Deep-Learning-Based Single-Image Height Reconstruction from Very-High-Resolution SAR Intensity Data

• URL: http://arxiv.org/abs/2111.02061v1
• Date: Wed, 3 Nov 2021 08:20:03 GMT
• Title: Deep-Learning-Based Single-Image Height Reconstruction from Very-High-Resolution SAR Intensity Data
• Authors: Michael Recla, Michael Schmitt
• Abstract summary: We present the first-ever demonstration of deep learning-based single image height prediction for the other important sensor modality in remote sensing: synthetic aperture radar (SAR) data. Besides the adaptation of a convolutional neural network (CNN) architecture for SAR intensity images, we present a workflow for the generation of training data. Since we put a particular emphasis on transferability, we are able to confirm that deep learning-based single-image height estimation is not only possible, but also transfers quite well to unseen data.
• Score: 1.7894377200944511
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Originally developed in fields such as robotics and autonomous driving with image-based navigation in mind, deep learning-based single-image depth estimation (SIDE) has found great interest in the wider image analysis community. Remote sensing is no exception, as the possibility to estimate height maps from single aerial or satellite imagery bears great potential in the context of topographic reconstruction. A few pioneering investigations have demonstrated the general feasibility of single image height prediction from optical remote sensing images and motivate further studies in that direction. With this paper, we present the first-ever demonstration of deep learning-based single image height prediction for the other important sensor modality in remote sensing: synthetic aperture radar (SAR) data. Besides the adaptation of a convolutional neural network (CNN) architecture for SAR intensity images, we present a workflow for the generation of training data, and extensive experimental results for different SAR imaging modes and test sites. Since we put a particular emphasis on transferability, we are able to confirm that deep learning-based single-image height estimation is not only possible, but also transfers quite well to unseen data, even if acquired by different imaging modes and imaging parameters.

Related papers

• Sparse Multi-baseline SAR Cross-modal 3D Reconstruction of Vehicle Targets [5.6680936716261705]
  We propose a Cross-Modal Reconstruction Network (CMR-Net), which integrates differentiable render and cross-modal supervision with optical images. CMR-Net, trained solely on simulated data, demonstrates high-resolution reconstruction capabilities on both publicly available simulation datasets and real measured datasets.
  arXiv  Detail & Related papers  (2024-06-06T15:18:59Z)
• Semantic Guided Large Scale Factor Remote Sensing Image Super-resolution with Generative Diffusion Prior [13.148815217684277]
  Large scale factor super-resolution (SR) algorithms are vital for maximizing the utilization of low-resolution (LR) satellite data captured from orbit. Existing methods confront challenges in recovering SR images with clear textures and correct ground objects. We introduce a novel framework, the Semantic Guided Diffusion Model (SGDM), designed for large scale factor remote sensing image super-resolution.
  arXiv  Detail & Related papers  (2024-05-11T16:06:16Z)
• RS-Mamba for Large Remote Sensing Image Dense Prediction [58.12667617617306]
  We propose the Remote Sensing Mamba (RSM) for dense prediction tasks in large VHR remote sensing images. RSM is specifically designed to capture the global context of remote sensing images with linear complexity. Our model achieves better efficiency and accuracy than transformer-based models on large remote sensing images.
  arXiv  Detail & Related papers  (2024-04-03T12:06:01Z)
• Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery [78.43828998065071]
  Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks. Such pre-training techniques have also been explored recently in the remote sensing domain due to the availability of large amount of unlabelled data. In this paper, we re-visit transformers pre-training and leverage multi-scale information that is effectively utilized with multiple modalities.
  arXiv  Detail & Related papers  (2024-03-08T16:18:04Z)
• Diffusion Models for Interferometric Satellite Aperture Radar [73.01013149014865]
  Probabilistic Diffusion Models (PDMs) have recently emerged as a very promising class of generative models. Here, we leverage PDMs to generate several radar-based satellite image datasets. We show that PDMs succeed in generating images with complex and realistic structures, but that sampling time remains an issue.
  arXiv  Detail & Related papers  (2023-08-31T16:26:17Z)
• An Empirical Study of Remote Sensing Pretraining [117.90699699469639]
  We conduct an empirical study of remote sensing pretraining (RSP) on aerial images. RSP can help deliver distinctive performances in scene recognition tasks. RSP mitigates the data discrepancies of traditional ImageNet pretraining on RS images, but it may still suffer from task discrepancies.
  arXiv  Detail & Related papers  (2022-04-06T13:38:11Z)
• Learning Efficient Representations for Enhanced Object Detection on Large-scene SAR Images [16.602738933183865]
  It is a challenging problem to detect and recognize targets on complex large-scene Synthetic Aperture Radar (SAR) images. Recently developed deep learning algorithms can automatically learn the intrinsic features of SAR images. We propose an efficient and robust deep learning based target detection method.
  arXiv  Detail & Related papers  (2022-01-22T03:25:24Z)
• Homography augumented momentum constrastive learning for SAR image retrieval [3.9743795764085545]
  We propose a deep learning-based image retrieval approach using homography transformation augmented contrastive learning. We also propose a training method for the DNNs induced by contrastive learning that does not require any labeling procedure.
  arXiv  Detail & Related papers  (2021-09-21T17:27:07Z)
• Multi-Modal Depth Estimation Using Convolutional Neural Networks [0.8701566919381223]
  This paper addresses the problem of dense depth predictions from sparse distance sensor data and a single camera image on challenging weather conditions. It explores the significance of different sensor modalities such as camera, Radar, and Lidar for estimating depth by applying Deep Learning approaches.
  arXiv  Detail & Related papers  (2020-12-17T15:31:49Z)
• Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline [100.5353614588565]
  We propose to incorporate the domain knowledge of the LDR image formation pipeline into our model. We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization. We demonstrate that the proposed method performs favorably against state-of-the-art single-image HDR reconstruction algorithms.
  arXiv  Detail & Related papers  (2020-04-02T17:59:04Z)
• Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images [59.906948203578544]
  We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object. We first estimate per-view depth maps using a deep multi-view stereo network. These depth maps are used to coarsely align the different views. We propose a novel multi-view reflectance estimation network architecture.
  arXiv  Detail & Related papers  (2020-03-27T21:28:54Z)

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.