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multi-resolution segmentation parameters optimization and evaluation for VHR remote sensing image based on meanNSQI and discrepancy measure

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JOURNAL OF SPATIAL SCIENCE 2021年第2期66卷 253-278页

作者： Chen, Yunhao Chen, Qiang Jing, Changfeng Beijing Normal Univ State Key Lab Earth Surface Proc & Resource Ecol Beijing Peoples R China Beijing Univ Civil Engn & Architecture Beijing Adv Innovat Ctr Future Urban Design Beijing Peoples R China Beijing Univ Civil Engn & Architecture Sch Geomat & Urban Spatial Informat Beijing Peoples R China

multi-resolution segmentation (MRS) is known to be a general segmentation algorithm for very-high-resolution (VHR) remote sensing applications. The critical problems of MRS are the optimization of the parameters and the evaluation of segmentation quality. Based on the principle of maximizing the intra-object homogeneity and inter-object heterogeneity, we propose a novel Normalized segmentation Quality Index (NSQI) and use level filtering to acquire the optimal parameters of the MRS algorithm. Using the geometric and arithmetic discrepancy between the segmented object and the reference object as the evaluation criterion, we then evaluate the quality of the segmented objects. The results of two experiments confirm the effectiveness of our meanNSQI and discrepancy measure approach. Additionally, a sensitivity analysis of the segmentation quality index demonstrates the reliability of the meanNSQI and the discrepancy measure.

关键词： multi-resolution segmentation discrepancy measure very high resolution remote sensing parameters optimization

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Generation of Homogeneous Slope Units Using a Novel Object-Oriented multi-resolution segmentation Method

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WATER 2021年第23期13卷 3422页

作者： Li, Yange He, Jianhua Chen, Fang Han, Zheng Wang, Weidong Chen, Guangqi Huang, Jianling Cent South Univ Sch Civil Engn Changsha 410075 Peoples R China Minist Educ Key Lab Engn Struct Heavy Haul Railway Changsha 410075 Peoples R China Hunan Prov Key Lab Disaster Prevent & Mitigat Rai Changsha 410075 Peoples R China Kyushu Univ Dept Civil Engn Fukuoka 8190395 Japan

The generation of map units is a fundamental step for an appropriate assessment of landslide susceptibility. Recent studies have indicated that the terrain relief-based slope units perform better in homogeneity compared with the grid units. However, it is difficult at present to generate high-precision and high-matching slope units by traditional methods. The problem commonly concentrates in the plain areas without obvious terrain reliefs and the junction of sudden changes in terrain. In this paper, we propose a novel object-oriented segmentation method for generating homogeneous slope units. Herein, the multi-resolution segmentation algorithm in the image processing field is introduced, enabling the integration of terrain boundary conditions and image segmentation conditions in slope units. In order to illustrate the performances of the proposed method, Kitakyushu region in Japan is selected as a case study. The results show that the proposed method generates satisfactory slope units that satisfactorily reproduce the actual terrain relief, with the best within-unit and between-unit homogeneities compared with the previous methods, in particular at the plain areas. We also verify the effectiveness of the presented method through the sensitivity analysis using different resolutions of digital elevation models (DEMs) data of the region. It is reported that the presented approach is notably advanced in the requirements of the quality of DEM data, as the presented approach is less sensitive to DEM spatial resolution compared with other available methods.

关键词： slope unit object-oriented method multi-resolution segmentation DEM spatial resolution Kitakyushu region

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Application of Taguchi Optimization and ANOVA Statistics in Optimal Parameter Setting of multi-resolution segmentation 9

Application of Taguchi Optimization and ANOVA Statistics in ...

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9th International Conference on Recent Advances in Space Technologies (RAST)

作者： Tonbul, Hasan Kavzoglu, Taskin Gebze Tech Univ Dept Geomat Engn Kocaeli Turkey

ISBN: (纸本)9781538694480

Over the past two decades, object-based image analysis (OBIA) has become an important tool for information extraction from remote sensing images. segmentation parameter estimation and optimization is one of the most important research areas in OBIA studies. However, parameter optimization is an extremely difficult and laborious process for high-quality segmentation. In this paper, Taguchi optimization technique was employed to determine the optimal values of main parameters of multi-resolution segmentation (MRS) (i.e. scale, shape, compactness) using the L-25(3(5)) experimental design. Based on the signal to noise ratio criteria, the best optimum MRS parameters have been determined as 10-0.1-0.9 for scale, shape and compactness, respectively. In addition, analysis of variance (ANOVA) was conducted in order to specify the effects of the MRS parameters considering root mean square (RMS) of over- and under-segmentation. The results showed that the scale was the most dominant factor with the contribution of 57.97% compared with shape and compactness. It was also observed that the Taguchi technique was effective in the optimization of MRS parameters within the reliability interval of 95%.

