�ݺ�ߣshows by User: RupeshKumar301638

�ݺ�ߣshows by User: RupeshKumar301638 / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: RupeshKumar301638 / Wed, 23 Apr 2025 20:31:55 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: RupeshKumar301638 Pansharpening using PCA python Implementation /slideshow/pansharpening-using-pca-python-implementation/278337776 presentation-250423203155-13227e0e
A Python implementation of Principal Component Analysis (PCA) based pansharpening for enhancing multispectral satellite imagery using high-resolution panchromatic data.]]>
A Python implementation of Principal Component Analysis (PCA) based pansharpening for enhancing multispectral satellite imagery using high-resolution panchromatic data.]]> Wed, 23 Apr 2025 20:31:55 GMT /slideshow/pansharpening-using-pca-python-implementation/278337776 RupeshKumar301638@slideshare.net(RupeshKumar301638) Pansharpening using PCA python Implementation RupeshKumar301638 A Python implementation of Principal Component Analysis (PCA) based pansharpening for enhancing multispectral satellite imagery using high-resolution panchromatic data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation-250423203155-13227e0e-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> A Python implementation of Principal Component Analysis (PCA) based pansharpening for enhancing multispectral satellite imagery using high-resolution panchromatic data.

Pansharpening using PCA python Implementation from RupeshKumar301638

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0 EarthMapper: A Toolbox for the Semantic Segmentation of Remote Sensing Imagery /slideshow/earthmapper-a-toolbox-for-the-semantic-segmentation-of-remote-sensing-imagery/277363911 earthmapperppt-250331122545-c69a31a2
Kemker, R., Gewali, U. B., Kanan, C. - "EarthMapper: A Toolbox for the Semantic Segmentation of Remote Sensing Imagery."]]>
Kemker, R., Gewali, U. B., Kanan, C. - "EarthMapper: A Toolbox for the Semantic Segmentation of Remote Sensing Imagery."]]> Mon, 31 Mar 2025 12:25:45 GMT /slideshow/earthmapper-a-toolbox-for-the-semantic-segmentation-of-remote-sensing-imagery/277363911 RupeshKumar301638@slideshare.net(RupeshKumar301638) EarthMapper: A Toolbox for the Semantic Segmentation of Remote Sensing Imagery RupeshKumar301638 Kemker, R., Gewali, U. B., Kanan, C. - "EarthMapper: A Toolbox for the Semantic Segmentation of Remote Sensing Imagery." <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/earthmapperppt-250331122545-c69a31a2-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Kemker, R., Gewali, U. B., Kanan, C. - "EarthMapper: A Toolbox for the Semantic Segmentation of Remote Sensing Imagery."

EarthMapper: A Toolbox for the Semantic Segmentation of Remote Sensing Imagery from RupeshKumar301638

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0 PCA-Based Pansharpening of Multispectral Images Using a Panchromatic Band /slideshow/pca-based-pansharpening-of-multispectral-images-using-a-panchromatic-band/276397527 presentation1-250307182642-08e0b448
PCA-Based Pansharpening of Multispectral Images Using a Panchromatic Band Remote Sensing - Computer Vision - Satellite Imagery - Satellite Image Analysis - PCA-Based Pansharpening]]>
PCA-Based Pansharpening of Multispectral Images Using a Panchromatic Band Remote Sensing - Computer Vision - Satellite Imagery - Satellite Image Analysis - PCA-Based Pansharpening]]> Fri, 07 Mar 2025 18:26:42 GMT /slideshow/pca-based-pansharpening-of-multispectral-images-using-a-panchromatic-band/276397527 RupeshKumar301638@slideshare.net(RupeshKumar301638) PCA-Based Pansharpening of Multispectral Images Using a Panchromatic Band RupeshKumar301638 PCA-Based Pansharpening of Multispectral Images Using a Panchromatic Band Remote Sensing - Computer Vision - Satellite Imagery - Satellite Image Analysis - PCA-Based Pansharpening <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/presentation1-250307182642-08e0b448-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> PCA-Based Pansharpening of Multispectral Images Using a Panchromatic Band Remote Sensing - Computer Vision - Satellite Imagery - Satellite Image Analysis - PCA-Based Pansharpening

