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Object-Based Change Detection in Urban Areas: The Effects of segmentation Strategy, Scale, and Feature Space on Unsupervised Methods

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REMOTE SENSING 2016年第9期8卷 761-761页

作者： Ma, Lei Li, Manchun Blaschke, Thomas Ma, Xiaoxue Tiede, Dirk Cheng, Liang Chen, Zhenjie Chen, Dong Nanjing Univ Jiangsu Prov Key Lab Geog Informat Sci & Technol Nanjing 210023 Jiangsu Peoples R China Salzburg Univ Dept Geoinformat Z GIS Hellbrunner Str 34 A-5020 Salzburg Austria Nanjing Univ Collaborat Innovat Ctr South Sea Studies Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Sch Geog & Oceanog Sci Nanjing 210023 Jiangsu Peoples R China

Object-based change detection (OBCD) has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data. However, some OBCD issues relating to the segmentation of high-resolution images remain to be explored. For example, segmentation units derived using different segmentation strategies, segmentation scales, feature space, and change detection methods have rarely been assessed. In this study, we have tested four common unsupervised change detection methods using different segmentation strategies and a series of segmentation scale parameters on two WorldView-2 images of urban areas. We have also evaluated the effect of adding extra textural and Normalized Difference Vegetation Index (NDVI) information instead of using only spectral information. Our results indicated that change detection methods performed better at a medium scale than at a fine scale where close to the pixel size. Multivariate Alteration Detection (MAD) always outperformed the other methods tested, at the same confidence level. The overall accuracy appeared to benefit from using a two-date segmentation strategy rather than single-date segmentation. Adding textural and NDVI information appeared to reduce detection accuracy, but the magnitude of this reduction was not consistent across the different unsupervised methods and segmentation strategies. We conclude that a two-date segmentation strategy is useful for change detection in high-resolution imagery, but that the optimization of thresholds is critical for unsupervised change detection methods. Advanced methods need be explored that can take advantage of additional textural or other parameters.

关键词： multiresolution segmentation WorldView-2 MAD two-date change detection OBCD high spatial resolution sensitivity specificity

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Identifying Generalizable Image segmentation Parameters for Urban Land Cover Mapping through Meta-Analysis and Regression Tree Modeling

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REMOTE SENSING 2018年第1期10卷 73-73页

作者： Johnson, Brian A. Jozdani, Shahab E. Inst Global Environm Strategies Nat Resources & Ecosyst Serv Area 2108-11 Kamiyamaguchi Hayama Kanagawa 2400115 Japan Univ Isfahan Dept Geomat Engn Esfahan *** Iran

The advent of very high resolution (VHR) satellite imagery and the development of Geographic Object-Based Image Analysis (GEOBIA) have led to many new opportunities for fine-scale land cover mapping, especially in urban areas. Image segmentation is an important step in the GEOBIA framework, so great time/effort is often spent to ensure that computer-generated image segments closely match real-world objects of interest. In the remote sensing community, segmentation is frequently performed using the multiresolution segmentation (MRS) algorithm, which is tuned through three user-defined parameters (the scale, shape/color, and compactness/smoothness parameters). The scale parameter (SP) is the most important parameter and governs the average size of generated image segments. Existing automatic methods to determine suitable SPs for segmentation are scene-specific and often computationally intensive, so an approach to estimating appropriate SPs that is generalizable (i.e., not scene-specific) could speed up the GEOBIA workflow considerably. In this study, we attempted to identify generalizable SPs for five common urban land cover types (buildings, vegetation, roads, bare soil, and water) through meta-analysis and nonlinear regression tree (RT) modeling. First, we performed a literature search of recent studies that employed GEOBIA for urban land cover mapping and extracted the MRS parameters used, the image properties (i.e., spatial and radiometric resolutions), and the land cover classes mapped. Using this data extracted from the literature, we constructed RT models for each land cover class to predict suitable SP values based on the: image spatial resolution, image radiometric resolution, shape/color parameter, and compactness/smoothness parameter. Based on a visual and quantitative analysis of results, we found that for all land cover classes except water, relatively accurate SPs could be identified using our RT modeling results. The main advantage of our approach over

