Multitask GLocal OBIA-Mamba for Sentinel-2 Landcover Mapping

Computer Vision & MultiModal AI

Published: arXiv: 2511.10604v1

Authors

Zack Dewis Yimin Zhu Zhengsen Xu Mabel Heffring Saeid Taleghanidoozdoozan Kaylee Xiao Motasem Alkayid Lincoln Linlin Xu

Abstract

Although Sentinel-2 based land use and land cover (LULC) classification is critical for various environmental monitoring applications, it is a very difficult task due to some key data challenges (e.g., spatial heterogeneity, context information, signature ambiguity). This paper presents a novel Multitask Glocal OBIA-Mamba (MSOM) for enhanced Sentinel-2 classification with the following contributions. First, an object-based image analysis (OBIA) Mamba model (OBIA-Mamba) is designed to reduce redundant computation without compromising fine-grained details by using superpixels as Mamba tokens. Second, a global-local (GLocal) dual-branch convolutional neural network (CNN)-mamba architecture is designed to jointly model local spatial detail and global contextual information. Third, a multitask optimization framework is designed to employ dual loss functions to balance local precision with global consistency. The proposed approach is tested on Sentinel-2 imagery in Alberta, Canada, in comparison with several advanced classification approaches, and the results demonstrate that the proposed approach achieves higher classification accuracy and finer details that the other state-of-the-art methods.

Paper Summary

Problem

The main problem addressed in this research paper is the challenge of accurately classifying land use and land cover (LULC) from Sentinel-2 satellite images. This is a critical task for various environmental monitoring applications, such as biodiversity monitoring, urban planning, and environmental management. However, the classification process is difficult due to several key data challenges, including spatial heterogeneity, context information, and signature ambiguity.

Key Innovation

The key innovation of this work is the development of a novel approach called Multitask Glocal OBIA-Mamba (MSOM) for enhanced Sentinel-2 classification. MSOM combines object-based image analysis (OBIA) with a Mamba model, which is a type of neural network. The approach uses superpixels as tokens, reducing redundant computation and preserving fine-grained details. It also employs a global-local dual-branch convolutional neural network (CNN)-Mamba architecture to jointly model local spatial detail and global contextual information.

Practical Impact

This research has significant practical implications for various fields, including environmental monitoring, urban planning, and land use management. The proposed MSOM approach can be applied to classify LULC from Sentinel-2 imagery, providing accurate and efficient results. This can help policymakers and stakeholders make informed decisions about land use, conservation, and resource management. Additionally, the approach can be used to monitor changes in land use and land cover over time, enabling early detection of environmental issues and more effective management of natural resources.

Analogy / Intuitive Explanation

Imagine trying to classify different types of rocks in a landscape. Traditional methods would look at each individual rock and try to identify its characteristics. However, this approach can be time-consuming and may not capture the relationships between different rocks. The MSOM approach is like using a map to understand the broader landscape, taking into account the relationships between different rocks and their spatial context. By doing so, it can identify patterns and features that might be missed by traditional methods, leading to more accurate and efficient classification results.

Paper Information

Categories:

cs.CV cs.LG

Published Date:

arXiv ID:

2511.10604v1

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