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

  title={Forest Type Mapping using Object-specific Texture Measures from Multispectral Ikonos Imagery: Segmentation Quality and Image Classification Issues},
  author={Minho Kim and Marguerite Madden and Timothy A. Warner},
  journal={Photogrammetric Engineering and Remote Sensing},
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… 

Evaluation of object-based image analysis techniques on very high-resolution satellite image for biomass estimation in a watershed of hilly forest of Nepal

Methods of forest carbon estimation using remote-sensing data and techniques are evolving within a short timeframe as compared to traditional forest inventory methods. Object-based image analysis

Parameter-Based Performance Analysis of Object-Based Image Analysis Using Aerial and Quikbird-2 Images

Effectiveness and applicability of the segmentation method in relation to its parameters was analysed and compactness parameter was found to be having minimal effect on the construction of image objects, hence it can be set to a constant value in image classification.

Object-oriented and pixel-based classification approaches to classify tropical successional stages using airborne high–spatial resolution images

Airborne high–spatial resolution images were evaluated for mapping purposes in a complex Atlantic rainforest environment in southern Brazil. Two study sites, covered predominantly by secondary

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Forest structure estimates with GEOBIA and multi-scale optical sensors

The GEOBIA (GEOgraphic Object-Based Image Analysis) approach persists to reveal its effectiveness in remote sensing data analysis, which provides paradigms that integrate analyst’s expert knowledge

Classifying a high resolution image of an urban area using super-object information

Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects

This study used geographic object-based image analysis (GEOBIA) with very high spatial resolution (VHR) aerial imagery (0.3 m spatial resolution) to classify vegetation, channel and bare mud classes

Does spatial resolution matter? A multi-scale comparison of object-based and pixel-based methods for detecting change associated with gas well drilling operations

For studying the impact of forest clearing associated with gas extraction, GEOBIA is more accurate than pixel-based methods, but only at the very finest resolution of 1 m, and for coarser spatial resolutions, per-pixel methods are not statistically different from GEO BIA.



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This research employs local variance and spatial autocorrelation to estimate the optimal size of image objects for segmenting forest stands to investigate how between-object correlation changes with segmentation scale in terms of over-, optimal, and under-segmentation.

Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery

In this paper, we evaluate the capability of the high spatial resolution airborne Digital Airborne Imaging System (DAIS) imagery for detailed vegetation classification at the alliance level with the

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Image segmentation is a method of defining discrete objects or classes of objects in images. Addition of n spatial attxibute, i.e., image texture, improves the segmentation process in most areas

Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery

A new method was developed to choose the optimal scale parameter with the aid of Bhattacharya Distance (BD), a well-known index of class separability in traditional pixel-based classification, to determine an optimal scale at which the segmented objects have the potential to achieve the best classification accuracy.

Object-based contextual image classification built on image segmentation

  • T. Blaschke
  • Environmental Science, Computer Science
    IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003
  • 2003
This paper builds on a discussion of different approaches to image segmentation techniques and demonstrates through several applications how segmentation and object-based methods improve on pixel-based image analysis/classification methods.

Incorporating texture into classification of forest species composition from airborne multispectral images

Although research with digital airborne remote sensing data has been undertaken in different ecoregions to classify forested areas, the potential role of such imagery in deriving information to

Evaluation Of The Grey-level Co-occurrence Matrix Method For Land-cover Classification Using Spot Imagery

Absfruct-Nine cover types have been classified using a textural/ spectral approach. The texture analysis is based on the grey-level cooccurrence matrix method. Texture features are created from a

An object-specific image-texture analysis of H-resolution forest imagery☆

Using spatial Co-occurrence texture to increase forest structure and species composition classification accuracy

should be augmented with texture measures (St-Onge and The analysis of forest structure and species composition with Cavayas, 1995). Texture can be used in the classification or, high spatial

Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama