positioning index). When comparing our approach to other classification methods and to other studies that have mapped SPM, we obtain the highest overall accuracy with a value of 85%. This approach allowed us to produce a fine-resolution 185 km(2) SPM map in a heterogeneous post-glaciation Precambrian Shield setting.

关键词： Soil parent material object-based image analysis LiDAR Digital mapping Classification tree Terrain analysis

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object-based image analysis of Ground-Penetrating Radar Data for Archaic Hearths

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REMOTE SENSING 2020年第16期12卷 2539-2539页

作者： Cornett, Reagan L. Ernenwein, Eileen G. East Tennessee State Univ Dept Geosci Johnson City TN 37614 USA

object-based image analysis (OBIA) has been increasingly used to identify terrain features of archaeological sites, but only recently to extract subsurface archaeological features from geophysical data. In this study, we use a semi-automated OBIA to identify Archaic (8000-1000 BC) hearths from Ground-Penetrating Radar (GPR) data collected at David Crockett Birthplace State Park in eastern Tennessee in the southeastern United States. The data were preprocessed usingGPR-SLICE,Surfer,andArchaeofusionsoftware, and amplitude depth slices were selected that contained anomalies ranging from 0.80 to 1.20 m below surface (BS). Next, the data were segmented withinESRI ArcMapGIS software using a global threshold and, after vectorization, classified using four attributes: area, perimeter, length-to-width ratio, and Circularity Index. The user-defined parameters were based on an excavated Archaic circular hearth found at a depth greater than one meter, which consisted of fire-cracked rock and had a diameter greater than one meter. These observations were in agreement with previous excavations of hearths at the site. Features that had a high probability of being Archaic hearths were further delineated by human interpretation from radargrams and then ground-truthed by auger testing. The semi-automated OBIA successfully predicted 15 probable Archaic hearths at depths ranging from 0.85 to 1.20 m BS. Observable spatial clustering of hearths may indicate episodes of seasonal occupation by small mobile groups during the Archaic Period.

关键词： archaeology archeological prospection near-surface geophysics object-based image analysis remote sensing

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