Assessment of satellite image segmentation in RGB and HSV color space using image quality measures

Ganesan, P.; Rajini, V.

doi:10.1109/icaee.2014.6838441

Cited by 53 publications

(22 citation statements)

References 7 publications

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“…The performance of the proposed GFKuts method is evaluated by computing the Accuracy, Precision, Recall and F1-score. These metrics are also compared against traditional image segmentation methods such as Thresholding [41] and K-means [42], but also against the original GrabCut method [33]. Table 1 contains the numerical results regarding the evaluation of the aforementioned segmentation algorithms.…”

Section: Nir-image Segmentation Metricsmentioning

confidence: 99%

A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops

et al. 2020

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Traditional methods to measure spatio-temporal variations in biomass rely on a labor-intensive destructive sampling of the crop. In this paper, we present a high-throughput phenotyping approach for the estimation of Above-Ground Biomass Dynamics (AGBD) using an unmanned aerial system. Multispectral imagery was acquired and processed by using the proposed segmentation method called GFKuts, that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo based K-means, and a guided image filtering. Accurate plot segmentation results enabled the extraction of several canopy features associated with biomass yield. Machine learning algorithms were trained to estimate the AGBD according to the growth stages of the crop and the physiological response of two rice genotypes under lowland and upland production systems. Results report AGBD estimation correlations with an average of r = 0.95 and R 2 = 0.91 according to the experimental data. We compared our segmentation method against a traditional technique based on clustering. A comprehensive improvement of 13% in the biomass correlation was obtained thanks to the segmentation method proposed herein.

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Section: Nir-image Segmentation Metricsmentioning

confidence: 99%

A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops

et al. 2020

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“…A complete quantitative assessment through Entropy [25], MSE (Mean Squares Error), PSNR (Peak Signal to Noise Ratio) [26] & finally the SSIM (Structural Similarity Index) [27] is discussed in this section. The outcomes of the quantitative analysis are presented in Fig.…”

Section: B Quantitative Analysismentioning

confidence: 99%

An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis

Mittal¹,

et al. 2019

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An edge detection is important for its reliability and security which delivers a better understanding of object recognition in the applications of computer vision, such as pedestrian detection, face detection, and video surveillance. This paper introduced two fundamental limitations encountered in edge detection: edge connectivity and edge thickness, those have been used by various developments in the state-of-theart. An optimal selection of the threshold for effectual edge detection has constantly been a key challenge in computer vision. Therefore, a robust edge detection algorithm using multiple threshold approaches (B-Edge) is proposed to cover both the limitations. The majorly used canny edge operator focuses on two thresholds selections and still witnesses a few gaps for optimal results. To handle the loopholes of the canny edge operator, our method selects the simulated triple thresholds that target to the prime issues of the edge detection: image contrast, effective edge pixels selection, errors handling, and similarity to the ground truth. The qualitative and quantitative experimental evaluations demonstrate that our edge detection method outperforms competing algorithms for mentioned issues. The proposed approach endeavors an improvement for both grayscale and colored images.INDEX TERMS Edge, edge connectivity, edge detection, edge width uniformity, threshold.SUDIPTA ROY received the Ph.D. degree in computer science and engineering from the Department of Computer Science and Engineering, University of Calcutta. He is currently with the Radiological Chemistry and Imaging Laboratory, Washington University in Saint Louis, USA. He has more than five years of experience in teaching and research. He is an author of more than 30 publications in refereed national and international journals and conferences, including the IEEE, Springer, Elsevier, and many others. He is an author of one book and many book chapters. He holds an U.S. patent in medical image processing and filed an Indian patent in smart agricultural system. His research interests include biomedical image analysis, image processing, steganography, artificial intelligence, big data analysis, machine learning, and big data technologies.

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“…Ganesan P et al, use a modified fuzzy c-means clustering algorithm to present a comparative study of satellite image segmentation in RGB and HSV color space [9]. They use GeoEye-1 satellite image as the experiment data.…”

Section: Related Workmentioning

confidence: 99%

Improvement of Segmentation Performance for Feature Extraction on Whirlwind Cloud-based Satellite Image using DBSCAN Clustering Algorithm

Sa'ada

Harsono

Basuki

2019

emitter

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Images contain a lot of information that can be used in a variety of areas. One of the images that have much information inside is satellite image. In order to extract the information properly, the image processing step should be performed properly. The segmentation process plays an important role in image processing, especially for feature extraction. Many ways were developed to perform the segmentation image. In this study, we apply DBSCAN clustering to segment images on whirlwind cloud feature extraction problems. DBSCAN is a density-based classifier method which means it is suitable to group a density-based data. While the image used in the segmentation process is the Himawari 8 satellite image which also contains density-based data. It contains various information about clouds condition like cloud type, cloud temperature, cloud humidity, rainfall potential based on cloud temperature, etc. This study uses Himawari 8 satellite images as input where the images taken are images several hours before a wirlwind event in an area, while the cluster method used is the DBSCAN algorithm. Clustering is done to get the extraction features of a wirlwind in the form of centroid points that characterize the movement of a cloud. Segmentation performance was observed based on the number of centroid points as a result of clustering several types of clouds in an area before a wirlwind occurred. Based on segmentation testing using the DBSCAN algorithm for cloud data in an area for several hours before a wirlwind, better segmentation performance was obtained compared to the segmentation results of the Meng hee heng k-means algorithm for the same test data specifications. DBSCAN separates a type of cloud in more detail that makes it easier to record each centroid of each cluster around the scene. It is even able to cluster small groups of clouds independently so that these small groups of clouds can also be detected as features.

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Assessment of satellite image segmentation in RGB and HSV color space using image quality measures

Cited by 53 publications

References 7 publications

A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops

A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops

An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis

Improvement of Segmentation Performance for Feature Extraction on Whirlwind Cloud-based Satellite Image using DBSCAN Clustering Algorithm

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