scholarly journals Satellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm

2021 ◽  
Vol 13 (21) ◽  
pp. 4351
Author(s):  
Kowsalya Thiagarajan ◽  
Mukunthan Manapakkam Anandan ◽  
Andrzej Stateczny ◽  
Parameshachari Bidare Divakarachari ◽  
Hemalatha Kivudujogappa Lingappa

Satellite image classification is widely used in various real-time applications, such as the military, geospatial surveys, surveillance and environmental monitoring. Therefore, the effective classification of satellite images is required to improve classification accuracy. In this paper, the combination of Hierarchical Framework and Ensemble Learning (HFEL) and optimal feature selection is proposed for the precise identification of satellite images. The HFEL uses three different types of Convolutional Neural Networks (CNN), namely AlexNet, LeNet-5 and a residual network (ResNet), to extract the appropriate features from images of the hierarchical framework. Additionally, the optimal features from the feature set are extracted using the Correlation Coefficient-Based Gravitational Search Algorithm (CCGSA). Further, the Multi Support Vector Machine (MSVM) is used to classify the satellite images by extracted features from the fully connected layers of the CNN and selected features of the CCGSA. Hence, the combination of HFEL and CCGSA is used to obtain the precise classification over different datasets such as the SAT-4, SAT-6 and Eurosat datasets. The performance of the proposed HFEL–CCGSA is analyzed in terms of accuracy, precision and recall. The experimental results show that the HFEL–CCGSA method provides effective classification over the satellite images. The classification accuracy of the HFEL–CCGSA method is 99.99%, which is high when compared to AlexNet, LeNet-5 and ResNet.

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Youchuan Wan ◽  
Mingwei Wang ◽  
Zhiwei Ye ◽  
Xudong Lai

Texture image classification is an important topic in many applications in machine vision and image analysis. Texture feature extracted from the original texture image by using “Tuned” mask is one of the simplest and most effective methods. However, hill climbing based training methods could not acquire the satisfying mask at a time; on the other hand, some commonly used evolutionary algorithms like genetic algorithm (GA) and particle swarm optimization (PSO) easily fall into the local optimum. A novel approach for texture image classification exemplified with recognition of residential area is detailed in the paper. In the proposed approach, “Tuned” mask is viewed as a constrained optimization problem and the optimal “Tuned” mask is acquired by maximizing the texture energy via a newly proposed gravitational search algorithm (GSA). The optimal “Tuned” mask is achieved through the convergence of GSA. The proposed approach has been, respectively, tested on some public texture and remote sensing images. The results are then compared with that of GA, PSO, honey-bee mating optimization (HBMO), and artificial immune algorithm (AIA). Moreover, feature extracted by Gabor wavelet is also utilized to make a further comparison. Experimental results show that the proposed method is robust and adaptive and exhibits better performance than other methods involved in the paper in terms of fitness value and classification accuracy.


2019 ◽  
Vol 11 (5) ◽  
pp. 546 ◽  
Author(s):  
Mohammad Firozjaei ◽  
Iman Daryaei ◽  
Amir Sedighi ◽  
Qihao Weng ◽  
Seyed Alavipanah

Image classification is one of the most common methods of information extraction from satellite images. In this paper, a novel algorithm for image classification based on gravity theory was developed, which was called “homogeneity distance classification algorithm (HDCA)”. The proposed HDCA used texture and spectral information for classifying images in two iterative supplementary computing stages: (1) merging, (2) traveling and escaping operators. The HDCA was equipped by a new concept of distance, the weighted Manhattan distance (WMD). Moreover, an improved gravitational search algorithm (IGSA) was applied for selecting features and determining optimal feature space scale in HDCA. In the case of multispectral satellite image classification, the proposed method was compared with two well-known classification methods, Maximum Likelihood classifier (MLC) and Support Vector Machine (SVM). The results of the comparison indicated that overall accuracy values for HDCA, MLC, and SVM are 95.99, 93.15, and 95.00, respectively. Furthermore, the proposed HDCA method was also used for classifying hyperspectral reference datasets (Indian Pines, Salinas and Salinas-A scene). The classification results indicated substantial improvement over previous algorithms and studies by 2% in Indian Pines dataset, 0.7% in the Salinas dataset and 1.2% in the Salinas-A scene. These experimental results demonstrate that the proposed algorithm can classify both multispectral and hyperspectral remote sensing images with reliable accuracy because this algorithm uses the WMD in the classification process and the IGSA to select automatically optimal features for image classification based on spectral and texture information.


2016 ◽  
Vol 3 (4) ◽  
pp. 1-11
Author(s):  
M. Lakshmikantha Reddy ◽  
◽  
M. Ramprasad Reddy ◽  
V.C. Veera Reddy ◽  
◽  
...  

Author(s):  
Umit Can ◽  
Bilal Alatas

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.


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