Scale-variable region-merging for high resolution remote sensing image segmentation

Su, Tengfei

doi:10.1016/j.isprsjprs.2018.12.003

Cited by 36 publications

(18 citation statements)

References 36 publications

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“…However, selection of an efficient segmentation method and its parameters is a very difficult task depending on the texture, size and complex structure of the earth objects (Johnson and Xie, 2011). When an appropriate and efficient segmentation approach is not preferred, over-segmentation and undersegmentation may arise (Su, 2019). Therefore, optimum methods for image segmentation quality assessment are required.…”

Section: Segmentationmentioning

confidence: 99%

A Spectral Band Based Comparison of Unsupervised Segmentation Evaluation Methods for Image Segmentation Parameter Optimization

Tonbul

Kavzoğlu

2020

International Journal of Environment and Geoinformatics

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Very high-resolution images obtained with recently launched satellite sensors have been used intensively in the remote sensing area. The widespread use of high-resolution images has greatly facilitated the creation and updating of land use/land cover (LULC) maps. Traditional pixel-based image analysis methods that extract information based solely on the spectral values of pixels are generally not suitable for high-resolution images. Unlike pixel-based approaches, object-based image analysis (OBIA) uses pixel clustering (image objects) instead of pixels by considering the shape, texture, context and spectral features and provides richer information extraction. Image segmentation is an important process and prerequisite for the OBIA process. It is essential to evaluate the performance of segmentation algorithms for the determination of effective segmentation methods and optimization of segmentation parameters. In this study, the multi-resolution segmentation algorithm is used for the segmentation process. The effect of spectral bands on segmentation quality was analysed using a Worldview-2 high-resolution satellite image. In order to analyse segmentation quality, two unsupervised quality metrics, namely, F-measure and Plateau Objective Function (POF) values were calculated for each band separately. In this manner, optimum parameter values were determined using different variations of Moran's I Index and variance values. Image segmentation was performed by using different scale, shape and compactness parameter values. In this context, 30 segmentation analyses were performed considering three different spectral bands (red, green and near-infrared bands). The results showed that the highest segmentation quality was acquired for the NIR band among the spectral bands for the F-measure method, while the highest segmentation quality value was achieved for the green band for the POF metric. In addition, the optimum segmentation parameter values of the scale, shape and compactness were determined as 30-0.3-0.5 and 50-0.1-0.3, for F-measure and POF approaches, respectively.

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Section: Segmentationmentioning

confidence: 99%

A Spectral Band Based Comparison of Unsupervised Segmentation Evaluation Methods for Image Segmentation Parameter Optimization

Tonbul

Kavzoğlu

2020

International Journal of Environment and Geoinformatics

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“…The accuracy of segmentation is essential because if segmentation errors appear, misclassification tends to occur [19]. There are two types of segmentation error: oversegmentation error (OSE) and under-segmentation error (USE) [20].…”

Section: Introductionmentioning

confidence: 99%

“…There are two types of segmentation error: oversegmentation error (OSE) and under-segmentation error (USE) [20]. OSE means that a 2 of 22 geo-object is partitioned into more than one segment, and USE indicates that a segment covers more than one geo-object [19]. Therefore, many different segmentation models were established to improve the accuracy of segmentation, and the most popular ones include watershed [21][22][23], mean-shift [24][25][26], Markov random field [27][28][29][30], conditional random field [31][32][33], and region merging [18,19,[34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Improvement of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria

et al. 2021

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Image segmentation plays a significant role in remote sensing image processing. Among numerous segmentation algorithms, the region-merging segmentation algorithm is widely used due to its well-organized structure and outstanding results. Many merging criteria (MC) were designed to improve the accuracy of region-merging segmentation, but each MC has its own shortcomings, which can cause segmentation errors. Segmentation accuracy can be improved by referring to the segmentation results. To achieve this, an approach for detecting and correcting region-merging image segmentation errors is proposed, and then an iterative optimization model is established. The main contributions of this paper are as follows: (1) The conflict types of matching segment pairs are divided into scale-expression conflict (SEC) and region-ownership conflict (ROC), and ROC is more suitable for optimization. (2) An equal-scale local evaluation method was designed to quantify the optimization potential of ROC. (3) A regional anchoring strategy is proposed to preserve the results of the previous iteration optimization. Three QuickBird satellite images of different land-cover types were used for validating the proposed approach. Both unsupervised and supervised evaluation results prove that the proposed approach can effectively improve segmentation accuracy. All explicit and implicit optimization modes are concluded, which further illustrate the stability of the proposed approach.

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“…However, in order to take full advantage of RS data, these images should be processed in an efficient and effective way, with the aim of providing a final data representation that satisfies the final user/application. In this context, the demand for RS image processing methods has increased over the past decades [47]- [52], while the scientific community has made great efforts to develop a large number of methodologies to address very specific problems, such as spatial/spectral resolution enhancement [53]- [57], data restoration/denoising [58]- [61], data compressing, band selection and dimensionality reduction [62]- [68], image segmentation [69]- [72], spectral unmixing [73]- [77], target, object, change and anomaly detection [20], [78]- [84], and data classification [85]- [90]. The original codes of many of these methods are available in different repositories.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Remotely Sensed Images Through Social Media

Redondo

Haut

Paoletti

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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During the past decades, the volume of big data available in remote sensing (RS) applications has grown significantly. In addition, a number of applications related to monitoring human activity are being developed based on this kind of data. This has considerably increased the demand for RS processing methods. In this sense, the scientific community is facing the challenge of how to maximize the potential of the data that are produced in a fast and efficient way. In particular, the provision of processing algorithms that can be developed in an easy way is a fundamental problem for the RS community, due to the large volume of data offered by different portals and agencies, and the need for algorithms developed on different platforms and using a variety of programming frameworks. To address these challenges, this paper takes advantage of social media tools to bring images and algorithms closer to users. In particular, a new system based on the Telegram messaging application Bot (called @ThuleRSbot) has been developed to provide a wide variety of RS image processing methods to users under the same interface. The system has been developed using the Python language and the Telegram Bot application program interface (API). The most remarkable characteristics of the system are: 1) a completely open architecture that facilitates the incorporation of new algorithms without effort, and 2) an easy way to automatically gather satellite images from the Sentinel Hub platform. Code available on: https://github.com/mhaut/ThuleRSbot.

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Scale-variable region-merging for high resolution remote sensing image segmentation

Cited by 36 publications

References 36 publications

A Spectral Band Based Comparison of Unsupervised Segmentation Evaluation Methods for Image Segmentation Parameter Optimization

A Spectral Band Based Comparison of Unsupervised Segmentation Evaluation Methods for Image Segmentation Parameter Optimization

Improvement of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria

Analysis of Remotely Sensed Images Through Social Media

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