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<p>Advances in satellite spatial analysis enhance Land Use/Land Cover (LU/LC) assessment accuracy. Over the past four decades, Deep Learning Algorithms have seen substantial progress, especially in the comparison between Pixel-Based (PB) and Object-Based (OB) methods. These developments have greatly enhanced the reliability of satellite data for both scientific research and practical applications. This study investigates LC mapping in regions affected by mining activities, utilizing Earth observation data to explore potential benefits and insights. By conducting a comprehensive review of existing literature, the research aims to clarify the strengths and limitations of using spatial features for accurate LU/LC classification. The study offers an in-depth analysis of PB and OB techniques in satellite image classification, using samples from a false-colour composite image to train and validate Deep Convolutional Neural Networks (DCNNs) and Deep Neural Networks (DNNs) models. OB samples, comprising 6,000 carefully selected 6 × 6-pixel image samples representing various LU types, were compared to PB samples that utilized all pixels within the image samples. The comparison revealed that OB-DCNNs classification achieved a superior accuracy of 97.5%, compared to 91.5% for PB-DNNs classification. These findings highlight the enhanced effectiveness of OB classification for precise LU/LC assessment from satellite imagery.</p>

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Comparative evaluation of Pixel-Based and Object-Based classification approaches for Land Use/Land Cover mapping using deep learning on satellite data

  • 期刊论文

  • 发表日期：

    2025年11月10日

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  • Ajay Kumar,
  • Avinash Kumar Ranjan,
  • Priyanka Paygude,
  • Rachan Daimary

摘要

Advances in satellite spatial analysis enhance Land Use/Land Cover (LU/LC) assessment accuracy. Over the past four decades, Deep Learning Algorithms have seen substantial progress, especially in the comparison between Pixel-Based (PB) and Object-Based (OB) methods. These developments have greatly enhanced the reliability of satellite data for both scientific research and practical applications. This study investigates LC mapping in regions affected by mining activities, utilizing Earth observation data to explore potential benefits and insights. By conducting a comprehensive review of existing literature, the research aims to clarify the strengths and limitations of using spatial features for accurate LU/LC classification. The study offers an in-depth analysis of PB and OB techniques in satellite image classification, using samples from a false-colour composite image to train and validate Deep Convolutional Neural Networks (DCNNs) and Deep Neural Networks (DNNs) models. OB samples, comprising 6,000 carefully selected 6 × 6-pixel image samples representing various LU types, were compared to PB samples that utilized all pixels within the image samples. The comparison revealed that OB-DCNNs classification achieved a superior accuracy of 97.5%, compared to 91.5% for PB-DNNs classification. These findings highlight the enhanced effectiveness of OB classification for precise LU/LC assessment from satellite imagery.