Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning

@article{Munteanu2022SemanticSO,
  title={Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning},
  author={Alexandru Munteanu and Marian Neagul},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.14364}
}
In recent years, the geospatial industry has been developing at a steady pace. This growth implies the addition of satellite constellations that produce a copious supply of satellite imagery and other Remote Sensing data on a daily basis. Sometimes, this information, even if in some cases we are referring to publicly available data, it sits unaccounted for due to the sheer size of it. Processing such large amounts of data with the help of human labour or by using traditional automation methods… 
[PDF] Semantic Reader

References

SHOWING 1-10 OF 36 REFERENCES

EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

A patch-based land use and land cover classification approach using Sentinel-2 satellite images that covers 13 spectral bands and is comprised of ten classes with a total of 27 000 labeled and geo-referenced images is presented.

SpaceNet: A Remote Sensing Dataset and Challenge Series

It is proposed that the frequent revisits of earth imaging satellite constellations may accelerate existing efforts to quickly update foundational maps when combined with advanced machine learning techniques.

Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery

This paper proposes an approach for automatic road extraction based on a fully convolutional neural network of U-net family that consists of ResNet-34 pre-trained on ImageNet and decoder adapted from vanilla U-Net.

Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding

Experimental results obtained in the framework of RS image scene classification problems show that a shallow Convolutional Neural Network architecture trained on the BigEarthNet provides much higher accuracy compared to a state-of-the-art CNN model pre-trained on the ImageNet.

Road Extraction by Deep Residual U-Net

A semantic segmentation neural network, which combines the strengths of residual learning and U-Net, is proposed for road area extraction, which outperforms all the comparing methods and demonstrates its superiority over recently developed state of the arts methods.

Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations

Experimental results reveal that, by designing morphological filtering methods that take into account the complementary nature of spatial and spectral information in a simultaneous manner, it is possible to alleviate the problems related to each of them when taken separately.

MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets

A Comparison of Two Morphological Techniques in the Classification of Urban Land Cover

The study concluded that CMP and MMP can greatly improve the classification of heterogeneous landscapes that typify urban areas by effectively representing the structural landscape information necessary for discriminating related land cover classes.

Classification and feature extraction for remote sensing images from urban areas based on morphological transformations

It is seen that relatively few features are needed to achieve the same classification accuracies as in the original feature space when classification of panchromatic high-resolution data from urban areas using morphological and neural approaches.

Fully convolutional networks for semantic segmentation

The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.