Distinguishing wetland vegetation and channel features with object-based image segmentation

Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models’ training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model’s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.

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Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

Remote Sensing ◽

10.3390/rs12111772 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1772

Author(s):

Brian Alan Johnson ◽

Lei Ma

Keyword(s):

Remote Sensing ◽

Image Analysis ◽

Image Segmentation ◽

Online Survey ◽

Accuracy Assessment ◽

Learning Approaches ◽

Special Issue ◽

Object Based Image Analysis ◽

Object Based ◽

Wide Range

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

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A REGION-BASED MULTI-SCALE APPROACH FOR OBJECT-BASED IMAGE ANALYSIS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b7-241-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 241-247 ◽

Cited By ~ 6

Author(s):

T. Kavzoglu ◽

M. Yildiz Erdemir ◽

H. Tonbul

Keyword(s):

Image Analysis ◽

Image Segmentation ◽

Classification Accuracy ◽

Scale Parameter ◽

Rate Of Change ◽

Multi Scale ◽

Object Based Image Analysis ◽

Optimal Scale ◽

Segmentation Process ◽

Object Based

Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.

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Image Segmentation Methods for Object-based Analysis and Classification

Remote Sensing Image Analysis: Including the Spatial Domain - Remote Sensing and Digital Image Processing ◽

10.1007/1-4020-2560-2_12 ◽

2006 ◽

pp. 211-236 ◽

Cited By ~ 1

Author(s):

Thomas Blaschke ◽

Charles Burnett ◽

Anssi Pekkarinen

Keyword(s):

Image Segmentation ◽

Object Based ◽

Segmentation Methods

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Self-adaptive Image Segmentation Optimization for Hierarchal Object-based Classification of Drone-based Images

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/540/1/012090 ◽

2020 ◽

Vol 540 ◽

pp. 012090

Author(s):

Rami Al-Ruzouq ◽

Mohamed Barakat A. Gibril ◽

Abdallah Shanableh

Keyword(s):

Image Segmentation ◽

Object Based ◽

Self Adaptive

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Object-based image segmentation using DWT/RDWT multiresolution Markov random field

1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258) ◽

10.1109/icassp.1999.757593 ◽

1999 ◽

Cited By ~ 3

Author(s):

Lei Zheng ◽

J.C. Liu ◽

A.K. Chan ◽

W. Smith

Keyword(s):

Image Segmentation ◽

Random Field ◽

Markov Random Field ◽

Markov Random ◽

Object Based

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SAR Image Segmentation Using Morphological Attribute Profiles

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-3-39-2014 ◽

2014 ◽

Vol XL-3 ◽

pp. 39-44 ◽

Cited By ~ 4

Author(s):

M. Boldt ◽

A. Thiele ◽

K. Schulz ◽

S. Hinz

Keyword(s):

Image Segmentation ◽

High Resolution ◽

Region Growing ◽

Object Based Image Analysis ◽

Sar Image Segmentation ◽

High Resolution Imagery ◽

Object Based ◽

High Resolution Images ◽

Novel Method ◽

Image Set

In the last years, the spatial resolution of remote sensing sensors and imagery has continuously improved. Focusing on spaceborne Synthetic Aperture Radar (SAR) sensors, the satellites of the current generation (TerraSAR-X, COSMO-SykMed) are able to acquire images with sub-meter resolution. Indeed, high resolution imagery is visually much better interpretable, but most of the established pixel-based analysis methods have become more or less impracticable since, in high resolution images, self-sufficient objects (vehicle, building) are represented by a large number of pixels. Methods dealing with Object-Based Image Analysis (OBIA) provide help. Objects (segments) are groupings of pixels resulting from image segmentation algorithms based on homogeneity criteria. The image set is represented by image segments, which allows the development of rule-based analysis schemes. For example, segments can be described or categorized by their local neighborhood in a context-based manner. <br><br> In this paper, a novel method for the segmentation of high resolution SAR images is presented. It is based on the calculation of morphological differential attribute profiles (DAP) which are analyzed pixel-wise in a region growing procedure. The method distinguishes between heterogeneous and homogeneous image content and delivers a precise segmentation result.

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RETRIEVING IMAGES BY COMPARING HOMOGENEOUS COLOR AND TEXTURE OBJECTS IN THE IMAGE

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001404003514 ◽

2004 ◽

Vol 18 (06) ◽

pp. 1093-1110 ◽

Cited By ~ 1

Author(s):

HUN-WOO YOO ◽

DONG-SIK JANG ◽

KWANG-KYU SEO ◽

MYUNG-EUI LEE

Keyword(s):

Image Segmentation ◽

Image Retrieval ◽

Vector Quantization ◽

Textural Features ◽

Clustering Method ◽

Retrieval Method ◽

Simple Representation ◽

Object Based ◽

Efficient Retrieval ◽

Image Segmentation Algorithm

An object-based image retrieval method is addressed in this paper. For that purpose, a new image segmentation algorithm and image comparing method between segmented objects are proposed. For image segmentation, color and textural features are extracted from each pixel in the image and these features are used as inputs into VQ (Vector Quantization) clustering method, which yields homogeneous objects in terms of color and texture. In this procedure, colors are quantized into a few dominant colors for simple representation and efficient retrieval. In the retrieval case, two comparing schemes are proposed. Comparisons between one query object and multi-objects of a database image and comparisons between multi-query objects and multi-objects of a database image are proposed. For fast retrieval, dominant object colors are key-indexed into the database.

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