关键词： high spatial resolution imagery multiresolution segmentation object-based image analysis GEOBIA

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MLNet: multichannel feature fusion lozenge network for land segmentation

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JOURNAL OF APPLIED REMOTE SENSING 2022年第1期16卷

作者： Gao, Jiahong Weng, Liguo Xia, Min Lin, Haifeng Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing Peoples R China Nanjing Univ Informat Sci & Technol B DAT Nanjing Peoples R China Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing Peoples R China

The use of remote sensing images for land cover analysis has broad prospects. At present, the resolution of aerial remote sensing images is getting higher and higher, and the span of time and space is getting larger and larger, therefore segmenting target objects enconter great difficulties. Convolutional neural networks are widely used in many image semantic segmentation tasks, but existing models often use simple accumulation of various convolutional layers or the direct stacking of interfeature reuse of up- and downs ampling, the network very heavy. To improve the accuracy of land cover segmentation, we propose a multichannel feature fusion lozenge network. The multichannel feature fusion lozenge network (MLNet) is a three-sided network composed of three branches: one branch uses different levels of feature indexes to sample to maintain the integrity of high-frequency information;one branch focuses on contextual information and strengthens the compatibility of information within and between classes;and the last branch uses feature integration to filter redundant information based on multiresolution segmentation to extract key features. Compared with FCN, UNet, PSP, and other serial single road computing models, the MLNet, which performs feature fusion after three-way parallelism structure, can significantly improve the accuracy with only small increase in complexity. Experimental results show that the average accuracy of 85.30% is obtained on the land cover data set, which is much higher than that of 82.98% of FCN, 81.87% of UNet, 77.52% of SegNet, and 83.09% of EspNet, which proves the effectiveness of the model. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： land cover lozenge net three-sided network multiresolution segmentation extract key features

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Unsupervised Parameterization for Optimal segmentation of Agricultural Parcels from Satellite Images in Different Agricultural Landscapes

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REMOTE SENSING 2020年第18期12卷 3096-3096页

作者： Tetteh, Gideon Okpoti Gocht, Alexander Schwieder, Marcel Erasmi, Stefan Conrad, Christopher Thunen Inst Farm Econ Bundesallee 63 D-38116 Braunschweig Germany Humboldt Univ Geog Dept Unter Linden 6 D-10099 Berlin Germany Martin Luther Univ Halle Wittenberg Inst Geosci & Geog D-06099 Halle Germany

Image segmentation is a cost-effective way to obtain information about the sizes and structural composition of agricultural parcels in an area. To accurately obtain such information, the parameters of the segmentation algorithm ought to be optimized using supervised or unsupervised methods. The difficulty in obtaining reference data makes unsupervised methods indispensable. In this study, we evaluated an existing unsupervised evaluation metric that minimizes a global score (GS), which is computed by summing up the intra-segment uniformity and inter-segment dissimilarity within a segmentation output. We modified this metric and proposed a new metric that uses absolute difference to compute the GS. We compared this proposed metric with the existing metric in two optimization approaches based on the multiresolution segmentation (MRS) algorithm to optimally delineate agricultural parcels from Sentinel-2 images in Lower Saxony, Germany. The first approach searches for optimal scale while keeping shape and compactness constant, while the second approach uses Bayesian optimization to optimize the three main parameters of the MRS algorithm. Based on a reference data of agricultural parcels, the optimal segmentation result of each optimization approach was evaluated by calculating the quality rate, over-segmentation, and under-segmentation. For both approaches, our proposed metric outperformed the existing metric in different agricultural landscapes. The proposed metric identified optimal segmentations that were less under-segmented compared to the existing metric. A comparison of the optimal segmentation results obtained in this study to existing benchmark results generated via supervised optimization showed that the unsupervised Bayesian optimization approach based on our proposed metric can potentially be used as an alternative to supervised optimization, particularly in geographic regions where reference data is unavailable or an automated evaluation system is sought.

