Feature Extraction and Object Detection Using Fast-Convolutional Neural Network for Remote Sensing Satellite Image

Devi, N. Bharatha; Kavida, A. Celine; Murugan, R.

doi:10.1007/s12524-022-01506-x

Cited by 15 publications

(4 citation statements)

References 23 publications

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“…This characteristic is more pronounced when the input data is a multi-bit image. At the same time, because of their processing characteristics, images can be directly used as input data for training or classification, and their application is more convenient and efficient [4].…”

Section: Algorithm Principle Of Convolutional Neural Networkmentioning

confidence: 99%

Application of Convolutional Neural Networks in High Score Remote Sensing Image Classification

2024

AJCIS

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Remote sensing image classification is a critical link in remote sensing. Traditional remote sensing image classification is based on shallow structure model algorithms such as SVM and decision tree. However, when faced with high-resolution remote sensing images, due to a large amount of data and complex data features, the recognition accuracy of traditional shallow models has been unable to meet the current needs. When faced with image classification, convolutional neural networks can better cope with image translation, scaling, and other deformations and effectively reduce the errors introduced by the above factors. At the same time, the convolutional neural network can capture and extract complex signal features with the help of a deeper feature processing level to obtain a better classification effect. This paper discusses the application of convolutional neural networks to classify high-resolution remote sensing images and provides some references for remote sensing image processing and target recognition.

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Section: Algorithm Principle Of Convolutional Neural Networkmentioning

confidence: 99%

Application of Convolutional Neural Networks in High Score Remote Sensing Image Classification

2024

AJCIS

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“…In machine learning, a thorough dataset is created that includes all of the system parameters. ML is beneficial in situations where theoretical knowledge alone is insufficient to anticipate some facts (5,6) . It has a wide range of applications, including land use and cover problems (7) , disaster management, and climate change (8) .…”

Section: Introductionmentioning

confidence: 99%

Image Analysis and Classification Using HRSVM-CNN for Land-Cover Classification by Using Remote Sensing Images

Vinuja,

Devi,

Mary

2024

IJST

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Objective: To effectively analyze diverse satellite images and derive valuable insights, it's crucial to employ efficient methods for classification and image processing. However, due to imperfections in image formats and sensor data, satellite imagery often contains flaws and inaccuracies, requiring extra steps to enhance its quality. The proposed solution involves two key techniques: segmenting the input image using a Hybrid HRSVM-CNN and classifying the resulting high-resolution remote sensing image using a Convolutional Neural Network. This combined approach addresses the challenges posed by image inconsistencies and aims to improve the accuracy and efficiency of current methods for satellite image analysis. Methods: In this research, a high-resolution Support Vector Machine-Convolutional Neural Network (Hybrid HRSVM-CNN) and texture characteristics are used to create an automated land identification method for satellite Remote sensing (RS) images. This approach's main focus is segmentation using the Bendlet Transform and Improved Chan-Vese, and it also does classification using a Hybrid HRSVM-CNN based on feature extraction and gray-level co-occurrence matrix algorithm. Findings: The proposed classification method's accuracy was evaluated against several other classification algorithms, including Semi-Supervised Graph Based Method (SSG), Conditional Random Fields (CRF), k-Nearest Neighbor (KNN), and Bi-layer Graph-based Learning (BLGL), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). When compared with existing methods, the findings of the proposed method display excellent accuracy of 98.83%. Novelty: To start, an adaptive median filter is used to pre-process the satellite Remote sensing images, removing unwanted noises and other impacts. Following pre-processing, the image is segmented using the Bendlet Transform and Improved Chan-Vese algorithms. Gray-level co-occurrence matrix is utilized to extract texture information, and the Hybrid HRSVM-CNN is then used to categorize the various types of land. These applications frequently have a variety of issues that affect the categorization accuracy. A few significant factors, like location, irregularity, form, and diameter, reduce the process's overall accuracy. This research focuses on presenting a unique Land-Cover classification model in order to address such problems. The UC Merced Land Use dataset was considered in this research. Keywords: Bendlet Transform and Improved ChanVese Segmentation, Hybrid HRSVMCNN, Land detection system, Satellite remote sensing images, Adaptive median filter, Gray level cooccurrence matrix

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“…Fully Connected Networks (FCN) were initially implemented in 2015 for semantic segmentation. These FCNs, however, were not effective in preserving spatial information [7,12]. U-shaped network topologies were suggested as a solution to this issue.…”

Section: Introductionmentioning

confidence: 99%

Road segmentation: exploiting the efficiency of skip connections for efficient semantic segmentation

Madhumita

Hariharan

Devendra

et al. 2023

J. S. Asian Logistics Transport

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Extraction of features is a unique approach that has several real-time applications. In remote sensing, road feature extraction includes recognising and extracting essential information about roads from high-resolution remote sensing photos. This data may include the shape, size, direction, and connection of roadways and their surroundings we can increase the accuracy and efficiency of road recognition algorithms and better comprehend the geographical distribution of roads in metropolitan settings. Nevertheless, traditional techniques of road recognition sometimes rely on manual labour or hand-crafted characteristics, which may be costly and time-consuming. Convolutional neural networks (CNNs) have been frequently employed for road detection due to their capacity to automatically learn characteristics from data. In this research, we provide Modified LinkNet for the extraction of road features from high-resolution remote-sensing images. The proposed model incorporates several variables resulting in superior performance compared to current cutting-edge road detection techniques. SpaceNet road detection dataset yields 0.81 mIoU, which is approximately 0.1, 0.11, and 0.03 mIoU greater than DeeplabV3+, and LinkNet, respectively (base architecture). In addition, the training of the model enables more efficient inference making our method applicable to real-time applications and are therefore a more effective and efficient solution for the extraction of road features from remote sensing images.

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Feature Extraction and Object Detection Using Fast-Convolutional Neural Network for Remote Sensing Satellite Image

Cited by 15 publications

References 23 publications

Application of Convolutional Neural Networks in High Score Remote Sensing Image Classification

Application of Convolutional Neural Networks in High Score Remote Sensing Image Classification

Image Analysis and Classification Using HRSVM-CNN for Land-Cover Classification by Using Remote Sensing Images

Road segmentation: exploiting the efficiency of skip connections for efficient semantic segmentation

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