Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images

Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.

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APPLICABILITY OF MULTI-SEASONAL X-BAND SAR IMAGERY FOR MULTIRESOLUTION SEGMENTATION: A CASE STUDY IN A RIPARIAN MIXED FOREST

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

10.5194/isprsarchives-xl-1-w5-123-2015 ◽

2015 ◽

Vol XL-1-W5 ◽

pp. 123-128

Author(s):

Z. Dabiri ◽

D. Hölbling ◽

S. Lang ◽

A. Bartsch

Keyword(s):

Scale Parameter ◽

Spatial Scales ◽

Mixed Forest ◽

Scale Parameters ◽

Image Objects ◽

Object Based ◽

Multi Temporal ◽

Reference Map ◽

Sar Imagery ◽

Multiresolution Segmentation

The increasing availability of synthetic aperture radar (SAR) data from a range of different sensors necessitates efficient methods for semi-automated information extraction at multiple spatial scales for different fields of application. The focus of the presented study is two-fold: 1) to evaluate the applicability of multi-temporal TerraSAR-X imagery for multiresolution segmentation, and 2) to identify suitable Scale Parameters through different weighing of different homogeneity criteria, mainly colour variance. Multiresolution segmentation was used for segmentation of multi-temporal TerraSAR-X imagery, and the ESP (Estimation of Scale Parameter) tool was used to identify suitable Scale Parameters for image segmentation. The validation of the segmentation results was performed using very high resolution WorldView-2 imagery and a reference map, which was created by an ecological expert. The results of multiresolution segmentation revealed that in the context of object-based image analysis the TerraSAR-X images are applicable for generating optimal image objects. Furthermore, ESP tool can be used as an indicator for estimation of Scale Parameter for multiresolution segmentation of TerraSAR-X imagery. Additionally, for more reliable results, this study suggests that the homogeneity criterion of colour, in a variance based segmentation algorithm, needs to be set to high values. Setting the shape/colour criteria to 0.005/0.995 or 0.00/1 led to the best results and to the creation of adequate image objects.

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Optimal Segmentation Scale Parameter, Feature Subset and Classification Algorithm for Geographic Object-Based Crop Recognition Using Multisource Satellite Imagery

Remote Sensing ◽

10.3390/rs11050514 ◽

2019 ◽

Vol 11 (5) ◽

pp. 514 ◽

Cited By ~ 7

Author(s):

Lingbo Yang ◽

Lamin Mansaray ◽

Jingfeng Huang ◽

Limin Wang

Keyword(s):

Satellite Imagery ◽

Scale Parameter ◽

Selection Method ◽

Classification Algorithm ◽

Feature Subset Selection ◽

Computational Time ◽

Feature Subset ◽

Object Based ◽

Geographic Object ◽

Crop Classification

Geographic object-based image analysis (GEOBIA) has been widely used in the remote sensing of agricultural crops. However, issues related to image segmentation, data redundancy and performance of different classification algorithms with GEOBIA have not been properly addressed in previous studies, thereby compromising the accuracy of subsequent thematic products. It is in this regard that the current study investigates the optimal scale parameter (SP) in multi-resolution segmentation, feature subset, and classification algorithm for use in GEOBIA based on multisource satellite imagery. For this purpose, a novel supervised optimal SP selection method was proposed based on information gain ratio, and was then compared with a preexisting unsupervised optimal SP selection method. Additionally, the recursive feature elimination (RFE) and enhanced RFE (EnRFE) algorithms were modified to generate an improved EnRFE (iEnRFE) algorithm, which was then compared with its precursors in the selection of optimal classification features. Based on the above, random forest (RF), gradient boosting decision tree (GBDT) and support vector machine (SVM) were applied to segmented objects for crop classification. The results indicated that the supervised optimal SP selection method is more suitable for application in heterogeneous land cover, whereas the unsupervised method proved more efficient as it does not require reference segmentation objects. The proposed iEnRFE method outperformed the preexisting EnRFE and RFE methods in optimal feature subset selection as it recorded the highest accuracy and less processing time. The RF, GBDT, and SVM algorithms achieved overall classification accuracies of 91.8%, 92.4%, and 90.5%, respectively. GBDT and RF recorded higher classification accuracies and utilized much less computational time than SVM and are, therefore, considered more suitable for crop classification requiring large numbers of image features. These results have shown that the proposed object-based crop classification scheme could provide a valuable reference for relevant applications of GEOBIA in crop recognition using multisource satellite imagery.

