Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation

• URL: http://arxiv.org/abs/2402.16164v3
• Date: Fri, 7 Jun 2024 21:52:08 GMT
• Title: Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation
• Authors: Chenying Liu, Conrad M Albrecht, Yi Wang, Xiao Xiang Zhu,
• Abstract summary: Self-supervision provides remote sensing a tool to reduce the amount of exact, human-crafted geospatial annotations. In this work, we propose to exploit noisy semantic segmentation maps for model pretraining. The results from two datasets indicate the effectiveness of task-specific supervised pretraining with noisy labels.
• Score: 18.598405597933752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compared to supervised deep learning, self-supervision provides remote sensing a tool to reduce the amount of exact, human-crafted geospatial annotations. While image-level information for unsupervised pretraining efficiently works for various classification downstream tasks, the performance on pixel-level semantic segmentation lags behind in terms of model accuracy. On the contrary, many easily available label sources (e.g., automatic labeling tools and land cover land use products) exist, which can provide a large amount of noisy labels for segmentation model training. In this work, we propose to exploit noisy semantic segmentation maps for model pretraining. Our experiments provide insights on robustness per network layer. The transfer learning settings test the cases when the pretrained encoders are fine-tuned for different label classes and decoders. The results from two datasets indicate the effectiveness of task-specific supervised pretraining with noisy labels. Our findings pave new avenues to improved model accuracy and novel pretraining strategies for efficient remote sensing image segmentation.

Related papers

• Terrain-Informed Self-Supervised Learning: Enhancing Building Footprint Extraction from LiDAR Data with Limited Annotations [1.3243401820948064]
  Building footprint maps offer promise of precise footprint extraction without extensive post-processing. Deep learning methods face challenges in generalization and label efficiency. We propose terrain-aware self-supervised learning tailored to remote sensing.
  arXiv  Detail & Related papers  (2023-11-02T12:34:23Z)
• Impact of Label Types on Training SWIN Models with Overhead Imagery [0.0]
  This work examined the impact of training shifted window transformers using bounding boxes and segmentation labels. We found that the models trained on only target pixels do not show performance improvement for classification tasks. For object detection, we found that models trained with either label type showed equivalent performance across testing.
  arXiv  Detail & Related papers  (2023-10-11T15:14:54Z)
• Learning Semantic Segmentation with Query Points Supervision on Aerial Images [57.09251327650334]
  We present a weakly supervised learning algorithm to train semantic segmentation algorithms. Our proposed approach performs accurate semantic segmentation and improves efficiency by significantly reducing the cost and time required for manual annotation.
  arXiv  Detail & Related papers  (2023-09-11T14:32:04Z)
• Location-Aware Self-Supervised Transformers [74.76585889813207]
  We propose to pretrain networks for semantic segmentation by predicting the relative location of image parts. We control the difficulty of the task by masking a subset of the reference patch features visible to those of the query. Our experiments show that this location-aware pretraining leads to representations that transfer competitively to several challenging semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2022-12-05T16:24:29Z)
• CaSP: Class-agnostic Semi-Supervised Pretraining for Detection and Segmentation [60.28924281991539]
  We propose a novel Class-agnostic Semi-supervised Pretraining (CaSP) framework to achieve a more favorable task-specificity balance. Using 3.6M unlabeled data, we achieve a remarkable performance gain of 4.7% over ImageNet-pretrained baseline on object detection.
  arXiv  Detail & Related papers  (2021-12-09T14:54:59Z)
• Evaluating Self and Semi-Supervised Methods for Remote Sensing Segmentation Tasks [4.7590051176368915]
  We evaluate recent self and semi-supervised ML techniques that leverage unlabeled data for improving downstream task performance. These methods are especially valuable for remote sensing tasks since there is easy access to unlabeled imagery and getting ground truth labels can often be expensive.
  arXiv  Detail & Related papers  (2021-11-19T07:41:14Z)
• Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite Images [1.0152838128195467]
  Semi-supervised techniques generate pseudo-labels from a small set of labeled examples. We propose to use an active learning-based sampling strategy to select a highly representative set of labeled training data. We report a 27% improvement in mIoU with as little as 2% labeled data using active learning sampling strategies.
  arXiv  Detail & Related papers  (2021-10-15T00:29:31Z)
• Semi-weakly Supervised Contrastive Representation Learning for Retinal Fundus Images [0.2538209532048867]
  We propose a semi-weakly supervised contrastive learning framework for representation learning using semi-weakly annotated images. We empirically validate the transfer learning performance of SWCL on seven public retinal fundus datasets.
  arXiv  Detail & Related papers  (2021-08-04T15:50:09Z)
• Noisy Labels Can Induce Good Representations [53.47668632785373]
  We study how architecture affects learning with noisy labels. We show that training with noisy labels can induce useful hidden representations, even when the model generalizes poorly. This finding leads to a simple method to improve models trained on noisy labels.
  arXiv  Detail & Related papers  (2020-12-23T18:58:05Z)
• Attention-Aware Noisy Label Learning for Image Classification [97.26664962498887]
  Deep convolutional neural networks (CNNs) learned on large-scale labeled samples have achieved remarkable progress in computer vision. The cheapest way to obtain a large body of labeled visual data is to crawl from websites with user-supplied labels, such as Flickr. This paper proposes the attention-aware noisy label learning approach to improve the discriminative capability of the network trained on datasets with potential label noise.
  arXiv  Detail & Related papers  (2020-09-30T15:45:36Z)
• Improving Semantic Segmentation via Self-Training [75.07114899941095]
  We show that we can obtain state-of-the-art results using a semi-supervised approach, specifically a self-training paradigm. We first train a teacher model on labeled data, and then generate pseudo labels on a large set of unlabeled data. Our robust training framework can digest human-annotated and pseudo labels jointly and achieve top performances on Cityscapes, CamVid and KITTI datasets.
  arXiv  Detail & Related papers  (2020-04-30T17:09:17Z)

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.