Forest Type Mapping using Object-specific Texture Measures from Multispectral Ikonos Imagery: Segmentation Quality and Image Classification Issues

By Minho Kim, Marguerite Madden, Timothy A. Warner. 2009

This study investigated the use of a geographic object-based image analysis (GEOBIA) approach with the incorporation of object-specific grey-level co-occurrence matrix (GLCM) texture measures from a multispectral Ikonos image for delineation of deciduous, evergreen, and mixed forest types in Guilford Courthouse National Military Park, North Carolina. A series of automated segmentations was produced at a range of scales, each resulting in an associated range of number and size of objects (or segments). Prior to classification, the spatial autocorrelation of each segmentation was evaluated by calculating Moran’s I using the average image digital numbers (DNs) per segment. An initial assumption was made that the optimal segmentation scales would have the lowest spatial autocorrelation, and conversely, that over- and under-segmentation would result in higher autocorrelation between segments. At these optimal segmentation scales, the automated segmentation was found to yield information comparable to manually interpreted stand-level forest maps in terms of the size and number of segments. A series of object-based classifications was carried out on the image at the entire range of segmentation scales. The results demonstrated that the scale of segmentation directly influenced the object-based forest type classification results. The accuracies were higher for classification of images identified from a spatial autocorrelation analysis to have an optimal segmentation, compared to those determined to have over- and under-segmentation. An overall accuracy of 79 percent with a Kappa of 0.65 was obtained at the optimal segmentation scale of 19. The addition of object-specific GLCM multiple texture analysis improved classification accuracies up to a value of 83 percent overall accuracy and a Kappa of 0.71 by reducing the confusion between evergreen and mixed forest types. Although some misclassification still remained because of local segmentation quality, a visual assessment of the texture-enhanced GEOBIA classification generally agreeable with manually interpreted forest types.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, background): “wide range of applications, such as natural hazards detection [5-7], LULC determination [8-43], geospatial object detection [27, 44-52], geographic image retrieval [53-63], vegetation mapping [64-68], environment monitoring, and urban planning.”
Unsupervised image segmentation evaluation and refinement using a multi-scale approach (2011, unknown): “In a similar study, Kim et al. (2008, 2009) computed unweighted variance (NIR band) and Global Moran's I (NIR band) and graphed each separately to compare multiple segmentations and identify optimal parameters for segmenting forest stands.”
Strathprints Institutional Repository Shuhui and Ren, Jinchang (2015) Effective and Efficient Midlevel Visual Elements-oriented Land-use Classification Using Vhr Remote Sensing Images. Ieee Transactions on Geoscience and Remote Sensing, 53 (8). Pp. 4238-4249. Issn 0196-2892 Effective and Efficient M (unknown): “…important role for a wide range of applications, such as natural geological hazards detection [1] [2], general land-use/land-cover (LULC) determination[3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17], vegetation types mapping [18], environment monitoring, and urban planning, etc.”
Scene-Level Geographic Image Classification Based on a Covariance Descriptor Using Supervised Collaborative Kernel Coding (2016, background): “As a fundamental research area of remote sensing image analysis, scene-level geographic image classification is of great importance for land use and land cover (LULC) image classification [1–3], semantic interpretations of images [4], geographic image retrieval [5–7] and forest type mapping [8], whi”
Assessing object-based classification: advantages and limitations (2009, background): “Given the fact that image segmentation is scale-dependent (Benz et al. 2004), we hypothesize that the segmentation accuracy and the overall effect of object-based classification are both dependent on the scale of image segmentation (Addink et al. 2007, Kim et al. 2009).”
Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales (2017, methodology): “GLCM texture has been incorporated into object classification in GEOBIA applications (Kim, Madden, and Warner 2009; Rabia and Terribile 2013); however, texture may also help define edges when forming geographical objects (Okubo et al. 2010).”
Deep learning for remote sensing image classification: A survey (2018, methodology): “The former include properties such as color (typically the color histograms, CH; Swain & Ballard, 1991), texture (typically the local binary patterns, LBP; Ojala, Pietikinen, & Menp, 2000), and structure (typically the scale invariant feature transforms, SIFT; Kim, Madden, & Warner, 2009), and the l”
SVM-Based Fuzzy Decision Trees for Classification of High Spatial Resolution Remote Sensing Images (2012, background): “Traditional textural features include the gray-level co-occurrence matrix [1], features obtained through”
Unmanned Aerial Vehicle for Remote Sensing Applications - A Review (2019, methodology): “In HR and VHR image processing, texture features have been widely used to improve LULC classification accuracy [76,77], such as grey-level concurrent matrixes (GLCMs) [78] and local binary patterns (LBPs) [79–81].”