Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach
Volume-3 | Issue-2
Light Weight CNN based Robust Image Watermarking Scheme for Security
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Principle of 6G Wireless Networks: Vision, Challenges and Applications
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PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
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Is Internet becoming a Major Contributor for Global warming - The Online Carbon Footprint
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A Study on Various Task-Work Allocation Algorithms in Swarm Robotics
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AUTOMATION USING IOT IN GREENHOUSE ENVIRONMENT
Volume-1 | Issue-1
Principle of 6G Wireless Networks: Vision, Challenges and Applications
Volume-3 | Issue-4
Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach
Volume-3 | Issue-2
Light Weight CNN based Robust Image Watermarking Scheme for Security
Volume-3 | Issue-2
VIRTUAL REALITY GAMING TECHNOLOGY FOR MENTAL STIMULATION AND THERAPY
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Design of Digital Image Watermarking Technique with Two Stage Vector Extraction in Transform Domain
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Analysis of Natural Language Processing in the FinTech Models of Mid-21st Century
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PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
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Image Augmentation based on GAN deep learning approach with Textual Content Descriptors
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Comparative Analysis for Personality Prediction by Digital Footprints in Social Media
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Volume - 3 | Issue - 2 | june 2021
Published
10 July, 2021
Over the last decade, remote sensing technology has advanced dramatically, resulting in significant improvements on image quality, data volume, and application usage. These images have essential applications since they can help with quick and easy interpretation. Many standard detection algorithms fail to accurately categorize a scene from a remote sensing image recorded from the earth. A method that uses bilinear convolution neural networks to produce a less-weighted set of models those results in better visual recognition in remote sensing images using fine-grained techniques. This proposed hybrid method is utilized to extract scene feature information in two times from remote sensing images for improved recognition. In layman's terms, these features are defined as raw, and only have a single defined frame, so they will allow basic recognition from remote sensing images. This research work has proposed a double feature extraction hybrid deep learning approach to classify remotely sensed image scenes based on feature abstraction techniques. Also, the proposed algorithm is applied to feature values in order to convert them to feature vectors that have pure black and white values after many product operations. The next stage is pooling and normalization, which occurs after the CNN feature extraction process has changed. This research work has developed a novel hybrid framework method that has a better level of accuracy and recognition rate than any prior model.
Keywordsremote sensing image deep learning
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