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Evaluating the Effects of Image Texture Analysis on Plastic Greenhouse Segments via Recognition of the OSI-USI-ETA-CEI Pattern

Remote Sensing ◽

10.3390/rs11030231 ◽

2019 ◽

Vol 11 (3) ◽

pp. 231 ◽

Cited By ~ 1

Author(s):

Yao Yao ◽

Shixin Wang

Keyword(s):

Texture Analysis ◽

Visual Analysis ◽

Scale Parameter ◽

Image Texture ◽

Quantization Level ◽

Image Texture Analysis ◽

Segmentation Evaluation ◽

Object Based ◽

Geographic Object ◽

Error Index

Compared to multispectral or panchromatic bands, fusion imagery contains both the spectral content of the former and the spatial resolution of the latter. Even though the Estimation of Scale Parameter (ESP), the ESP 2 tool, and some segmentation evaluation methods have been introduced to simplify the choice of scale parameter (SP), shape, and compactness, many challenges remain, including obtaining the natural border of plastic greenhouses (PGs) from a GaoFen-2 (GF-2) fusion imagery, accelerating the progress of follow-up texture analysis, and accurately evaluating over-segmentation and under-segmentation of PG segments in geographic object-based image analysis. Considering the features of high-resolution images, the heterogeneity of fusion imagery was compressed using texture analysis before calculating the optimal scale parameter in ESP 2 in this study. As a result, we quantified the effects of image texture analysis, including increasing averaging operator size (AOS) and decreasing greyscale quantization level (GQL) on PG segments via recognition of a proposed Over-Segmentation Index (OSI)-Under-Segmentation Index (USI)-Error Index of Total Area (ETA)-Composite Error Index (CEI) pattern. The proposed pattern can be used to reasonably evaluate the quality of PG segments obtained from GF-2 fusion imagery and its derivative images, showing that appropriate texture analysis can effectively change the heterogeneity of a fusion image for better segmentation. The optimum setup of GQL and AOS are determined by comparing CEI and visual analysis.

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A Model-based Parameter Optimization Control Strategy for Trajectory Tracking with Torque and Velocity Constraints

10.21203/rs.3.rs-975228/v1 ◽

2021 ◽

Author(s):

Min Luo ◽

Xiaorong Hou ◽

Xiaoxue Li ◽

Jinbo Lu ◽

Jing Yang

Keyword(s):

Parameter Optimization ◽

Trajectory Tracking ◽

Learning Strategy ◽

Optimization Methods ◽

Optimization Method ◽

Settling Time ◽

Trajectory Tracking Control ◽

Time Index ◽

Model Based ◽

Time Optimization

Abstract The wheeled robots trajectory tracking control methods rarely constrain the torque and speed at the same time. In actual application, the torque and speed of the robot cannot exceed the saturation limit of the actuator. This paper develops a model-based trajectory tracking parameter optimization controller with both velocity and torque constraints, using a gradient descent parameter iterative learning strategy to minimize the settling time index of the system. Trajectory tracking time optimization methods usually require a given analytical expression of the system time, while this time optimization method only requires that the settling time is solvable. The MATLAB simulation experiments show that the proposed parameter optimization controller for trajectory tracking can perform velocity and torque constraints while having a relatively good overall rapidity time index. If the resolution of the robot sensor can meet the design requirements, the optimization method can strictly control the system torque maximum to a reasonably small expected value. When the resolution of the robot sensor is limited, this optimization method can restrict the system torque maximum within a reasonable saturation constraint range.

