Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection

• URL: http://arxiv.org/abs/2006.05923v1
• Date: Wed, 10 Jun 2020 16:16:01 GMT
• Title: Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection
• Authors: Gonzalo Mateo-Garc\'ia, Valero Laparra, Dan L\'opez-Puigdollers, Luis G\'omez-Chova
• Abstract summary: The number of Earth observation satellites carrying optical sensors with similar characteristics is constantly growing. Differences in retrieved radiances lead to significant drops in accuracy, which hampers knowledge and information sharing across sensors. We propose a domain adaptation to reduce the statistical differences between images of two satellite sensors in order to boost the performance of transfer learning models.
• Score: 1.5828697880068703
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The number of Earth observation satellites carrying optical sensors with similar characteristics is constantly growing. Despite their similarities and the potential synergies among them, derived satellite products are often developed for each sensor independently. Differences in retrieved radiances lead to significant drops in accuracy, which hampers knowledge and information sharing across sensors. This is particularly harmful for machine learning algorithms, since gathering new ground truth data to train models for each sensor is costly and requires experienced manpower. In this work, we propose a domain adaptation transformation to reduce the statistical differences between images of two satellite sensors in order to boost the performance of transfer learning models. The proposed methodology is based on the Cycle Consistent Generative Adversarial Domain Adaptation (CyCADA) framework that trains the transformation model in an unpaired manner. In particular, Landsat-8 and Proba-V satellites, which present different but compatible spatio-spectral characteristics, are used to illustrate the method. The obtained transformation significantly reduces differences between the image datasets while preserving the spatial and spectral information of adapted images, which is hence useful for any general purpose cross-sensor application. In addition, the training of the proposed adversarial domain adaptation model can be modified to improve the performance in a specific remote sensing application, such as cloud detection, by including a dedicated term in the cost function. Results show that, when the proposed transformation is applied, cloud detection models trained in Landsat-8 data increase cloud detection accuracy in Proba-V.

Related papers

• MSSIDD: A Benchmark for Multi-Sensor Denoising [55.41612200877861]
  We introduce a new benchmark, the Multi-Sensor SIDD dataset, which is the first raw-domain dataset designed to evaluate the sensor transferability of denoising models. We propose a sensor consistency training framework that enables denoising models to learn the sensor-invariant features.
  arXiv  Detail & Related papers  (2024-11-18T13:32:59Z)
• Adaptive Domain Learning for Cross-domain Image Denoising [57.4030317607274]
  We present a novel adaptive domain learning scheme for cross-domain image denoising. We use existing data from different sensors (source domain) plus a small amount of data from the new sensor (target domain) The ADL training scheme automatically removes the data in the source domain that are harmful to fine-tuning a model for the target domain. Also, we introduce a modulation module to adopt sensor-specific information (sensor type and ISO) to understand input data for image denoising.
  arXiv  Detail & Related papers  (2024-11-03T08:08:26Z)
• BenchCloudVision: A Benchmark Analysis of Deep Learning Approaches for Cloud Detection and Segmentation in Remote Sensing Imagery [0.0]
  This paper examines seven cutting-edge semantic segmentation and detection algorithms applied to clouds identification. To increase the model's adaptability, critical elements including the type of imagery and the amount of spectral bands used during training are analyzed. Research tries to produce machine learning algorithms that can perform cloud segmentation using only a few spectral bands.
  arXiv  Detail & Related papers  (2024-02-21T16:32:43Z)
• Augmenting Deep Learning Adaptation for Wearable Sensor Data through Combined Temporal-Frequency Image Encoding [4.458210211781739]
  We present a novel modified-recurrent plot-based image representation that seamlessly integrates both temporal and frequency domain information. We evaluate the proposed method using accelerometer-based activity recognition data and a pretrained ResNet model, and demonstrate its superior performance compared to existing approaches.
  arXiv  Detail & Related papers  (2023-07-03T09:29:27Z)
• Energy-Based Models for Cross-Modal Localization using Convolutional Transformers [52.27061799824835]
  We present a novel framework for localizing a ground vehicle mounted with a range sensor against satellite imagery in the absence of GPS. We propose a method using convolutional transformers that performs accurate metric-level localization in a cross-modal manner. We train our model end-to-end and demonstrate our approach achieving higher accuracy than the state-of-the-art on KITTI, Pandaset, and a custom dataset.
  arXiv  Detail & Related papers  (2023-06-06T21:27:08Z)
• Semantically-consistent Landsat 8 image to Sentinel-2 image translation for alpine areas [0.0]
  In this paper, an experiment of domain adaptation through style-transferring is conducted using the HRSemI2I model to narrow the sensor discrepancy between Landsat 8 and Sentinel-2. The HRSemI2I model, adjusted to work with 6-band imagery, shows significant intersection-over-union performance improvement for both mean and per class metrics. A second contribution is providing different schemes of generalization between two label schemes - NALCMS 2015 and CORINE.
  arXiv  Detail & Related papers  (2022-12-22T22:07:28Z)
• dual unet:a novel siamese network for change detection with cascade differential fusion [4.651756476458979]
  We propose a novel Siamese neural network for change detection task, namely Dual-UNet. In contrast to previous individually encoded the bitemporal images, we design an encoder differential-attention module to focus on the spatial difference relationships of pixels. Experiments demonstrate that the proposed approach consistently outperforms the most advanced methods on popular seasonal change detection datasets.
  arXiv  Detail & Related papers  (2022-08-12T14:24:09Z)
• Real-time detection of uncalibrated sensors using Neural Networks [62.997667081978825]
  An online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. The solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively.
  arXiv  Detail & Related papers  (2021-02-02T15:44:39Z)
• Semantic Change Detection with Asymmetric Siamese Networks [71.28665116793138]
  Given two aerial images, semantic change detection aims to locate the land-cover variations and identify their change types with pixel-wise boundaries. This problem is vital in many earth vision related tasks, such as precise urban planning and natural resource management. We present an asymmetric siamese network (ASN) to locate and identify semantic changes through feature pairs obtained from modules of widely different structures.
  arXiv  Detail & Related papers  (2020-10-12T13:26:30Z)
• Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition [131.6328804788164]
  We propose a framework, named Semantics-aware Adaptive Knowledge Distillation Networks (SAKDN), to enhance action recognition in vision-sensor modality (videos) The SAKDN uses multiple wearable-sensors as teacher modalities and uses RGB videos as student modality.
  arXiv  Detail & Related papers  (2020-09-01T03:38:31Z)
• Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching [12.93459392278491]
  We present an end-to-end deep phase correlation network (DPCN) to match heterogeneous sensor measurements. The primary component is a differentiable correlation-based estimator that back-propagates the pose error to learnable feature extractors. With the interpretable modeling, the network is light-weighted and promising for better generalization.
  arXiv  Detail & Related papers  (2020-08-21T13:42:25Z)

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.