A Modified and Enhanced Normalized built-up Index using Multispectral and Thermal Bands

Azmi, Rida; Alami, Omar Bachir; Saadane, Abd Errahim; Kacimi, Ilias; Chafiq, Tarik

doi:10.17485/ijst/2016/v9i27/87405

Cited by 11 publications

(4 citation statements)

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“…where p n and t n represent the predicted category and the true label category of the pixel, respectively, and the rest of the parameters are defned in the same way as in formula (1).…”

Section: Dice Loss Function Te Cross-entropy Loss Function (Equation (mentioning

confidence: 99%

“…Te accuracy of information extraction may be considerably improved by using high-resolution remote sensing images. Experts and scholars from all over the world have focused on remote sensing data classifcation methods in recent decades, ranging from supervised and unsupervised classifcations based on traditional statistical analysis [1]. Among these, the pixel-based statistical classifcation approach has emerged as the most popular and well developed, with promising results in particular domains [2].…”

Section: Introductionmentioning

confidence: 99%

“…Image segmentation errors include both over-segmentation and under-segmentation. Tese segmentation issues can afect the categorizing process in two ways: (1) Poorly segmented image objects with over-segmentation or under-segmentation errors produce image objects that span multiple classes, introducing classifcation errors because all pixels in each mixed image object must be assigned to the same class; (2) features extracted from poorly segmented image objects with over-segmentation or under-segmentation errors do not represent the properties of real objects on the Earth's surface (e.g., shape and surface area), so they may not be useful and may even reduce the accuracy of the classifcation.…”

Section: Introductionmentioning

confidence: 99%

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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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“…where p n and t n represent the predicted category and the true label category of the pixel, respectively, and the rest of the parameters are defned in the same way as in formula (1).…”

Section: Dice Loss Function Te Cross-entropy Loss Function (Equation (mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Abderrahim¹,

Azmi

Saadane³

2023

Scientific Programming

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“…The satellite images were subjected to atmospheric correction and radiometric correction to maintain spectral indices variables loyalty with magnitudes of brightness values [47]. The images were for the month of August to December with very little to no clouds.…”

Section: Datasetsmentioning

confidence: 99%

Impact Quantification of Decentralization in Urban Growth by Extracting Impervious Surfaces Using ISEI in Model Maker

et al. 2020

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Decentralization problems in Africa have caused some infrastructure disparity between country capitals and distant districts. In Ghana, less public investment has created a gap between implementation results and theoretical benefits. Spectral indices are a good approach to extracting impervious surfaces, which is a good method of measuring urbanization. These are restricted by complexity, sensor limitation, threshold values, and high computational time. In this study, we measure the urbanization dynamics of Wa District in Ghana by applying a proposed method of impervious surface extraction index (ISEI), to evaluate the decentralization policy using Landsat images from 1984–2018 and a single S2A data. Comparing our proposed method with five other existing indexes, ISEI provided good discriminated results between target feature and background, with pixel values ranging between 0 and +1. Other indexes produced negative values. ISEI accuracy varied from 84.62–94.00% while existing indexes varied from 73.85–90.00%. Our results also showed increased impervious surface areas of 83.26 km2, which is about 7.72% of total area while the average annual urban growth was recorded as 4.42%. These figures proved that the quantification of decentralization is very positive. The study provides a foundation for urban environment research in the context of decentralization policy.

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Identification of Impervious Built-Up Surface Features Using ResourceSat-2 LISS-III-Based Novel Optical Built-Up Index

Santra

Mitra

Sinha

et al. 2020

Remote Sensing and GIScience

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A Modified and Enhanced Normalized built-up Index using Multispectral and Thermal Bands

Cited by 11 publications

References 0 publications

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

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

Impact Quantification of Decentralization in Urban Growth by Extracting Impervious Surfaces Using ISEI in Model Maker

Identification of Impervious Built-Up Surface Features Using ResourceSat-2 LISS-III-Based Novel Optical Built-Up Index

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