Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer

• URL: http://arxiv.org/abs/2105.07322v1
• Date: Sun, 16 May 2021 00:57:43 GMT
• Title: Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer
• Authors: Arthita Ghosh, Max Ehrlich, Larry Davis, Rama Chellappa
• Abstract summary: We propose an unsupervised domain adaptation based approach using adversarial learning. We aim to harvest information from smaller quantities of high resolution data (source domain) and utilize the same to super-resolve low resolution imagery (target domain)
• Score: 78.24493844353258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Urban material recognition in remote sensing imagery is a highly relevant, yet extremely challenging problem due to the difficulty of obtaining human annotations, especially on low resolution satellite images. To this end, we propose an unsupervised domain adaptation based approach using adversarial learning. We aim to harvest information from smaller quantities of high resolution data (source domain) and utilize the same to super-resolve low resolution imagery (target domain). This can potentially aid in semantic as well as material label transfer from a richly annotated source to a target domain.

Related papers

• Can Location Embeddings Enhance Super-Resolution of Satellite Imagery? [2.3020018305241337]
  Publicly available satellite imagery, such as Sentinel- 2, often lacks the spatial resolution required for accurate analysis of remote sensing tasks. We propose a novel super-resolution framework that enhances generalization by incorporating geographic context through location embeddings. We demonstrate the effectiveness of our method on the building segmentation task, showing significant improvements over state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-01-27T08:16:54Z)
• Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data [66.49494950674402]
  We leverage emerging text-to-image generative models in creating large-scale synthetic supervision for the task of damage assessment from aerial images. We build an efficient and easily scalable pipeline to generate thousands of post-disaster images from low-resource domains. We validate the strength of our proposed framework under cross-geography domain transfer setting from xBD and SKAI images in both single-source and multi-source settings.
  arXiv  Detail & Related papers  (2024-05-22T16:07:05Z)
• Super-Resolving Face Image by Facial Parsing Information [52.1267613768555]
  Face super-resolution is a technology that transforms a low-resolution face image into the corresponding high-resolution one. We build a novel parsing map guided face super-resolution network which extracts the face prior from low-resolution face image. High-resolution features contain more precise spatial information while low-resolution features provide strong contextual information.
  arXiv  Detail & Related papers  (2023-04-06T08:19:03Z)
• Handling Image and Label Resolution Mismatch in Remote Sensing [10.009103959118931]
  We show how to handle resolution mismatch between overhead imagery and ground-truth label sources. We present a method that is supervised using low-resolution labels, but takes advantage of an exemplar set of high-resolution labels. Our method incorporates region aggregation, adversarial learning, and self-supervised pretraining to generate fine-supervised predictions.
  arXiv  Detail & Related papers  (2022-11-28T21:56:07Z)
• Progressive Domain Adaptation with Contrastive Learning for Object Detection in the Satellite Imagery [0.0]
  State-of-the-art object detection methods largely fail to identify small and dense objects. We propose a small object detection pipeline that improves the feature extraction process. We show we can alleviate the degradation of object identification in previously unseen datasets.
  arXiv  Detail & Related papers  (2022-09-06T15:16:35Z)
• Unsupervised Remote Sensing Super-Resolution via Migration Image Prior [12.728430583409997]
  We propose a new unsupervised learning framework, called "MIP", which achieves super-resolution tasks without low/high resolution image pairs. In this paper, we show that MIP achieves significant improvements over state-of-the-art methods both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2021-05-08T03:29:35Z)
• Semantic Layout Manipulation with High-Resolution Sparse Attention [106.59650698907953]
  We tackle the problem of semantic image layout manipulation, which aims to manipulate an input image by editing its semantic label map. A core problem of this task is how to transfer visual details from the input images to the new semantic layout while making the resulting image visually realistic. We propose a high-resolution sparse attention module that effectively transfers visual details to new layouts at a resolution up to 512x512.
  arXiv  Detail & Related papers  (2020-12-14T06:50:43Z)
• Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images [54.44842669325082]
  This work proposes to bridge the gap between image quality and the price by improving the image quality via super-resolution (SR) We design an SR framework that analyzes the regional information content on each patch of the low-resolution image. We show substantial decrease in inference time while achieving similar performance to that of existing deep SR methods.
  arXiv  Detail & Related papers  (2020-08-03T13:55:39Z)
• Unsupervised Transfer Learning with Self-Supervised Remedy [60.315835711438936]
  Generalising deep networks to novel domains without manual labels is challenging to deep learning. Pre-learned knowledge does not transfer well without making strong assumptions about the learned and the novel domains. In this work, we aim to learn a discriminative latent space of the unlabelled target data in a novel domain by knowledge transfer from labelled related domains.
  arXiv  Detail & Related papers  (2020-06-08T16:42:17Z)
• Unsupervised Enhancement of Real-World Depth Images Using Tri-Cycle GAN [8.477619837043214]
  We aim to enhance highly degraded, real-world depth images acquired by a low-cost sensor. In the absence of clean ground-truth, we approach the task as an unsupervised domain-translation between the low-quality sensor domain and a high-quality sensor domain. We employ the highly-successful Cycle-GAN to this task, but find it to perform poorly in this case.
  arXiv  Detail & Related papers  (2020-01-11T18:19:09Z)

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.