关键词： agricultural parcels OBIA multiresolution segmentation unsupervised segmentation evaluation spatial autocorrelation weighted variance bayesian optimization optimal segmentation

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Texture segmentation using hierarchical wavelet decomposition

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PATTERN RECOGNITION 1995年第12期28卷 1819-1824页

作者： Salari, E Ling, Z Computer Vision and Image Processing Laboratory Department of Electrical Engineering University of Toledo Toledo OH 43606 U.S.A.

This paper presents a texture segmentation algorithm based on a hierarchical wavelet decomposition. Using Daubechies' four-tap filter, an original image is decomposed into three detail images and one approximate image. The decomposition can be recursively applied to the approximate image to generate a lower resolution of the pyramid. The segmentation starts at the lowest resolution using the K-means clustering scheme and textural features obtained from various sub-bands. The result of segmentation is propagated through the pyramid to a higher resolution with continuously improving the segmentation. The lower resolution levels help to build the contour of the segmented texture, while higher levels refine the process, and correct possible errors.

关键词： Texture segmentation Wavelet transform Clustering algorithm multiresolution segmentation

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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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Unsupervised texture segmentation by determining the interior of texture regions based on wavelet transform

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2001年第8期15卷 1231-1250页

作者： Lee, KL Chen, LH Natl Chiao Tung Univ Dept Comp & Informat Sci Hsinchu 30050 Taiwan

Traditional approaches for texture segmentation via wavelet transform usually adopt textural features to achieve segmentation purposes. However, for a natural image, the characteristics of the pixels in a texture region are not similar everywhere from a global viewpoint, and over-segmentation often occurs. To deal with this issue, an unsupervised texture segmentation method based on determining the interior of texture regions is proposed. The key idea of the proposed method is that if the pixels of the input image can be classified into interior pixels (pixels within a texture region) and boundary ones, then the segmentation can be achieved by applying region growing on the interior pixels and reclassifying boundary pixels. Based on the fact that each pixel P within a texture region will have similar characteristics with its neighbors, after applying wavelet transform, pixel P will have similar response with its neighbors in each transformed subimage. Thus, by applying a multilevel thresholding technique to segment each subimage into several regions, pixel P and its neighbors will be assigned to the same region in most subimages. Based on these segmented results, an interior pixels finding algorithm is then provided to find all interior pixels of textural regions. The algorithm considers a pixel which is in the same region as its neighbors in most subimages as an interior pixel. The effectiveness of this method is proved by successfully segmenting natural texture images and comparing with other methods.

关键词： texture segmentation wavelet transform multiresolution segmentation unsupervised clustering

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Automated registration of potential locations for solar energy production with Light Detection And Ranging (LiDAR) and small format photogrammetry

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JOURNAL OF CLEANER PRODUCTION 2016年第Part5期112卷 3820-3829页

作者： Szabo, Szilard Enyedi, Peter Horvath, Miklos Kovacs, Zoltan Burai, Peter Csoknyai, Tamas Szabo, Gergely Univ Debrecen Dept Phys Geog & Geoinformat H-4032 Debrecen Hungary Karoly Robert Coll Res Inst Remote Sensing & Rural Dev H-3200 Gyongyos Hungary Budapest Univ Technol & Econ Dept Bldg Serv & Proc Engn Budapest Hungary

Energy production and consumption is a key element in future development which is influenced both by the technical possibilities available and by decision makers. Sustainability issues are closely linked in with energy policy, given the desire to increase the proportion of renewable energy. According to the Horizon 2020 climate and energy package, European Union (EU) member countries have to reduce the amount of greenhouse gases they emit by 20%, to increase the proportion of renewable energy to 20% and to improve energy efficiency by 20% by 2020. In this study we aim to assess the opportunities available to exploit solar radiation on roofs with Light Detection And Ranging (LiDAR) and photogrammetry techniques. The surveyed area was in Debrecen, the second largest city in Hungary. An aerial LiDAR survey was conducted with a density of 12 points/m(2), over a 7 x 1.8 km wide band. We extracted the building and roof models of the buildings from the point cloud. Furthermore, we applied a low-cost drone (DJI Phantom with a GoPro camera) in a smaller area of the LiDAR survey and also created a 3D model: buildings and roof planes were identified with multiresolution segmentation of the digital surface models (DSM) and orthophoto coverages. Building heights and building geometry were also extracted and validated in field surveys. 50 buildings were chosen for the geodetic survey and the results of the accuracy assessment were extrapolated to other buildings;in addition to this, 100 building heights were measured. We focused primarily on the roofs, as these surfaces offer possible locations for thermal and photovoltaic equipment. We determined the slope and aspect of roof planes and calculated the incoming solar energy according to roof planes before comparing the results of the point cloud processing of LiDAR data and the segmentation of DSMs. Extracted roof geometries showed varying degrees of accuracy: the research proved that LiDAR-based roof-modelling is the best choice

