Satellite Image Semantic Segmentation

2021-10-12 · Eric Guérin, Killian Oechslin, Christian Wolf, Benoît Martinez

In this paper, we propose a method for the automatic semantic segmentation of satellite images into six classes (sparse forest, dense forest, moor, herbaceous formation, building, and road). We rely on Swin Transformer architecture and build the dataset from IGN open data. We report quantitative and qualitative segmentation results on this dataset and discuss strengths and limitations. The dataset and the trained model are made publicly available.

📄 PDF Abstract BibTeX arXiv:2110.05812

Code (1)

koechslin/swin-transformer-semantic-segmentation 공식 구현 pytorch

Tasks

2D Semantic Segmentation Segmentation Semantic Segmentation

Methods 이 논문이 사용한 방법론

Multi-Head Attention 설명 없음

Attention 설명 없음

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Residual Connection 설명 없음

Stochastic Depth Stochastic Depth aims to shrink the depth of a network during training, while keeping it unchanged during testing. This is achieved by randomly dropping entire…

Adam 설명 없음

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…

Evaluating the Efficacy of Cut-and-Paste Data Augmentation in Semantic Segmentation for Satellite Imagery

2024-04-08 · Ionut M. Motoi, Leonardo Saraceni, Daniele Nardi, Thomas A. Ciarfuglia

Satellite imagery is crucial for tasks like environmental monitoring and urban planning. Typically, it relies on semantic segmentation or Land Use Land Cover (LULC) classification to categorize each pixel. Despite the ad…

Data Augmentation Segmentation Semantic Segmentation

Active Label Refinement for Semantic Segmentation of Satellite Images

2023-09-12 · Tuan Pham Minh, Jayan Wijesingha, Daniel Kottke, Marek Herde 외

Remote sensing through semantic segmentation of satellite images contributes to the understanding and utilisation of the earth's surface. For this purpose, semantic segmentation networks are typically trained on large se…

Active Learning Segmentation Semantic Segmentation

DeepTriNet: A Tri-Level Attention Based DeepLabv3+ Architecture for Semantic Segmentation of Satellite Images

2023-09-05 · Tareque Bashar Ovi, Shakil Mosharrof, Nomaiya Bashree, Md Shofiqul Islam 외

The segmentation of satellite images is crucial in remote sensing applications. Existing methods face challenges in recognizing small-scale objects in satellite images for semantic segmentation primarily due to ignoring …

Change Detection Image Segmentation Management Segmentation+1

A Comparison and Strategy of Semantic Segmentation on Remote Sensing Images

2019-05-24 · Junxing Hu, Ling Li, Yijun Lin, Fengge Wu 외

In recent years, with the development of aerospace technology, we use more and more images captured by satellites to obtain information. But a large number of useless raw images, limited data storage resource and poor tr…

Deep Learning Segmentation Semantic Segmentation

Semantic Segmentation of Medium-Resolution Satellite Imagery using Conditional Generative Adversarial Networks

2020-12-05 · Aditya Kulkarni, Tharun Mohandoss, Daniel Northrup, Ernest Mwebaze 외

Semantic segmentation of satellite imagery is a common approach to identify patterns and detect changes around the planet. Most of the state-of-the-art semantic segmentation models are trained in a fully supervised way u…

Image-to-Image Translation Land Cover Classification Segmentation Semantic Segmentation+1