PCA-Based Pansharpening of Multispectral Images Using a Panchromatic Band from RupeshKumar301638

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0 Synthetic Image Data Generation using GAN &Triple GAN.pptx /slideshow/synthetic-image-data-generation-using-gan-triple-ganpptx/258617552 triplegan-230625152447-c45b526b
The presentation focuses on the utilization of different deep generative models for synthetic image generation. These models include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Auto-Regressive Models, and Flow-Based Models. Firstly, the presentation introduces VAEs, which are probabilistic models that aim to encode input images into a latent space and generate new images by sampling from this latent space. It explains the underlying principles of VAEs and their ability to generate diverse and realistic synthetic images. Next, the presentation delves into GANs, which involve two competing neural networks: a generator network and a discriminator network. The generator network generates synthetic images, while the discriminator network learns to distinguish between real and synthetic images. The presentation describes the training process and the theoretical basis of GANs. The presentation further explores Auto-Regressive Models, which model the joint probability distribution of the image pixels conditioned on previous pixels. It discusses how these models leverage the dependencies among pixels to generate coherent and high-quality synthetic images. Flow-Based Models, another class of generative models, are then introduced. These models learn a bijective transformation between a simple base distribution and the target distribution of images. The presentation explains how these models can generate images by sampling from the base distribution and applying the inverse transformation. Finally, the presentation highlights the Triple GAN, a specific type of GAN that exhibits superiority in synthetic image generation compared to other models and existing GANs. It discusses the unique characteristics of Triple GAN, such as its improved stability and ability to generate high-resolution images. The presentation supports these claims by providing mathematical proofs and presenting implementation results that demonstrate the superior performance of Triple GAN in generating realistic and diverse synthetic images. Overall, the presentation covers various deep generative models, their principles, and their applications in synthetic image generation. It emphasizes the superiority of Triple GAN, supported by mathematical proofs and implementation results, showcasing its advancements in this field.]]>
The presentation focuses on the utilization of different deep generative models for synthetic image generation. These models include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Auto-Regressive Models, and Flow-Based Models. Firstly, the presentation introduces VAEs, which are probabilistic models that aim to encode input images into a latent space and generate new images by sampling from this latent space. It explains the underlying principles of VAEs and their ability to generate diverse and realistic synthetic images. Next, the presentation delves into GANs, which involve two competing neural networks: a generator network and a discriminator network. The generator network generates synthetic images, while the discriminator network learns to distinguish between real and synthetic images. The presentation describes the training process and the theoretical basis of GANs. The presentation further explores Auto-Regressive Models, which model the joint probability distribution of the image pixels conditioned on previous pixels. It discusses how these models leverage the dependencies among pixels to generate coherent and high-quality synthetic images. Flow-Based Models, another class of generative models, are then introduced. These models learn a bijective transformation between a simple base distribution and the target distribution of images. The presentation explains how these models can generate images by sampling from the base distribution and applying the inverse transformation. Finally, the presentation highlights the Triple GAN, a specific type of GAN that exhibits superiority in synthetic image generation compared to other models and existing GANs. It discusses the unique characteristics of Triple GAN, such as its improved stability and ability to generate high-resolution images. The presentation supports these claims by providing mathematical proofs and presenting implementation results that demonstrate the superior performance of Triple GAN in generating realistic and diverse synthetic images. Overall, the presentation covers various deep generative models, their principles, and their applications in synthetic image generation. It emphasizes the superiority of Triple GAN, supported by mathematical proofs and implementation results, showcasing its advancements in this field.]]