Enhancing crop classification accuracy by synthetic SAR-Optical data generation using deep learning

• URL: http://arxiv.org/abs/2402.02121v1
• Date: Sat, 3 Feb 2024 11:07:50 GMT
• Title: Enhancing crop classification accuracy by synthetic SAR-Optical data generation using deep learning
• Authors: Ali Mirzaei, Hossein Bagheri, and Iman Khosravi
• Abstract summary: In agricultural regions, the dominant crops typically consist of one or two specific types, while other crops are scarce. When collecting training samples to create a map of agricultural products, there is an abundance of samples from the dominant crops, forming the majority classes. Our findings demonstrate that the proposed method generates synthetic data with higher quality that can significantly increase the number of samples for minority classes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Crop classification using remote sensing data has emerged as a prominent research area in recent decades. Studies have demonstrated that fusing SAR and optical images can significantly enhance the accuracy of classification. However, a major challenge in this field is the limited availability of training data, which adversely affects the performance of classifiers. In agricultural regions, the dominant crops typically consist of one or two specific types, while other crops are scarce. Consequently, when collecting training samples to create a map of agricultural products, there is an abundance of samples from the dominant crops, forming the majority classes. Conversely, samples from other crops are scarce, representing the minority classes. Addressing this issue requires overcoming several challenges and weaknesses associated with traditional data generation methods. These methods have been employed to tackle the imbalanced nature of the training data. Nevertheless, they still face limitations in effectively handling the minority classes. Overall, the issue of inadequate training data, particularly for minority classes, remains a hurdle that traditional methods struggle to overcome. In this research, We explore the effectiveness of conditional tabular generative adversarial network (CTGAN) as a synthetic data generation method based on a deep learning network, in addressing the challenge of limited training data for minority classes in crop classification using the fusion of SAR-optical data. Our findings demonstrate that the proposed method generates synthetic data with higher quality that can significantly increase the number of samples for minority classes leading to better performance of crop classifiers.

Related papers

• Hyperspectral Imaging-Based Grain Quality Assessment With Limited Labelled Data [2.4797200957733576]
  We present a novel approach to grain quality assessment using hyperspectral imaging (HSI) and few-shot learning techniques. HSI offers a non-invasive, real-time alternative by capturing both spatial and spectral information. We evaluate the performance of few-shot classifiers in two scenarios: first, classification of grain types seen during training, and second, generalisation to unseen grain types.
  arXiv  Detail & Related papers  (2024-11-17T01:02:18Z)
• Granularity Matters in Long-Tail Learning [62.30734737735273]
  We offer a novel perspective on long-tail learning, inspired by an observation: datasets with finer granularity tend to be less affected by data imbalance. We introduce open-set auxiliary classes that are visually similar to existing ones, aiming to enhance representation learning for both head and tail classes. To prevent the overwhelming presence of auxiliary classes from disrupting training, we introduce a neighbor-silencing loss.
  arXiv  Detail & Related papers  (2024-10-21T13:06:21Z)
• Artificial Data Point Generation in Clustered Latent Space for Small Medical Datasets [4.542616945567623]
  This paper introduces a novel method, Artificial Data Point Generation in Clustered Latent Space (AGCL) AGCL is designed to enhance classification performance on small medical datasets through synthetic data generation. It was applied to Parkinson's disease screening, utilizing facial expression data.
  arXiv  Detail & Related papers  (2024-09-26T09:51:08Z)
• Tackling Diverse Minorities in Imbalanced Classification [80.78227787608714]
  Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. We propose generating synthetic samples iteratively by mixing data samples from both minority and majority classes. We demonstrate the effectiveness of our proposed framework through extensive experiments conducted on seven publicly available benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-28T18:48:34Z)
• Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery [101.18253437732933]
  We present an Agave tequilana Weber azul crop segmentation and maturity classification using very high resolution satellite imagery. We solve real-world deep learning problems in the very specific context of agave crop segmentation. With the resulting accurate models, agave production forecasting can be made available for large regions.
  arXiv  Detail & Related papers  (2023-03-21T03:15:29Z)
• Generative models-based data labeling for deep networks regression: application to seed maturity estimation from UAV multispectral images [3.6868861317674524]
  Monitoring seed maturity is an increasing challenge in agriculture due to climate change and more restrictive practices. Traditional methods are based on limited sampling in the field and analysis in laboratory. We propose a method for estimating parsley seed maturity using multispectral UAV imagery, with a new approach for automatic data labeling.
  arXiv  Detail & Related papers  (2022-08-09T09:06:51Z)
• Scale-Equivalent Distillation for Semi-Supervised Object Detection [57.59525453301374]
  Recent Semi-Supervised Object Detection (SS-OD) methods are mainly based on self-training, generating hard pseudo-labels by a teacher model on unlabeled data as supervisory signals. We analyze the challenges these methods meet with the empirical experiment results. We introduce a novel approach, Scale-Equivalent Distillation (SED), which is a simple yet effective end-to-end knowledge distillation framework robust to large object size variance and class imbalance.
  arXiv  Detail & Related papers  (2022-03-23T07:33:37Z)
• Imbalanced Classification via Explicit Gradient Learning From Augmented Data [0.0]
  We propose a novel deep meta-learning technique to augment a given imbalanced dataset with new minority instances. The advantage of the proposed method is demonstrated on synthetic and real-world datasets with various imbalance ratios.
  arXiv  Detail & Related papers  (2022-02-21T22:16:50Z)
• Imbalanced Data Learning by Minority Class Augmentation using Capsule Adversarial Networks [31.073558420480964]
  We propose a method to restore the balance in imbalanced images, by coalescing two concurrent methods. In our model, generative and discriminative networks play a novel competitive game. The coalescing of capsule-GAN is effective at recognizing highly overlapping classes with much fewer parameters compared with the convolutional-GAN.
  arXiv  Detail & Related papers  (2020-04-05T12:36:06Z)
• M2m: Imbalanced Classification via Major-to-minor Translation [79.09018382489506]
  In most real-world scenarios, labeled training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion. In this paper, we explore a novel yet simple way to alleviate this issue by augmenting less-frequent classes via translating samples from more-frequent classes. Our experimental results on a variety of class-imbalanced datasets show that the proposed method improves the generalization on minority classes significantly compared to other existing re-sampling or re-weighting methods.
  arXiv  Detail & Related papers  (2020-04-01T13:21:17Z)
• Adversarial Feature Hallucination Networks for Few-Shot Learning [84.31660118264514]
  Adversarial Feature Hallucination Networks (AFHN) is based on conditional Wasserstein Generative Adversarial networks (cWGAN) Two novel regularizers are incorporated into AFHN to encourage discriminability and diversity of the synthesized features.
  arXiv  Detail & Related papers  (2020-03-30T02:43:16Z)

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.