Satellite Image Classification and Segmentation by Using JSEG Segmentation Algorithm

Abbas, Khamael; Rydh, Mustafa

doi:10.5815/ijigsp.2012.10.07

Cited by 11 publications

(3 citation statements)

References 12 publications

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“…The best-known region grooving RSIS algorithm is JSEG [ 16 ]. JSEG is applied and uses the color and texture characteristics of the image to define the growth rules.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fuzzy-Based Remote Sensing Image Segmentation Method

Cardone,

Di Martino,

Miraglia

2023

Sensors

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Image segmentation is a well-known image processing task that consists of partitioning an image into homogeneous areas. It is applied to remotely sensed imagery for many problems such as land use classification and landscape changes. Recently, several hybrid remote sensing image segmentation techniques have been proposed that include metaheuristic approaches in order to increase the segmentation accuracy; however, the critical point of these approaches is the high computational complexity, which affects time and memory consumption. In order to overcome this criticality, we propose a fuzzy-based image segmentation framework implemented in a GIS-based platform for remotely sensed images; furthermore, the proposed model allows us to evaluate the reliability of the segmentation. The Fast Generalized Fuzzy c-means algorithm is implemented to segment images in order to detect local spatial relations between pixels and the Triple Center Relation validity index is used to find the optimal number of clusters. The framework elaborates the composite index to be analyzed starting by multiband remotely sensed images. For each cluster, a segmented image is obtained in which the pixel value represents, transformed into gray levels, the graph belonging to the cluster. A final thematic map is built in which the pixels are classified based on the assignment to the cluster to which they belong with the highest membership degree. In addition, the reliability of the classification is estimated by associating each class with the average of the membership degrees of the pixels assigned to it. The method was tested in the study area consisting of the south-western districts of the city of Naples (Italy) for the segmentation of composite indices maps determined by multiband remote sensing images. The segmentation results are consistent with the segmentations of the study area by morphological and urban characteristics, carried out by domain experts. The high computational speed of the proposed image segmentation method allows it to be applied to massive high-resolution remote sensing images.

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“…The best-known region grooving RSIS algorithm is JSEG [ 16 ]. JSEG is applied and uses the color and texture characteristics of the image to define the growth rules.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fuzzy-Based Remote Sensing Image Segmentation Method

Cardone,

Di Martino,

Miraglia

2023

Sensors

View full text Add to dashboard Cite

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“…The analyst must choose a classifier that will accomplish the best for a certain task. Now a days, it is difficult to state which classifier is optimum for all situations as the characteristic of each data set and the circumstances for each study vary so greatly [10]. There are two main approaches used in hyperspectral classification: Supervised and Unsupervised [9].…”

Section: Introductionmentioning

confidence: 99%

Supervised Classification Approaches to Analyze Hyperspectral Dataset

El_Rahman¹,

Aliady²,

Alrashed³

2015

IJIGSP

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In this paper, Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) classification approaches were used to classify hyperspectral image of Georgia, USA, using Environment of Visualizing Images (ENVI). It is a software application used to process and analyze geospatial imagery. Spatial, spectral subset and atmospheric correction have been performed for SAM and SID algorithms. Results showed that classification accuracy using the SAM approach was 72.67%, and SID classification accuracy was 73.12%. Whereas, the accuracy of SID approach is better than SAM approach. Consequently, the two approaches (SID and SAM) have proven to be accurately converged in classification of hyperspectral image of Georgia, USA.

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“…Image classification is viewed as an important aspect of remote sensing, image analysis, and pattern recognition (Abbas and Rydh, 2012). Image classification in remote sensing involves assigning pixels or the basic units of an image to classes.…”

Section: Supervised Classifiermentioning

confidence: 99%

Oil Spill Hyperspectral Data Analysis: Using Minimum Distance and Binary Encoding Algorithms

El_Rahman¹

2017

IJCNT

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Abstract:Oil spill calamities have increased, threating maritime ecosystems. This reinforces the need for accurate mapping of oilspill calamities. The use of hyperspectral classifiers to extract areas of oil spill in a test site was achieved in this work. The paper describes the effects of utilizing a set of hyperspectral image analysis algorithms such as Minimum Distance (MD) and Binary Encoding (BE) algorithms to classify hyperspectral images of oil-spill areas in the Gulf of Mexico using Environment for Visualizing Images software. Hyperspectral image subseting, region of interest and principal component analysis were performed in the preprocessing stage, which is used to reduce the vast amount of data and eliminate redundant data. The paper provides empirical insights on the classification accuracy of hyperspectral images. A confusion matrix is used to determine the accuracy of a classification by comparing a classification result with ground truth information. The overall accuracies were 94.6399% and 88.4422% for the MD and BE algorithms, respectively. Therefore, the two algorithms are accurate for classifying hyperspectral images of the Gulf of Mexico. However, the MD algorithm is more accurate than the BE algorithm.

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Satellite Image Classification and Segmentation by Using JSEG Segmentation Algorithm

Cited by 11 publications

References 12 publications

A Novel Fuzzy-Based Remote Sensing Image Segmentation Method

A Novel Fuzzy-Based Remote Sensing Image Segmentation Method

Supervised Classification Approaches to Analyze Hyperspectral Dataset

Oil Spill Hyperspectral Data Analysis: Using Minimum Distance and Binary Encoding Algorithms

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