Generate knowledge base from very high spatial resolution satellite image using robust classification rules and genetic programming

Azmi, Rida; Hicham, Amar; Abderrahim, Norelyaqine

doi:10.1109/morgeo49228.2020.9121914

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…In recent years, object-oriented classifcation algorithms have attracted researchers' interest [3,4]. It has been demonstrated to have the ability to overcome the sufering from some forms with per-pixel analysis, such as the omission of geometric and contextual information.…”

Section: Introductionmentioning

confidence: 99%

Architecture of Deep Convolutional Encoder-Decoder Networks for Building Footprint Semantic Segmentation

Abderrahim¹,

Azmi

Saadane³

2023

Scientific Programming

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Building extraction from high-resolution aerial images is critical in geospatial applications such as telecommunications, dynamic urban monitoring, updating geographic databases, urban planning, disaster monitoring, and navigation. Automatic building extraction is a massive task because buildings in various places have varied spectral and geometric qualities. As a result, traditional image processing approaches are insufficient for autonomous building extraction from high-resolution aerial imaging applications. Automatic object extraction from high-resolution images has been achieved using semantic segmentation and deep learning models, which have become increasingly important in recent years. In this study, the U-Net model was used for building extraction, initially designed for biomedical image analysis. The encoder part of the U-Net model has been improved with ResNet50, VGG19, VGG16, DenseNet169, and Xception. However, three other models have been implemented to test the performance of the model studied: PSPNet, FPN, and LinkNet. The performance analysis through the intersection of union method has shown that U-Net with the VGG16 encoder presents the best results compared to the other models with a high IoU score of 83.06%. This research aims to examine the effectiveness of these four approaches for extracting buildings from high-resolution aerial data.

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Section: Introductionmentioning

confidence: 99%