关键词： Roof plane Solar irradiation Point cloud multiresolution segmentation Drone

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multiresolution-Based Rough Fuzzy Possibilistic -Means Clustering Method for Land Cover Change Detection

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 570-580页

作者： Xiao, Tong Wan, Yiliang Chen, Jianjun Shi, Wenzhong Qin, Jianxin Li, Deping Hunan Normal Univ Sch Geog Sci Changsha 410081 Peoples R China Hunan Normal Univ Hunan Key Lab Geospatial Big Data Min & Applicat Changsha 410081 Peoples R China Hunan Vocat Coll Engn Changsha 410081 Peoples R China Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong 999077 Peoples R China

Object-oriented change detection (OOCD) plays an important role in remote sensing change detection. Generally, most of current OOCD methods adopt the highest predicted probability to determine whether objects have changes. However, it ignores the fact that only parts of an object have changes, which will generate the uncertain classification information. To reduce the classification uncertainty, an improved rough-fuzzy possibilistic $c$-means clustering algorithm combined with multiresolution scales information (MRFPCM) is proposed. First, stacked bitemporal images are segmented using the multiresolution segmentation approach from coarse to fine scale. Second, objects at the coarsest scale are classified into changed, unchanged, and uncertain categories by the proposed MRFPCM. Third, all the changed and unchanged objects in previous scales are combined as training samples to classify the uncertain objects into new changed, unchanged, and uncertain objects. Finally, segmented objects are classified layer by layer based on the MRFPCM until there are no uncertain objects. The MRFPCM method is validated on three datasets with different land change complexity and compared with five widely used change detection methods. The experimental results demonstrate the effectiveness and stability of the proposed approach.

关键词： Classification uncertainty land cover change detection (LCCD) multiresolution segmentation rough fuzzy possibilistic c-means clustering algorithm (RFPCM)

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Mapping Secondary Forest Succession on Abandoned Agricultural Land with LiDAR Point Clouds and Terrestrial Photography

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REMOTE SENSING 2015年第7期7卷 8300-8322页

作者： Kolecka, Natalia Kozak, Jacek Kaim, Dominik Dobosz, Monika Ginzler, Christian Psomas, Achilleas Jagiellonian Univ Inst Geog & Spatial Management Dept GIS Cartog & Remote Sensing PL-30387 Krakow Poland Swiss Fed Res Inst WSL CH-8903 Birmensdorf Switzerland

Secondary forest succession on abandoned agricultural land has played a significant role in land cover changes in Europe over the past several decades. However, it is difficult to quantify over large areas. In this paper, we present a conceptual framework for mapping forest succession patterns using vegetation structure information derived from LiDAR data supported by national topographic vector data. This work was performed in the Szczawnica commune in the Polish Carpathians. Using object-based image analysis segments of no vegetation, and sparse/dense low/medium/high vegetation were distinguished and subsequently compared to the national topographic dataset to delineate agricultural land that is covered by vegetation, which indicates secondary succession on abandoned fields. The results showed that 18.7% of the arable land and 40.4% of grasslands, that is 31.0% of the agricultural land in the Szczawnica commune, may currently be experiencing secondary forest succession. The overall accuracy of the approach was assessed using georeferenced terrestrial photographs and was found to be 95.0%. The results of this study indicate that the proposed methodology can potentially be applied in large-scale mapping of secondary forest succession patterns on abandoned land in mountain areas.

关键词： forest succession land cover LiDAR multiresolution segmentation terrestrial photography

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