关键词： ANOVA OBIA multi-resolution segmentation segmentation Quality Taguchi

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Parameter evaluation and optimization for multi-resolution segmentation in object-based shadow detection using very high resolution imagery

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GEOCARTO INTERNATIONAL 2017年第12期32卷 1307-1332页

作者： Luo, Hui Li, Deren Liu, Chong Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan Hubei Peoples R China Wuhan Univ Collaborat Innovat Ctr Geospatial Technol Wuhan Hubei Peoples R China Jiangxi Normal Univ Key Lab Poyang Lake Wetland & Watershed Res Minist Educ Nanchang Jiangxi Peoples R China

Object-based shadow detection in urban areas is an important topic in very high resolution remote sensing image processing. multi-resolution segmentation (MRS) is an effective segmentation method, and is used for object-based shadow detection. However, several input parameters within MRS may result in unstable performance for final shadow detection;thus, the evaluation and optimization for the parameters upon the final shadow detection accuracy cannot be overlooked. In this paper, the three parameters in MRS (scale s, weight of colour w(color) and weight of compactness w(compact)) upon the final result of a recently proposed method, object-based shadow detection with Dempster-Shafer theory, were evaluated and optimized by sensitivity analysis and Taguchi's method with three experimental data. Experiments show that scale s is the most sensitive parameter among the three parameters within MRS. More importantly, according to the Taguchi's method theory, there is a very significant interaction effect between s and w(color), which cannot be overlooked. The shadow detection accuracy yielded by the optimum parameter combination in consideration of the interaction effect is higher than that only optimized by covering the main effect of single parameter in most cases.

关键词： Parameter evaluation and optimization interaction effect orthogonal experimental design object-based shadow detection multi-resolution segmentation

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A Comparative Study of segmentation Quality for multi-resolution segmentation and Watershed Transform 8

A Comparative Study of Segmentation Quality for Multi-resolu...

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8th International Conference on Recent Advances in Space Technologies (RAST)

作者： Kavzoglu, Taskin Tonbul, Hasan Gebze Tech Univ Dept Geomat Engn TR-41400 Kocaeli Turkey

ISBN: (纸本)9781538616055

Object-Based Image Analysis (OBIA) has gained swift popularity in remote sensing area mainly due to the increasing availability of very high resolution imagery. Image segmentation is a major step within OBIA process. Image segmentation quality remarkably influences the subsequent image classification accuracy. It is necessary to implement advanced and robust methods to increase image segmentation quality that is generally measured by several accuracy metrics including Area Fit Index (AFI and Quality Rate (Qr). In this study, two widely-used segmentation algorithms, namely region-based multiresolution segmentation and edge-based watershed transform were applied to a very high resolution imagery acquired by VorldView-2 sensor to evaluate and compare their performance in terms of segmentation quality metrics. Totally five segmentation goodness metrics, namely under-segmentation, over-segmentation, root means square, AFI and Qr were applied through the manually digitized reference objects available on the imagery. ENVI and eCognition Developer software packages were used to perform watershed transform and multiresolution segmentation algorithms, respectively. Nearest neighbor classification method was applied and related accuracy assessment was conducted in two software platforms. Results showed that multi-resolution segmentation was superior (about 18% higher in terms of AFI) compared to watershed transform in the delineation of segments of reference objects. Also, higher classification accuracies (about 5%) were achieved by the use of multi-resolution segmentation.

关键词： Area Fit Index Quality Rate OBIA multi-resolution segmentation segmentation Quality Watershed transform

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multi-resolution remote sensing for the specieslevel classification of mangroves

Multi-resolution remote sensing for the specieslevel classif...

