Towards Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping

• URL: http://arxiv.org/abs/2203.00049v1
• Date: Mon, 28 Feb 2022 19:32:52 GMT
• Title: Towards Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping
• Authors: J{\o}rgen A. Agersborg, Luigi T. Luppino, Stian Normann Anfinsen and Jane Uhd Jepsen
• Abstract summary: We use Landsat-5 Thematic Mapper images from before the event are used, with RADARSAT-2 providing the post-event images. We obtain the difference images for both multispectral optical and synthetic aperture radar (SAR) by using a recently developed deep learning method for translating between the two domains.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we develop a method for mapping forest mortality in the forest-tundra ecotone using satellite data from heterogeneous sensors. We use medium resolution imagery in order to provide the complex pattern of forest mortality in this sparsely forested area, which has been induced by an outbreak of geometrid moths. Specifically, Landsat-5 Thematic Mapper images from before the event are used, with RADARSAT-2 providing the post-event images. We obtain the difference images for both multispectral optical and synthetic aperture radar (SAR) by using a recently developed deep learning method for translating between the two domains. These differences are stacked with the original pre- and post-event images in order to let our algorithm also learn how the areas appear before and after the change event. By doing this, and focusing on learning only the changes of interest with one-class classification (OCC), we obtain good results with very little training data.

Related papers

• Using Texture to Classify Forests Separately from Vegetation [0.0]
  This paper presents an initial proposal for a static, algorithmic process to identify forest regions in satellite image data. With strong initial results, this paper also identifies the next steps to improve the accuracy of the classification and verification processes.
  arXiv  Detail & Related papers  (2024-05-01T00:48:55Z)
• SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation [69.42764583465508]
  We explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks. To the best of our knowledge, we are the first to generate both images and corresponding masks for satellite segmentation.
  arXiv  Detail & Related papers  (2024-03-25T10:30:22Z)
• BD-MSA: Body decouple VHR Remote Sensing Image Change Detection method guided by multi-scale feature information aggregation [4.659935767219465]
  The purpose of remote sensing image change detection (RSCD) is to detect differences between bi-temporal images taken at the same place. Deep learning has been extensively used to RSCD tasks, yielding significant results in terms of result recognition.
  arXiv  Detail & Related papers  (2024-01-09T02:53:06Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images [60.34381768479834]
  Recent advancements in diffusion models have enabled the generation of realistic deepfakes from textual prompts in natural language. We pioneer a systematic study on deepfake detection generated by state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2023-04-02T10:25:09Z)
• 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)
• Multi-Label Classification on Remote-Sensing Images [0.0]
  This report aims to label the satellite image chips of the Amazon rainforest with atmospheric and various classes of land cover or land use through different machine learning and superior deep learning models. Our best score was achieved so far with the F2 metric is 0.927.
  arXiv  Detail & Related papers  (2022-01-06T08:42:32Z)
• A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection [93.38607559281601]
  We devise a hierarchical generative model that captures the multi-scale patch distribution of each training image. The anomaly score is obtained by aggregating the patch-based votes of the correct transformation across scales and image regions.
  arXiv  Detail & Related papers  (2021-04-29T17:49:48Z)
• Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning [52.07698484363237]
  We propose a Non-Homogeneous Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph reasoning. Our method achieves superior performance over many state-of-the-art algorithms for both the single image dehazing and hazy image understanding tasks.
  arXiv  Detail & Related papers  (2021-04-05T13:04:44Z)
• Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning [23.203687716051697]
  A robust unsupervised approach is proposed for small area change detection from synthetic aperture radar (SAR) images. A multi-scale superpixel reconstruction method is developed to generate a difference image (DI) A two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes.
  arXiv  Detail & Related papers  (2020-11-22T12:50:08Z)
• 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)

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.