> Sun, 25 Jun 2023 15:24:47 GMT /slideshow/synthetic-image-data-generation-using-gan-triple-ganpptx/258617552 RupeshKumar301638@slideshare.net(RupeshKumar301638) Synthetic Image Data Generation using GAN &Triple GAN.pptx RupeshKumar301638 The presentation focuses on the utilization of different deep generative models for synthetic image generation. These models include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Auto-Regressive Models, and Flow-Based Models. Firstly, the presentation introduces VAEs, which are probabilistic models that aim to encode input images into a latent space and generate new images by sampling from this latent space. It explains the underlying principles of VAEs and their ability to generate diverse and realistic synthetic images. Next, the presentation delves into GANs, which involve two competing neural networks: a generator network and a discriminator network. The generator network generates synthetic images, while the discriminator network learns to distinguish between real and synthetic images. The presentation describes the training process and the theoretical basis of GANs. The presentation further explores Auto-Regressive Models, which model the joint probability distribution of the image pixels conditioned on previous pixels. It discusses how these models leverage the dependencies among pixels to generate coherent and high-quality synthetic images. Flow-Based Models, another class of generative models, are then introduced. These models learn a bijective transformation between a simple base distribution and the target distribution of images. The presentation explains how these models can generate images by sampling from the base distribution and applying the inverse transformation. Finally, the presentation highlights the Triple GAN, a specific type of GAN that exhibits superiority in synthetic image generation compared to other models and existing GANs. It discusses the unique characteristics of Triple GAN, such as its improved stability and ability to generate high-resolution images. The presentation supports these claims by providing mathematical proofs and presenting implementation results that demonstrate the superior performance of Triple GAN in generating realistic and diverse synthetic images. Overall, the presentation covers various deep generative models, their principles, and their applications in synthetic image generation. It emphasizes the superiority of Triple GAN, supported by mathematical proofs and implementation results, showcasing its advancements in this field. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/triplegan-230625152447-c45b526b-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> The presentation focuses on the utilization of different deep generative models for synthetic image generation. These models include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Auto-Regressive Models, and Flow-Based Models. Firstly, the presentation introduces VAEs, which are probabilistic models that aim to encode input images into a latent space and generate new images by sampling from this latent space. It explains the underlying principles of VAEs and their ability to generate diverse and realistic synthetic images. Next, the presentation delves into GANs, which involve two competing neural networks: a generator network and a discriminator network. The generator network generates synthetic images, while the discriminator network learns to distinguish between real and synthetic images. The presentation describes the training process and the theoretical basis of GANs. The presentation further explores Auto-Regressive Models, which model the joint probability distribution of the image pixels conditioned on previous pixels. It discusses how these models leverage the dependencies among pixels to generate coherent and high-quality synthetic images. Flow-Based Models, another class of generative models, are then introduced. These models learn a bijective transformation between a simple base distribution and the target distribution of images. The presentation explains how these models can generate images by sampling from the base distribution and applying the inverse transformation. Finally, the presentation highlights the Triple GAN, a specific type of GAN that exhibits superiority in synthetic image generation compared to other models and existing GANs. It discusses the unique characteristics of Triple GAN, such as its improved stability and ability to generate high-resolution images. The presentation supports these claims by providing mathematical proofs and presenting implementation results that demonstrate the superior performance of Triple GAN in generating realistic and diverse synthetic images. Overall, the presentation covers various deep generative models, their principles, and their applications in synthetic image generation. It emphasizes the superiority of Triple GAN, supported by mathematical proofs and implementation results, showcasing its advancements in this field.

Synthetic Image Data Generation using GAN &Triple GAN.pptx from RupeshKumar301638

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0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/presentation-250423203155-13227e0e-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/pansharpening-using-pca-python-implementation/278337776 Pansharpening using PC... https://cdn.slidesharecdn.com/ss_thumbnails/earthmapperppt-250331122545-c69a31a2-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/earthmapper-a-toolbox-for-the-semantic-segmentation-of-remote-sensing-imagery/277363911 EarthMapper: A Toolbox... https://cdn.slidesharecdn.com/ss_thumbnails/presentation1-250307182642-08e0b448-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/pca-based-pansharpening-of-multispectral-images-using-a-panchromatic-band/276397527 PCA-Based Pansharpenin...