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C. Dastagiraiah, G. Sreeja, T. Santosh Bhargava, V. Tharun Raj, B. Koustubh Srivatsa
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Page No: 1 - 16
Abstract : Accurate classification of high-resolution satellite imagery is essential for various socio-economic and environmental applications, including urban planning and natural resource management. This study proposes an enhanced U-Net architecture integrated with a Spatial Pyramid Pooling (SPP) layer to improve pixel-level semantic segmentation of satellite images. The traditional U-Net model often loses critical object boundaries during pooling operations, which can hinder classification performance. By incorporating SPP, which captures multi-scale contextual information, the proposed model retains spatial details and enhances the ability to differentiate between similar land cover types. Experimental results on two public datasets demonstrate that our improved model outperforms existing algorithms trained with various datasets in terms of classification accuracy and boundary preservation.
Keyword Satellite image classification, Deep Learning, U-Net, Spatial Pyramid Pooling, Semantic Segmentation.
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Abstract

Accurate classification of high-resolution satellite imagery is crucial for environmental monitoring, urban development, and natural resource management. This study introduces an improved U-Net architecture enhanced with a Spatial Pyramid Pooling (SPP) layer to achieve precise pixel-level semantic segmentation. Traditional U-Net models often struggle with preserving fine object boundaries due to pooling operations. By integrating SPP, which captures multi-scale spatial context, our model maintains critical boundary information and improves the distinction between similar land cover types. Experiments conducted on two benchmark datasets show that the enhanced model outperforms existing deep learning approaches in terms of segmentation accuracy and edge preservation.Satellite Imagery Land Classification

Semantic Segmentation of Satellite Imagery for Land Cover Classification

Introduction

The demand for up-to-date and accurate earth surface information is rising due to its vital role in global, regional, and local planning. Applications range from natural resource management to land-use monitoring and urban development. Satellite image classification plays a key role in geographic information systems (GIS) used for environmental studies, policy-making, and infrastructure development.Satellite Imagery Land Classification

Over the past decade, numerous techniques have been developed to extract land use information from satellite images (Gong et al., 2015). Among them, Deep Learning has emerged as a powerful tool in image analysis due to its ability to handle complex data and deliver high accuracy. However, applying deep learning to satellite image segmentation remains a challenge. Generating pixel-level ground truth data is labor-intensive and time-consuming. Moreover, satellite imagery often shows high variability within classes and minimal contrast between different classes, complicating accurate segmentation.

Semantic segmentation in satellite imagery involves classifying each pixel into predefined categories such as roads, buildings, vegetation, water bodies, or unlabeled regions. This fine-grained classification is essential for tasks like land cover mapping, urban planning, and environmental assessment. However, traditional models often struggle to maintain accuracy in high-resolution images due to detail loss during pooling.

To address these challenges, this paper proposes an enhanced U-Net model incorporating a Spatial Pyramid Pooling (SPP) layer. This modification improves the model’s ability to capture and retain spatial context at multiple scales, leading to more accurate and consistent classification, especially along object boundaries.

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