A Fast and Effective Method for Unsupervised Segmentation Evaluation of Remote Sensing Images

The segmentation of remote sensing images with high spatial resolution is important and fundamental in geographic object-based image analysis (GEOBIA), so evaluating segmentation results without prior knowledge is an essential part in segmentation algorithms comparison, segmentation parameters selection, and optimization. In this study, we proposed a fast and effective unsupervised evaluation (UE) method using the area-weighted variance (WV) as intra-segment homogeneity and the difference to neighbor pixels (DTNP) as inter-segment heterogeneity. Then these two measures were combined into a fast-global score (FGS) to evaluate the segmentation. The effectiveness of DTNP and FGS was demonstrated by visual interpretation as qualitative analysis and supervised evaluation (SE) as quantitative analysis. For this experiment, the ‘‘Multi-resolution Segmentation’’ algorithm in eCognition was adopted in the segmentation and four typical study areas of GF-2 images were used as test data. The effectiveness analysis of DTNP shows that it can keep stability and remain sensitive to both over-segmentation and under-segmentation compared to two existing inter-segment heterogeneity measures. The effectiveness and computational cost analysis of FGS compared with two existing UE methods revealed that FGS can effectively evaluate segmentation results with the lowest computational cost.

[1]  D. Roberts,et al.  Open water detection in urban environments using high spatial resolution remote sensing imagery , 2020 .

[2]  Hui Yang,et al.  An Improved Method for Road Extraction from High-Resolution Remote-Sensing Images that Enhances Boundary Information , 2020, Sensors.

[3]  Hongmin Gao,et al.  An Efficient Building Extraction Method from High Spatial Resolution Remote Sensing Images Based on Improved Mask R-CNN , 2020, Sensors.

[4]  Jianwu Fang,et al.  Geo-Object-Based Land Cover Map Update for High-Spatial-Resolution Remote Sensing Images via Change Detection and Label Transfer , 2020, Remote. Sens..

[5]  Liang Xiao,et al.  Optimizing multiscale segmentation with local spectral heterogeneity measure for high resolution remote sensing images , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[6]  Ying Liu,et al.  Unsupervised segmentation parameter selection using the local spatial statistics for remote sensing image segmentation , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[7]  Caspar A. Mücher,et al.  SegOptim - A new R package for optimizing object-based image analyses of high-spatial resolution remotely-sensed data , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[8]  Dongmei Chen,et al.  Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[9]  Jing Yuan,et al.  Self-adaptive segmentation of satellite images based on a weighted aggregation approach , 2018, GIScience & Remote Sensing.

[10]  Ying Liu,et al.  Unsupervised Segmentation Evaluation Using Area-Weighted Variance and Jeffries-Matusita Distance for Remote Sensing Images , 2018, Remote. Sens..

[11]  Jianhua Liu,et al.  An adaptive scale estimating method of multiscale image segmentation based on vector edge and spectral statistics information , 2018, International Journal of Remote Sensing.

[12]  Wenzhong Shi,et al.  A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis , 2018 .

[13]  Hui Li,et al.  A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images , 2017, Sensors.

[14]  Ronald Kemker,et al.  Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning , 2017, ISPRS Journal of Photogrammetry and Remote Sensing.

[15]  Jian Yang,et al.  Region merging using local spectral angle thresholds: A more accurate method for hybrid segmentation of remote sensing images , 2017 .

[16]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Isao Endo,et al.  Image Segmentation Parameter Optimization Considering Within- and Between-Segment Heterogeneity at Multiple Scale Levels: Test Case for Mapping Residential Areas Using Landsat Imagery , 2015, ISPRS Int. J. Geo Inf..

[18]  Xueliang Zhang,et al.  Segmentation quality evaluation using region-based precision and recall measures for remote sensing images , 2015 .

[19]  Qihao Weng,et al.  An Automated Method to Parameterize Segmentation Scale by Enhancing Intrasegment Homogeneity and Intersegment Heterogeneity , 2015, IEEE Geoscience and Remote Sensing Letters.

[20]  Heather Reese,et al.  Tree Crown Mapping in Managed Woodlands (Parklands) of Semi-Arid West Africa Using WorldView-2 Imagery and Geographic Object Based Image Analysis , 2014, Sensors.

[21]  Jian Yang,et al.  A multi-band approach to unsupervised scale parameter selection for multi-scale image segmentation , 2014 .

[22]  Bin Chen,et al.  Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images , 2014, Optical Engineering.

[23]  Wei Gao,et al.  Automatic selection of optimal segmentation scales for high-resolution remote sensing images , 2013, Optics & Photonics - Optical Engineering + Applications.

[24]  Bo Wang,et al.  Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis , 2012 .

[25]  Brian Johnson,et al.  Unsupervised image segmentation evaluation and refinement using a multi-scale approach , 2011 .

[26]  Patricia Gober,et al.  Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery , 2011, Remote Sensing of Environment.

[27]  Guifeng Zhang,et al.  An Edge Embedded Marker-Based Watershed Algorithm for High Spatial Resolution Remote Sensing Image Segmentation , 2010, IEEE Transactions on Image Processing.

[28]  P. Gong,et al.  Accuracy Assessment Measures for Object-based Image Segmentation Goodness , 2010 .

[29]  Craig A. Coburn,et al.  Hybrid Segmentation - Artificial Neural Network Classification of High Resolution Hyperspectral Imagery for Site-Specific Herbicide Management in Agriculture , 2008 .

[30]  Hui Zhang,et al.  Image segmentation evaluation: A survey of unsupervised methods , 2008, Comput. Vis. Image Underst..

[31]  Antônio Miguel Vieira Monteiro,et al.  Parameter selection for region‐growing image segmentation algorithms using spatial autocorrelation , 2006 .

[32]  Chad Hendrix,et al.  A Comparison of Urban Mapping Methods Using High-Resolution Digital Imagery , 2003 .

[33]  Josef Kittler,et al.  Automatic watershed segmentation of randomly textured color images , 1997, IEEE Trans. Image Process..

[34]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  C. Woodcock,et al.  The factor of scale in remote sensing , 1987 .

[36]  Martin D. Levine,et al.  Dynamic Measurement of Computer Generated Image Segmentations , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  T. Martha,et al.  A Tool Assessing Optimal Multi-Scale Image Segmentation , 2017, Journal of the Indian Society of Remote Sensing.

[38]  K. Moffett,et al.  Remote Sens , 2015 .

[39]  Geoffrey E. Hinton,et al.  Machine Learning for Aerial Image Labeling , 2013 .

[40]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .

[41]  Luyao Huang,et al.  Object-Oriented Classification of High Resolution Satellite Image for Better Accuracy , 2008 .

[42]  U. Benz,et al.  Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information , 2004 .

[43]  Kannan,et al.  ON IMAGE SEGMENTATION TECHNIQUES , 2022 .