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International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS)

作者： Sanam, Humaira Mathai, Aby K. Lakshmanan, Gnanappazham Indian Inst Space Sci & Technol Earth & Space Sci Thiruvananthapuram India Indian Inst Sci Educ & Res Earth & Climate Sci Pune India

ISBN: (纸本)9798350345421

Remote sensing provides ample scope to map the species of mangroves of swampy and hostile habitats with good accuracy. Our study aims to evaluate and compare the performance of two classification approaches - pixel-based and object-based using a standard and yet robust, supervised, machine learning ensemble classifier - Random Forest on two multispectral satellite images;very high- resolution Worldview-2 (WV2) and medium-resolution Sentinel-2 (S2) of Gorai Creek, West Coast of India, acquired in the same season. A multiresolution segmentation algorithm is used for object-based image analysis (OBIA) based on Gray Level Co-occurrence Matrix (G1), Gray Level Difference Vectors (G2), and a few non-textural features. Random Forest (RF) classifier is applied to their dimensionality-reduced components using Principal Component Analysis (PCA). Pixel-based RF classification is also performed on the selection of homogeneous patches of regions of interest (ROIs) based on Jeffries- Matusita (JM) Distance. The accuracy report for the classification shows that S-2 imagery performs better than WV-2 for pixel- based classification but for objectbased classification, WV-2 was able to extract species information better than S-2.

关键词： mangroves object-based image analysis (OBIA) multi-resolution segmentation Jeffries-Matusita distance Random Forest classifier Gray Level Co-occurrence Matrix Gray Level Difference Vector

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Extraction of Hani terraces based on Sentinel-2 and GF-2 images in Honghe prefecture, Yunnan province

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GEOCARTO INTERNATIONAL 2024年第1期39卷

作者： Lv, Shuang Hong, Liang Yunnan Normal Univ Fac Geog Kunming Peoples R China

Monitoring Hani terraces quickly and accurately using remote sensing technology is crucial for the protecting World Cultural Heritage Sites. However, single remote sensing image is affected by the mutual constraints of temporal and spatial resolution, making it difficult to concurrently integrate key phenological and spatial information for accurate extraction. In this study, GF-2 and Sentinel-2 images are used to extract terraces based on objected-based image analysis. Firstly, the spatial and phenological features of objects were obtained by multi-resolution segmentation using GF-2 image. Secondly, the optimal spatial and key phenological features were optimized by recursive feature elimination cross-validation and separation index, respectively. Finally, all optimized features were adopted to extract terraces using random forest (RF) and support vector machine (SVM) classifiers, respectively. Comparing deep learning and traditional machine learning methods, the proposed method using RF achieved the highest accuracy with Kappa coefficient and overall accuracy of 89.45 and 94.73%, respectively.

关键词： Terrace extraction objected-based image analysis multi-resolution segmentation recursive feature elimination cross-validation separation index deep learning machine learning

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UAV Remote Sensing for Campus Monitoring: A Comparative Evaluation of Nearest Neighbor and Rule-Based Classification

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2021年第3期49卷 527-539页

作者： Tiwari, Anuj Sharma, Surendra Kumar Dixit, Abhilasha Mishra, Vishal Indian Inst Technol Roorkee Dept Civil Engn Roorkee Uttarakhand India Indian Inst Remote Sensing Dehradun Urban & Reg Studies Dept Dehra Dun Uttarakhand India Indian Inst Technol Roorkee Ctr Excellence Disaster Mitigat & Management Roorkee Uttarakhand India

UAV technology when aided with the unique data acquisition strategies, preprocessing techniques and analytical abilities of an established domain of remote sensing provide more affordable, customized and user-friendly option of "UAV-Remote Sensing". This extended branch of remote sensing flourishes in both the mapping and measurement, if implemented in the ordered fashion to ensure remote sensing grade data. The current study integrates the potential of UAV technology to the high-resolution data classification approach of object-based image analysis. Department of Civil Engineering, Indian Institute of Technology-Roorkee, India, is selected as study area. In the first part of the study, a detailed UAV survey followed by UAV data processing was carried out to capture the VHR orthorectified image of the selected study area. In the second step, a comparative assessment of nearest neighbor (NN) and rule-based classifications were performed. Orthorectified image was segmented using a multi-resolution segmentation. The overall accuracy for NN and rule-based classifier were 95.13% and 93.87%, respectively. Detailed assessment of user accuracy and producer accuracy described that tree, road, solar panel and waterbody were more accurately classified with NN classifier, whereas building, grass land, open land and vehicle were more accurately classified with rule-based classifier.

