Texture based Prototypical Network for Few-Shot Semantic Segmentation of Forest Cover: Generalizing for Different Geographical Regions

• URL: http://arxiv.org/abs/2203.15687v1
• Date: Tue, 29 Mar 2022 15:48:17 GMT
• Title: Texture based Prototypical Network for Few-Shot Semantic Segmentation of Forest Cover: Generalizing for Different Geographical Regions
• Authors: Gokul P and Ujjwal Verma
• Abstract summary: The proposed approach is trained for identifying tropical forests of South Asia and adapted to determine the temperate forest of Central Europe. An IoU of 0.62 for forest class (1-way 1-shot) was obtained using the proposed method, which is significantly higher (0.46 for PANet) than the existing few-shot semantic segmentation approach.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Forest plays a vital role in reducing greenhouse gas emissions and mitigating climate change besides maintaining the world's biodiversity. The existing satellite-based forest monitoring system utilizes supervised learning approaches that are limited to a particular region and depend on manually annotated data to identify forest. This work envisages forest identification as a few-shot semantic segmentation task to achieve generalization across different geographical regions. The proposed few-shot segmentation approach incorporates a texture attention module in the prototypical network to highlight the texture features of the forest. Indeed, the forest exhibits a characteristic texture different from other classes, such as road, water, etc. In this work, the proposed approach is trained for identifying tropical forests of South Asia and adapted to determine the temperate forest of Central Europe with the help of a few (one image for 1-shot) manually annotated support images of the temperate forest. An IoU of 0.62 for forest class (1-way 1-shot) was obtained using the proposed method, which is significantly higher (0.46 for PANet) than the existing few-shot semantic segmentation approach. This result demonstrates that the proposed approach can generalize across geographical regions for forest identification, creating an opportunity to develop a global forest cover identification tool.

Related papers

• Deep Learning tools to support deforestation monitoring in the Ivory Coast using SAR and Optical satellite imagery [0.0]
  Satellites can be employed to recognize the disappearance of the forest to prevent it from expand its area of interest. Forest-Non-Forest map (FNF) has been used as ground truth for models based on Sentinel images input. Models classifying forest and non-forests pixels over the area using open datasets to predict where deforestation could have occurred.
  arXiv  Detail & Related papers  (2024-09-16T14:26:41Z)
• Mapping High-level Semantic Regions in Indoor Environments without Object Recognition [50.624970503498226]
  The present work proposes a method for semantic region mapping via embodied navigation in indoor environments. To enable region identification, the method uses a vision-to-language model to provide scene information for mapping. By projecting egocentric scene understanding into the global frame, the proposed method generates a semantic map as a distribution over possible region labels at each location.
  arXiv  Detail & Related papers  (2024-03-11T18:09:50Z)
• ForensicsForest Family: A Series of Multi-scale Hierarchical Cascade Forests for Detecting GAN-generated Faces [53.739014757621376]
  We describe a simple and effective forest-based method set called em ForensicsForest Family to detect GAN-generate faces. ForenscisForest is a newly proposed Multi-scale Hierarchical Cascade Forest. Hybrid ForensicsForest integrates the CNN layers into models. Divide-and-Conquer ForensicsForest can construct a forest model using only a portion of training samplings.
  arXiv  Detail & Related papers  (2023-08-02T06:41:19Z)
• Mapping historical forest biomass for stock-change assessments at parcel to landscape scales [0.0]
  Map products can help identify where, when, and how forest carbon stocks are changing as a result of both anthropogenic and natural drivers alike. These products can thus serve as inputs to a wide range of applications including stock-change assessments, monitoring reporting, and verification frameworks.
  arXiv  Detail & Related papers  (2023-04-05T17:55:00Z)
• Neuroevolution-based Classifiers for Deforestation Detection in Tropical Forests [62.997667081978825]
  Millions of hectares of tropical forests are lost every year due to deforestation or degradation. Monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals. This paper proposes the use of pattern classifiers based on neuroevolution technique (NEAT) in tropical forest deforestation detection tasks.
  arXiv  Detail & Related papers  (2022-08-23T16:04:12Z)
• MultiEarth 2022 Deforestation Challenge -- ForestGump [0.0]
  We present an accurate deforestation estimation method with conventional UNet and comprehensive data processing. The diverse channels of Sentinel-1, Sentinel-2 and Landsat 8 are carefully selected and utilized to train deep neural networks. With the proposed method, deforestation status for novel queries are successfully estimated with high accuracy.
  arXiv  Detail & Related papers  (2022-06-22T04:10:07Z)
• Multiple-environment Self-adaptive Network for Aerial-view Geo-localization [85.52750931345287]
  Aerial-view geo-localization tends to determine an unknown position through matching the drone-view image with the geo-tagged satellite-view image. We propose a Multiple-environment Self-adaptive Network (MuSe-Net) to adjust the domain shift caused by environmental changing. In particular, MuSe-Net employs a two-branch neural network containing one multiple-environment style extraction network and one self-adaptive feature extraction network.
  arXiv  Detail & Related papers  (2022-04-18T16:04:29Z)
• Dense Siamese Network [86.23741104851383]
  We present Dense Siamese Network (DenseSiam), a simple unsupervised learning framework for dense prediction tasks. It learns visual representations by maximizing the similarity between two views of one image with two types of consistency, i.e., pixel consistency and region consistency. It surpasses state-of-the-art segmentation methods by 2.1 mIoU with 28% training costs.
  arXiv  Detail & Related papers  (2022-03-21T15:55:23Z)
• Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Bayesian Deep Learning [74.94436509364554]
  We propose a Bayesian deep learning approach to densely estimate forest structure variables at country-scale with 10-meter resolution. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 synthetic aperture radar images into maps of five different forest structure variables. We train and test our model on reference data from 41 airborne laser scanning missions across Norway.
  arXiv  Detail & Related papers  (2021-11-25T16:21:28Z)
• ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery [10.924137779582814]
  We develop a deep learning model called ForestNet to classify the drivers of primary forest loss in Indonesia. Using satellite imagery, ForestNet identifies the direct drivers of deforestation in forest loss patches of any size.
  arXiv  Detail & Related papers  (2020-11-11T00:28:40Z)

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.