Agricultural Plantation Classification using Transfer Learning Approach based on CNN

• URL: http://arxiv.org/abs/2206.09420v1
• Date: Sun, 19 Jun 2022 14:43:31 GMT
• Title: Agricultural Plantation Classification using Transfer Learning Approach based on CNN
• Authors: Uphar Singh, Tushar Musale, Ranjana Vyas, O.P.Vyas (Indian Institute of Information Technology, Allahabad, India)
• Abstract summary: The efficiency of hyper-spectral image recognition has increased significantly with deep learning. CNN and Multi-Layer Perceptron(MLP) has demonstrated to be an excellent process of classifying images. We propose using the method of transfer learning to decrease the training time and reduce the dependence on large labeled data-set.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyper-spectral images are images captured from a satellite that gives spatial and spectral information of specific region.A Hyper-spectral image contains much more number of channels as compared to a RGB image, hence containing more information about entities within the image. It makes them well suited for the classification of objects in a snap. In the past years, the efficiency of hyper-spectral image recognition has increased significantly with deep learning. The Convolution Neural Network(CNN) and Multi-Layer Perceptron(MLP) has demonstrated to be an excellent process of classifying images. However, they suffer from the issues of long training time and requirement of large amounts of the labeled data, to achieve the expected outcome. These issues become more complex while dealing with hyper-spectral images. To decrease the training time and reduce the dependence on large labeled data-set, we propose using the method of transfer learning.The features learned by CNN and MLP models are then used by the transfer learning model to solve a new classification problem on an unseen dataset. A detailed comparison of CNN and multiple MLP architectural models is performed, to determine an optimum architecture that suits best the objective. The results show that the scaling of layers not always leads to increase in accuracy but often leads to over-fitting, and also an increase in the training time.The training time is reduced to greater extent by applying the transfer learning approach rather than just approaching the problem by directly training a new model on large data-sets, without much affecting the accuracy.

Related papers

• Learned representation-guided diffusion models for large-image generation [58.192263311786824]
  We introduce a novel approach that trains diffusion models conditioned on embeddings from self-supervised learning (SSL) Our diffusion models successfully project these features back to high-quality histopathology and remote sensing images. Augmenting real data by generating variations of real images improves downstream accuracy for patch-level and larger, image-scale classification tasks.
  arXiv  Detail & Related papers  (2023-12-12T14:45:45Z)
• CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations [90.50864830038202]
  We present Contrastive Spatial Pre-Training (CSP), a self-supervised learning framework for geo-tagged images. We use a dual-encoder to separately encode the images and their corresponding geo-locations, and use contrastive objectives to learn effective location representations from images. CSP significantly boosts the model performance with 10-34% relative improvement with various labeled training data sampling ratios.
  arXiv  Detail & Related papers  (2023-05-01T23:11:18Z)
• Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification [3.9310727060473476]
  This paper proposes a new genetic programming-based evolutionary deep learning approach to data-efficient image classification. The new approach can automatically evolve variable-length models using many important operators from both image and classification domains. A flexible multi-layer representation enables the new approach to automatically construct shallow or deep models/trees for different tasks.
  arXiv  Detail & Related papers  (2022-09-27T08:10:16Z)
• Terrain Classification using Transfer Learning on Hyperspectral Images: A Comparative study [0.13999481573773068]
  convolutional neural network (CNN) and the Multi-Layer Perceptron (MLP) have been proven to be an effective method of image classification. However, they suffer from the issues of long training time and requirement of large amounts of the labeled data. We propose using the method of transfer learning to decrease the training time and reduce the dependence on large labeled dataset.
  arXiv  Detail & Related papers  (2022-06-19T14:36:33Z)
• Learning Enriched Features for Fast Image Restoration and Enhancement [166.17296369600774]
  This paper presents a holistic goal of maintaining spatially-precise high-resolution representations through the entire network. We learn an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Our approach achieves state-of-the-art results for a variety of image processing tasks, including defocus deblurring, image denoising, super-resolution, and image enhancement.
  arXiv  Detail & Related papers  (2022-04-19T17:59:45Z)
• Multiscale Convolutional Transformer with Center Mask Pretraining for Hyperspectral Image Classificationtion [14.33259265286265]
  We propose a noval multi-scale convolutional embedding module for hyperspectral images (HSI) to realize effective extraction of spatial-spectral information. Similar to Mask autoencoder, but our pre-training method only masks the corresponding token of the central pixel in the encoder, and inputs the remaining token into the decoder to reconstruct the spectral information of the central pixel.
  arXiv  Detail & Related papers  (2022-03-09T14:42:26Z)
• Few-Shot Learning for Image Classification of Common Flora [0.0]
  We will showcase our results from testing various state-of-the-art transfer learning weights and architectures versus similar state-of-the-art works in the meta-learning field for image classification utilizing Model-Agnostic Meta Learning (MAML) Our results show that both practices provide adequate performance when the dataset is sufficiently large, but that they both also struggle when data sparsity is introduced to maintain sufficient performance.
  arXiv  Detail & Related papers  (2021-05-07T03:54:51Z)
• Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples [11.025191332244919]
  We introduce new self-supervised learning (SSL) mechanism to obtain the high-performance pre-training model for RSIs scene classification from large unlabeled data. Experiments on three commonly used RSIs scene classification datasets demonstrated that this new learning paradigm outperforms the traditional dominant ImageNet pre-trained model. The insights distilled from our studies can help to foster the development of SSL in the remote sensing community.
  arXiv  Detail & Related papers  (2020-10-02T09:27:19Z)
• Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification [53.735029033681435]
  Transfer learning is a powerful methodology for adapting pre-trained deep neural networks on image recognition tasks to new domains. In this work, we demonstrate that adversarially-trained models transfer better than non-adversarially-trained models.
  arXiv  Detail & Related papers  (2020-07-11T22:48:42Z)
• RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr [60.07531696857743]
  Fine-tuning the deep convolution neural network(CNN) using a pre-trained model helps transfer knowledge learned from larger datasets to the target task. We propose RIFLE - a strategy that deepens backpropagation in transfer learning settings. RIFLE brings meaningful updates to the weights of deep CNN layers and improves low-level feature learning.
  arXiv  Detail & Related papers  (2020-07-07T11:27:43Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.