International Conference on Computer and Knowledge Engineering

Home / 14th International Conference on Computer and Knowledge Engineering

Adaptive Multi-Scale Attentional Network for Semantic Segmentation of Remote Sensing Images

Authors :

Melika Zare¹ Sattar Hashemi²

1- Comp. Sci. & Eng. Dept, Shiraz University, Iran 2- Comp. Sci. & Eng. Dept, Shiraz University, Iran

Keywords :

semantic segmentation،remote sensing،residual structure،attention mechanism،convolutional neural network

Abstract :

High-resolution remote sensing image segmentation is challenging due to the complex and diverse nature of ground objects, varying scales, and the presence of small targets that are difficult to detect. Traditional methods often fall short in capturing multi-scale contextual information and accurately segmenting small objects amidst large, cluttered backgrounds. In this paper, we propose the Adaptive Multi-Scale Attentional Network (AMSANet), which addresses these challenges by leveraging a Hierarchical Multi-Scale Dilated Convolution Module and an Efficient Residual Atrous Spatial Pyramid Pooling (ER-ASPP) module to enhance feature extraction and refinement. The AMSANet architecture integrates these advanced modules to effectively capture and merge features at multiple scales, employing coordinate attention mechanisms for precise final refinement. We validate our approach on the ISPRS Vaihingen and Potsdam datasets, demonstrating that AMSANet significantly outperforms state-of-the-art methods in terms of mean intersection over union (mIoU), overall accuracy (OA), and F1 score. Our experimental results underscore the efficacy of AMSANet in improving segmentation accuracy and robustness, particularly in managing high-resolution remote sensing images with intricate details and heterogeneous landscapes.

Papers List

List of archived papers

Cardiology Disease Diagnosis by Analyzing Histological Microscopic Images Using Deep Learning

Maria Salehpanah - Jafar Tanha - Zahra Jafari - SeyedEhsan Roshan - Sajad Rezaei

Decentralized Federated Learning in IoT Environments: A Hierarchical Approach

Majid Mohammadpour - Seyedakbar Mostafavi

Advancing Brain Tumor Detection via ViRCNN: A Fusion of Vision Transformers and Faster R-CNN

Mehrshad Momen-Tayefeh - S. AmirAli GH. Ghahramani - Ali Mohammad Afshin Hemmatyar

Time Series Analysis by Bi-GRU for Forecasting Bitcoin Trends based on Sentiment Analysis

Fatemeh Saadatmand - Mohammad Ali Zare Chahoki

An intelligent linguistic error detection approach to automated diagnosis of Dyslexia disorder in Persian speaking children

Fatemeh Asghari - Mahsa Khorasani - Mohsen Kahani - Seyed Amir Amin Yazdi - Mahdi Arkhodi Ghalenoei

UAV-based Firefighting by Multi-agent Reinforcement Learning

Reza Shami Tanha - Mohsen Hooshmand - Mohsen Afsharchi

African Vultures Optimization Algorithm for Optimal Damping Controllers Design in the Electrical Power Grid System

Aliyu Sabo - Theophilus Ebuka Odoh - Samuel Habu - Hossein Shahinzadeh - Farshad Ebrahimi

A Survey of the AVOA Metaheuristic Algorithm and its Suitability for Power System Optimization and Damping Controller Design

Aliyu Sabo - Theophilus Ebuka Odoh - Samuel Habu - Hossien Shahinzadeh - Farshad Ebrahimi

Improving Machine Learning Classification of Heart Disease Using the Graph-Based Techniques

Abolfazl Dibaji - Sadegh Sulaimany

Efficient T-Count Fault-tolerant Quantum Clifford+T Multiplexer

Negin Mashayekhi - Shekoofeh Moghimi - Mohammad Reza Reshadinezhad