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Optimizing the Segmentation of a High-Resolution Image by Using a Local Scale Parameter

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.87.7.503 ◽

2021 ◽

Vol 87 (7) ◽

pp. 503-511

Author(s):

Lei Zhang ◽

Hongchao Liu ◽

Xiaosong Li ◽

Xinyu Qian

Keyword(s):

Land Cover ◽

Scale Parameter ◽

Single Layer ◽

Remote Sensing Data ◽

Scale Parameters ◽

Object Based ◽

Segmentation Approach ◽

Optimal Segmentation ◽

Segment Matching

Image segmentation is a critical procedure in object-based identification and classification of remote sensing data. However, optimal scale-parameter selection presents a challenge, given the presence of complex landscapes and uncertain feature changes. This study proposes a local optimal segmentation approach that considers both intersegment heterogeneity and intrasegment homogeneity, uses the standard deviation and local Moran's index to explore each optimal segment across different scale parameters, and combines the optimal segments into a single layer. The optimal segment is measured by using high-spatial-resolution images. Results show that our approach out-performs and generates less error than the global optimal segmentation approach. The variety of land cover types or intrasegment homogeneity leads to segment matching with the geo-objects on different scales. Local optimal segmentation demonstrates sensitivity to land cover discrepancy and provides good performance on cross-scale segmentation.

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A New Hybrid Method for Parameter Optimization of SVR

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2018.p0271 ◽

2018 ◽

Vol 22 (2) ◽

pp. 271-279 ◽

Cited By ~ 1

Author(s):

Jiang Xie ◽

Taifeng Sun ◽

Jieyu Zhang ◽

Wu Zhang ◽

◽

...

Keyword(s):

Parameter Optimization ◽

Hybrid Method ◽

Scatter Search ◽

Back Propagation ◽

Optimization Methods ◽

Optimization Method ◽

Back Propagation Neural Network ◽

Support Vector ◽

Local Optimum ◽

Improvement Method

The performance of Support Vector Regression (SVR) depends heavily on its parameters, but some optimization methods based on Grid Search (GS) or evolutionary algorithms still have several issues that must be addressed. This paper proposes a new hybrid method (PSO-SS) that combines Particle Swarm Optimization (PSO) and Scatter Search (SS) to optimize the parameters of the SVR. In PSO-SS, to improve the search capability of PSO and reduce the likelihood of the PSO becoming trapped in the local optimum, the initial PSO population is generated by the diversification generation method and the improvement method of SS, and the velocity updating formula of PSO is improved by adding diversity information. On the StatLib and UCI datasets, our experiments show that the PSO-SS method is an effective parameter optimization method compared with other methods. In addition, an SVR model with its parameters optimized by PSO-SS (PSO-SS-SVR) is used to predict the grain size of aluminum alloys. The experimental results show that the PSO-SS-SVR method outperforms Back Propagation Neural Network (BPNN), PSO-SVR and the empirical model.

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Enhanced Success History Adaptive DE for Parameter Optimization of Photovoltaic Models

Complexity ◽

10.1155/2021/6660115 ◽

2021 ◽

Vol 2021 ◽

pp. 1-22 ◽

Cited By ~ 2

Author(s):

Yingjie Song ◽

Daqing Wu ◽

Ali Wagdy Mohamed ◽

Xiangbing Zhou ◽

Bin Zhang ◽

...

Keyword(s):

Parameter Optimization ◽

Time Complexity ◽

Method Comparison ◽

Optimization Methods ◽

Optimization Method ◽

Pv Systems ◽

The Past ◽

Comparison Results ◽

Mutation Strategy ◽

Poor Stability

In the past few decades, a lot of optimization methods have been applied in estimating the parameter of photovoltaic (PV) models and obtained better results, but these methods still have some deficiencies, such as higher time complexity and poor stability. To tackle these problems, an enhanced success history adaptive DE with greedy mutation strategy (EBLSHADE) is employed to optimize parameters of PV models to propose a parameter optimization method in this paper. In the EBLSHADE, the linear population size reduction strategy is used to gradually reduce population to improve the search capabilities and balance the exploitation and exploration capabilities. The less and more greedy mutation strategy is used to enhance the exploitation capability and the exploration capability. Finally, a parameter optimization method based on EBLSHADE is proposed to optimize parameters of PV models. The different PV models are selected to prove the effectiveness of the proposed method. Comparison results demonstrate that the EBLSHADE is an effective and efficient method and the parameter optimization method is beneficial to design, control, and optimize the PV systems.

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