An End-to-end Framework For Low-Resolution Remote Sensing Semantic Segmentation

• URL: http://arxiv.org/abs/2003.07955v1
• Date: Tue, 17 Mar 2020 21:41:22 GMT
• Title: An End-to-end Framework For Low-Resolution Remote Sensing Semantic Segmentation
• Authors: Matheus Barros Pereira and Jefersson Alex dos Santos
• Abstract summary: We propose an end-to-end framework that unites a super-resolution and a semantic segmentation module. It allows the semantic segmentation network to conduct the reconstruction process, modifying the input image with helpful textures. The results show that the framework is capable of achieving a semantic segmentation performance close to native high-resolution data.
• Score: 0.5076419064097732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-resolution images for remote sensing applications are often not affordable or accessible, especially when in need of a wide temporal span of recordings. Given the easy access to low-resolution (LR) images from satellites, many remote sensing works rely on this type of data. The problem is that LR images are not appropriate for semantic segmentation, due to the need for high-quality data for accurate pixel prediction for this task. In this paper, we propose an end-to-end framework that unites a super-resolution and a semantic segmentation module in order to produce accurate thematic maps from LR inputs. It allows the semantic segmentation network to conduct the reconstruction process, modifying the input image with helpful textures. We evaluate the framework with three remote sensing datasets. The results show that the framework is capable of achieving a semantic segmentation performance close to native high-resolution data, while also surpassing the performance of a network trained with LR inputs.

Related papers

• DDU-Net: A Domain Decomposition-based CNN for High-Resolution Image Segmentation on Multiple GPUs [46.873264197900916]
  A domain decomposition-based U-Net architecture is introduced, which partitions input images into non-overlapping patches. A communication network is added to facilitate inter-patch information exchange to enhance the understanding of spatial context. Results show that the approach achieves a $2-3,%$ higher intersection over union (IoU) score compared to the same network without inter-patch communication.
  arXiv  Detail & Related papers  (2024-07-31T01:07:21Z)
• MetaSegNet: Metadata-collaborative Vision-Language Representation Learning for Semantic Segmentation of Remote Sensing Images [7.0622873873577054]
  We propose a novel metadata-collaborative segmentation network (MetaSegNet) for semantic segmentation of remote sensing images. Unlike the common model structure that only uses unimodal visual data, we extract the key characteristic from freely available remote sensing image metadata. We construct an image encoder, a text encoder, and a crossmodal attention fusion subnetwork to extract the image and text feature.
  arXiv  Detail & Related papers  (2023-12-20T03:16:34Z)
• Low-Resolution Self-Attention for Semantic Segmentation [96.81482872022237]
  We introduce the Low-Resolution Self-Attention (LRSA) mechanism to capture global context at a significantly reduced computational cost. Our approach involves computing self-attention in a fixed low-resolution space regardless of the input image's resolution. We demonstrate the effectiveness of our LRSA approach by building the LRFormer, a vision transformer with an encoder-decoder structure.
  arXiv  Detail & Related papers  (2023-10-08T06:10:09Z)
• 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)
• RRSIS: Referring Remote Sensing Image Segmentation [25.538406069768662]
  Localizing desired objects from remote sensing images is of great use in practical applications. Referring image segmentation, which aims at segmenting out the objects to which a given expression refers, has been extensively studied in natural images. We introduce referring remote sensing image segmentation (RRSIS) to fill in this gap and make some insightful explorations.
  arXiv  Detail & Related papers  (2023-06-14T16:40:19Z)
• Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance [20.86089285980103]
  High resolution (HR) 3D images are widely used nowadays, such as medical images like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT)
  arXiv  Detail & Related papers  (2022-10-26T11:46:08Z)
• Few-shot Segmentation with Optimal Transport Matching and Message Flow [50.9853556696858]
  It is essential for few-shot semantic segmentation to fully utilize the support information. We propose a Correspondence Matching Network (CMNet) with an Optimal Transport Matching module. Experiments on PASCAL VOC 2012, MS COCO, and FSS-1000 datasets show that our network achieves new state-of-the-art few-shot segmentation performance.
  arXiv  Detail & Related papers  (2021-08-19T06:26:11Z)
• Deep Learning for Robust Motion Segmentation with Non-Static Cameras [0.0]
  This paper proposes a new end-to-end DCNN based approach for motion segmentation, especially for captured with such non-static cameras, called MOSNET. While other approaches focus on spatial or temporal context, the proposed approach uses 3D convolutions as a key technology to factor in temporal features in video frames. The network is able to perform well on scenes captured with non-static cameras where the image content changes significantly during the scene.
  arXiv  Detail & Related papers  (2021-02-22T11:58:41Z)
• A Holistically-Guided Decoder for Deep Representation Learning with Applications to Semantic Segmentation and Object Detection [74.88284082187462]
  One common strategy is to adopt dilated convolutions in the backbone networks to extract high-resolution feature maps. We propose one novel holistically-guided decoder which is introduced to obtain the high-resolution semantic-rich feature maps.
  arXiv  Detail & Related papers  (2020-12-18T10:51:49Z)
• CRNet: Cross-Reference Networks for Few-Shot Segmentation [59.85183776573642]
  Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images. With a cross-reference mechanism, our network can better find the co-occurrent objects in the two images. Experiments on the PASCAL VOC 2012 dataset show that our network achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-03-24T04:55:43Z)

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.