Genetic Algorithm Based Feature Subset Selection for Land Cover/ Land Use Mapping Using Wavelet Packet Transform

Rajesh, S.; Arivazhagan, S.; Moses, K. Pratheep; Abisekaraj, R.

doi:10.1007/s12524-012-0208-5

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…6 shows the accuracy obtained from various feature reduction algorithms. Our proposed modified algorithm achieves 89.95% using 3 feature sets, firefly algorithm achieves 87.86 using 7 feature sets, and genetic algorithm achieves 84.3% using 17 feature sets without 3-D DWT [15]. Results from Tab.…”

Section: Experiments and Resultsmentioning

confidence: 94%

Adaptive Window Based 3-D Feature Selection for Multispectral Image Classification Using Firefly Algorithm

Rajakani¹,

Kavitha²,

Ramachandran³

2023

Computer Systems Science and Engineering

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Feature extraction is the most critical step in classification of multispectral image. The classification accuracy is mainly influenced by the feature sets that are selected to classify the image. In the past, handcrafted feature sets are used which are not adaptive for different image domains. To overcome this, an evolutionary learning method is developed to automatically learn the spatial-spectral features for classification. A modified Firefly Algorithm (FA) which achieves maximum classification accuracy with reduced size of feature set is proposed to gain the interest of feature selection for this purpose. For extracting the most efficient features from the data set, we have used 3-D discrete wavelet transform which decompose the multispectral image in all three dimensions. For selecting spatial and spectral features we have studied three different approaches namely overlapping window (OW-3DFS), non-overlapping window (NW-3DFS) adaptive window cube (AW-3DFS) and Pixel based technique. Fivefold Multiclass Support Vector Machine (MSVM) is used for classification purpose. Experiments conducted on Madurai LISS IV multispectral image exploited that the adaptive window approach is used to increase the classification accuracy.

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Section: Experiments and Resultsmentioning

confidence: 94%

Adaptive Window Based 3-D Feature Selection for Multispectral Image Classification Using Firefly Algorithm

Rajakani¹,

Kavitha²,

Ramachandran³

2023

Computer Systems Science and Engineering

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“…Over past few decades Land-cover /land -use mapping mainly using satellite or airborne imagery due to better-quality data availability and accessibility. Remote sensing image Classification using deep learning used in wide fields of slum detection [12], land cover land use mapping [13,14,15], agriculture land detection and urban planning. In classification feature extraction play a major and important role.…”

Section: Introductionmentioning

confidence: 99%

Fused LISS IV Image Classification using Deep Convolution Neural Networks

Maheswari¹,

Rajesh²

2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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These days, earth observation frameworks give a large number of heterogeneous remote sensing information. The most effective method to oversee such fulsomeness in utilizing its reciprocity is a vital test in current remote sensing investigation. Considering optical Very High Spatial Resolution (VHSR) images, satellites acquire both Multi Spectral (MS) and panchromatic (PAN) images at various spatial goals. Information fusion procedures manage this by proposing a technique to consolidate reciprocity among the various information sensors. Classification of remote sensing image by Deep learning techniques using Convolutional Neural Networks (CNN) is increasing a solid decent footing because of promising outcomes. The most significant attribute of CNN-based strategies is that earlier element extraction is not required which prompts great speculation capacities. In this article, we are proposing a novel Deep learning based SMDTR-CNN (Same Model with Different Training Round with Convolution Neural Network) approach for classifying fused (LISS IV + PAN) image next to image fusion. The fusion of remote sensing images from CARTOSAT-1 (PAN image) and IRS P6 (LISS IV image) sensor is obtained by Quantization Index Modulation with Discrete Contourlet Transform (QIM-DCT). For enhancing the image fusion execution, we remove specific commotions utilizing Bayesian channel by Adaptive Type-2 Fuzzy System. The outcomes of the proposed procedures are evaluated with respect to precision, classification accuracy and kappa coefficient. The results revealed that SMDTR-CNN with Deep Learning got the best all-around precision and kappa coefficient. Likewise, the accuracy of each class of fused images in LISS IV + PAN dataset is improved by 2% and 5%, respectively.

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“…The Daubechies (DB2) wavelet filter is used for decomposition, and Mahalanobis distance classifier is used as the classifier. Since the methodology results in many features, some of which are found to be not useful, the best among the wavelet packet statistical and wavelet packet co-occurrence textural feature sets is selected using genetic algorithm (Rajesh et al 2013). It outperforms the other feature reduction techniques like principal component analysis and linear discriminant analysis.…”

Section: Introductionmentioning

confidence: 99%

Land Cover/Land Use Mapping of LISS IV Imagery Using Object-Based Convolutional Neural Network with Deep Features

Rajesh¹,

Nisia²,

Arivazhagan³

et al. 2019

J Indian Soc Remote Sens

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The paper proposes a new method for classifying the LISS IV satellite images using deep learning method. Deep learning method is to automatically extract many features without any human intervention. The classification accuracy through deep learning is still improved by including object-based segmentation. The object-based deep feature learning method using CNN is used to accurately classify the remotely sensed images. The method is designed with the technique of extracting the deep features and using it for object-based classification. The proposed system extracts deep features using pre-defined filter values, thus increasing the overall performance of the process compared to randomly initialized filter values. The object-based classification method can preserve edge information in complex satellite images. To improve the classification accuracy and to reduce complexity, object-based deep learning technique is used. The proposed object-based deep learning approach is used to drastically increase the classification accuracy. Here, the remotely sensed images were used to classify the urban areas of Ahmadabad and Madurai cities. Experimental results show a better performance with the object-based classification.

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Genetic Algorithm Based Feature Subset Selection for Land Cover/ Land Use Mapping Using Wavelet Packet Transform

Cited by 6 publications

References 24 publications

Adaptive Window Based 3-D Feature Selection for Multispectral Image Classification Using Firefly Algorithm

Adaptive Window Based 3-D Feature Selection for Multispectral Image Classification Using Firefly Algorithm

Fused LISS IV Image Classification using Deep Convolution Neural Networks

Land Cover/Land Use Mapping of LISS IV Imagery Using Object-Based Convolutional Neural Network with Deep Features

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