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

Johnson, Brian Alan; Ma, Lei

doi:10.3390/rs12111772

Cited by 33 publications

(22 citation statements)

References 25 publications

(37 reference statements)

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“…Image segmentation aims to partition an image into several homogenous sections such that the combination of no two adjacent sections is homogenous. Segmentation is a difficult task due to poor resolution, unfavorable environmental conditions, ambiguous regions, and the presence of pixels with a weak local correlation in satellite images [ 96 , 97 ]. Metaheuristic methods are proper tools that can deal with the difficulty of discovering the homogeneity measure in the images [ 98 ].…”

Section: Resultsmentioning

confidence: 99%

Social Network Search for Solving Engineering Optimization Problems

Bayzidi

Talatahari

Saraee

et al. 2021

Computational Intelligence and Neuroscience

139

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In this paper, a new metaheuristic optimization algorithm, called social network search (SNS), is employed for solving mixed continuous/discrete engineering optimization problems. The SNS algorithm mimics the social network user’s efforts to gain more popularity by modeling the decision moods in expressing their opinions. Four decision moods, including imitation, conversation, disputation, and innovation, are real-world behaviors of users in social networks. These moods are used as optimization operators that model how users are affected and motivated to share their new views. The SNS algorithm was verified with 14 benchmark engineering optimization problems and one real application in the field of remote sensing. The performance of the proposed method is compared with various algorithms to show its effectiveness over other well-known optimizers in terms of computational cost and accuracy. In most cases, the optimal solutions achieved by the SNS are better than the best solution obtained by the existing methods.

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Section: Resultsmentioning

confidence: 99%

Social Network Search for Solving Engineering Optimization Problems

Bayzidi

Talatahari

Saraee

et al. 2021

Computational Intelligence and Neuroscience

139

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“…Image segmentation is the first step of OBIA that consists of dividing the image into relatively homogeneous objects. Then, the objects are used for image classification and change detection analysis based on their spectral, spatial, and contextual attributes [20]. Image classification was performed using the OBIA implemented in the semiautomatic feature extraction tool in ENVI 5.3.…”

Section: Image Classification and Change Detection Analysismentioning

confidence: 99%

“…The tasseled cap transformation indices are also extensively used because they can simplify the six Landsat optical bands into three orthogonal indices: brightness, greenness, and wetness [17]. However, the object-based image analysis (OBIA) and machine-learning algorithms have started to gain their advantages over the pixel-based approaches in land cover mapping for change detection analysis [15,[18][19][20]. Besides the spectral information, the OBIA also takes into account the spatial heterogeneity of pixels grouped into clusters to form objects.…”

Section: Introductionmentioning

confidence: 99%

46-Year (1973–2019) Permafrost Landscape Changes in the Hola Basin, Northeast China Using Machine Learning and Object-Oriented Classification

Șerban

Şerban

et al. 2021

Remote Sensing

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Land use and cover changes (LUCC) in permafrost regions have significant consequences on ecology, engineered systems, and the environment. Obtaining more details about LUCC is crucial for sustainable development, land conservation, and environment management. The Hola Basin (957 km2) in the northernmost part of Northeast China, a boreal forest landscape underlain by discontinuous, sporadic, and isolated permafrost, was selected for the case study. The LUCC was analyzed using the Landsat archive of satellite images from 1973 to 2019. A thematic change detection analysis was performed by combining the object-based image analysis (OBIA) and the Support Vector Machine (SVM) algorithm. Four types of LUCC (forest, grass, water, and anthropic) were extracted with an overall accuracy of 80% for 1973 and >90% for 1986, 2000, and 2019. Forest, the dominant class (750 km2 in 1973), declined by 88 km2 (11.8%) from 1973 to 1986 but had a recovery of 78 km2 (12.5%) from 2000 to 2019. Grass, the second-largest class (187 km2 in 1973), increased by 86 km2 (46.5%) between 1973 and 1986 and decreased by 90 km2 (40%) between 2000 and 2019. The anthropic class continuously increased from 10 km2 (1973) to 37 km2 (2019). Major features in LUCC are attributed to rapid population growth, resource exploitation, agriculture intensification, economic development, and frequent forest fires. Under a pronounced climate warming, these drivers have been accelerating the degradation of permafrost, subsequently triggering natural hazards and deteriorating the ecological environment. This study represents a benchmark for sustainable LUCC management in the Hola Basin, Northeast China.

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“…Figure 4 shows the workflow for classification of individual shrub species. When the input data pixel size (in this case, 1.5 cm) is smaller than the size of the vegetation patches, an OBIA approach is recommended [54,[61][62][63]. This approach consists of two parts: image segmentation and classification.…”

Section: Uav Rgb Image Segmentationmentioning

confidence: 99%

Classifying Individual Shrub Species in UAV Images—A Case Study of the Gobi Region of Northwest China

Ding

Zhang

et al. 2021

Remote Sensing

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Shrublands are the main vegetation component in the Gobi region and contribute considerably to its ecosystem. Accurately classifying individual shrub vegetation species to understand their spatial distributions and to effectively monitor species diversity in the Gobi ecosystem is essential. High-resolution remote sensing data create vegetation type inventories over large areas. However, high spectral similarity between shrublands and surrounding areas remains a challenge. In this study, we provide a case study that integrates object-based image analysis (OBIA) and the random forest (RF) model to classify shrubland species automatically. The Gobi region on the southern slope of the Tian Shan Mountains in Northwest China was analyzed using readily available unmanned aerial vehicle (UAV) RGB imagery (1.5 cm spatial resolution). Different spectral and texture index images were derived from UAV RGB images as variables for species classification. Principal component analysis (PCA) extracted features from different types of variable sets (original bands, original bands + spectral indices, and original bands + spectral indices + texture indices). We tested the ability of several non-parametric decision tree models and different types of variable sets to classify shrub species. Moreover, we analyzed three main shrubland areas comprising different shrub species and compared the prediction accuracies of the optimal model in combination with different types of variable sets. We found that the RF model could generate higher accuracy compared with the other two models. The best results were obtained using a combination of the optimal variable set and the RF model with an 88.63% overall accuracy and 0.82 kappa coefficient. Integrating OBIA and RF in the species classification process provides a promising method for automatic mapping of individual shrub species in the Gobi region and can reduce the workload of individual shrub species classification.

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

Cited by 33 publications

References 25 publications

Social Network Search for Solving Engineering Optimization Problems

Social Network Search for Solving Engineering Optimization Problems

46-Year (1973–2019) Permafrost Landscape Changes in the Hola Basin, Northeast China Using Machine Learning and Object-Oriented Classification

Classifying Individual Shrub Species in UAV Images—A Case Study of the Gobi Region of Northwest China

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