关键词： Very high-resolution image (VHRI) Object-based image analysis (OBIA) Nearest neighbor (NN) Rule-based classifier multi-resolution segmentation

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Object-Oriented Remote Sensing Approaches for the Detection of Terrestrial Impact Craters as a Reconnaissance Survey

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REMOTE SENSING 2023年第15期15卷 3807-3807页

作者： Emmanuel, Habimana Yu, Jaehyung Wang, Lei Choi, Sung Hi Rwatangabo, Digne Edmond Rwabuhungu Chungnam Natl Univ Dept Astron Space Sci & Geol Daejeon 34134 South Korea Univ Rwanda Sch Min & Geol Kigali 3900 Rwanda Chungnam Natl Univ Dept Geol Sci Daejeon 31134 South Korea Louisiana State Univ Dept Geog & Anthropol Baton Rouge LA 70803 USA

The purpose of this study is to employ a remote sensing reconnaissance survey based on optimal segmentation parameters and an object-oriented random forest approach to the identification of possible terrestrial impact craters from the global 30-m resolution SRTM DEM. A dataset consisting of 94 confirmed and well-preserved terrestrial impact craters, 104 volcanic calderas, and 124 valleys were extracted from real-world surface features. For craters with different sizes, eight optimal scale parameters from 80 to 3000 have been identified using multi-resolution segmentation, where the scale parameters have a positive correlation (R-2 = 0.78) with the diameters of craters. The object-oriented random forest approach classified the tested impact craters, volcanic calderas, and valleys with an overall accuracy of 88.4% and a Kappa coefficient of 0.8. The investigated terrestrial impact craters, in general, have relatively lower rim circularity, higher length-to-width ratio, and lower relief, slope, and elevation than volcanic calderas. The topographic characteristics can be explained by geological processes associated with the formation and post-deformation of impact craters. The excavation and ejection by initial impact and rebound of excavated materials contribute to low elevation. The post-impact deformation, including inward collapse and slump of unstable rims, weathering, erosion, and sediment deposition, further reduces elevation and relief and modifies shapes resulting in lower circularity and higher length-to-width ratio. Due to the resolution limitation of the source DEM data and the number of real-world samples, the model has only been validated for craters of 0.88 to 100 km in diameter, which can be generalized to explore undiscovered terrestrial impact craters using cloud computing with global datasets provided by platforms such as Google Earth Engine and Microsoft Planetary Computer.

关键词： impact craters scale parameters multi-resolution segmentation

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Developing a multi-filter convolutional neural network for semantic segmentation using high-resolution aerial imagery and LiDAR data

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2018年 143卷 3-14页

作者： Sun, Ying Zhang, Xinchang Xin, Qinchuan Huang, Jianfeng Sun Yat Sen Univ Dept Geog & Planning Guangzhou 510275 Guangdong Peoples R China Guangzhou Univ Sch Geog Sci Guangzhou 510006 Guangdong Peoples R China Guangdong Key Lab Urbanizat & Geosimulat Guangzhou 510275 Guangdong Peoples R China

Semantic segmentation of LiDAR and high-resolution aerial imagery is one of the most challenging topics in the remote sensing domain. Deep convolutional neural network (CNN) and its derivatives have recently shown the abilities in pixel-wise prediction of remote sensing data. Many existing deep learning methods fuse LiDAR and high-resolution aerial imagery towards an inter-modal mode and thus overlook the intra-modal statistical characteristics. Additionally, the patch-based CNNs could generate the salt-and-pepper artifacts as characterized by isolated and spurious pixels on the object boundaries and patch edges leading to unsatisfied labelling results. This paper presents a semantic segmentation scheme that combines multi-filter CNN and multi-resolution segmentation (MRS). The multi-filter CNN aggregates LiDAR data and high-resolution optical imagery by multi modal data fusion for semantic labelling, and the MRS is further used to delineate object boundaries for reducing the salt-and-pepper artifacts. The proposed method is validated against two datasets: the ISPRS 2D semantic labelling contest of Potsdam and an area of Guangzhou in China labelled based on existing geodatabases. Various designs of data fusion strategy, CNN architecture and MRS scale are analyzed and discussed. Compared with other classification methods, our method improves the overall accuracies. Experiment results show that our combined method is an efficient solution for the semantic segmentation of LiDAR and high-resolution imagery.

关键词： LiDAR High-resolution imagery multi-modal fusion multi-resolution segmentation Semantic segmentation

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