Autonomous Building Detection Using Edge Properties and Image Color Invariants

Ghandour, Ali; Jezzini, Abedelkarim A.

doi:10.3390/buildings8050065

Cited by 20 publications

(13 citation statements)

References 26 publications

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“…There are several articles in the literature aimed to construct an automatic building detection system which is based on several segmentation algorithms ( Hermosilla et al, 2011 ). Ghandour & Jezzini (2018) presented an automatic building detection algorithm based on aerial satellite images. Their algorithm was based edge detection and the features extracted from image color invariants.…”

Section: Related Workmentioning

confidence: 99%

SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images

Shahin

Almotairi

2021

PeerJ Computer Science

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Building detection in high-resolution satellite images has received great attention, as it is important to increase the accuracy of urban planning. The building boundary detection in the desert environment is a real challenge due to the nature of low contrast images in the desert environment. The traditional computer vision algorithms for building boundary detection lack scalability, robustness, and accuracy. On the other hand, deep learning detection algorithms have not been applied to such low contrast satellite images. So, there is a real need to employ deep learning algorithms for building detection tasks in low contrast high-resolution images. In this paper, we propose a novel building detection method based on a single-shot multi-box (SSD) detector. We develop the state-of-the-art SSD detection algorithm based on three approaches. First, we propose data-augmentation techniques to overcome the low contrast images’ appearance. Second, we develop the SSD backbone using a novel saliency visual attention mechanism. Moreover, we investigate several pre-trained networks performance and several fusion functions to increase the performance of the SSD backbone. The third approach is based on optimizing the anchor-boxes sizes which are used in the detection stage to increase the performance of the SSD head. During our experiments, we have prepared a new dataset for buildings inside Riyadh City, Saudi Arabia that consists of 3878 buildings. We have compared our proposed approach vs other approaches in the literature. The proposed system has achieved the highest average precision, recall, F1-score, and IOU performance. Our proposed method has achieved a fast average prediction time with the lowest variance for our testing set. Our experimental results are very promising and can be generalized to other object detection tasks in low contrast images.

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Section: Related Workmentioning

confidence: 99%

SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images

Shahin

Almotairi

2021

PeerJ Computer Science

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“…The major binary blob was utilized to extract the building boundary edges. Then, the orientation angle has been calculated as in Ghandour and Jezzini [42]:…”

Section: Related Workmentioning

confidence: 99%

DCRN: An Optimized Deep Convolutional Regression Network for Building Orientation Angle Estimation in High-Resolution Satellite Images

Shahin

Almotairi

2021

Electronics

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Recently, remote sensing satellite image analysis has received significant attention from geo-information scientists. However, the current geo-information systems lack automatic detection of several building characteristics inside the high-resolution satellite images. The accurate extraction of buildings characteristics helps the decision-makers to optimize urban planning and achieve better decisions. Furthermore, Building orientation angle is a very critical parameter in the accuracy of automated building detection algorithms. However, the traditional computer vision techniques lack accuracy, scalability, and robustness for building orientation angle detection. This paper proposes two different approaches to deep building orientation angle estimation in the high-resolution satellite image. Firstly, we propose a transfer deep learning approach for our estimation task. Secondly, we propose a novel optimized DCRN network consisting of pre-processing, scaled gradient layer, deep convolutional units, dropout layers, and regression end layer. The early proposed gradient layer helps the DCRN network to extract more helpful information and increase its performance. We have collected a building benchmark dataset that consists of building images in Riyadh city. The images used in the experiments are 15,190 buildings images. In our experiments, we have compared our proposed approaches and the other approaches in the literature. The proposed system has achieved the lowest root mean square error (RMSE) value of 1.24, the lowest mean absolute error (MAE) of 0.16, and the highest adjusted R-squared value of 0.99 using the RMS optimizer. The cost of processing time of our proposed DCRN architecture is 0.0113 ± 0.0141 s. Our proposed approach has proven its stability with the input building image contrast variation for all orientation angles. Our experimental results are promising, and it is suggested to be utilized in other building characteristics estimation tasks in high-resolution satellite images.

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“…For the scope of this work, we used building detection algorithm proposed in [10] without loss of generality. We construct an array of the centroids of the detected buildings in the pre-war image, which we refer to as Pre-war Buidings' Centroids Set PBCS.…”

Section: Damaged Building Candidatesmentioning

confidence: 99%

Post-War Building Damage Detection

Ghandour

Jezzini

2018

The 2nd International Electronic Conference on Remote Sensing

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Natural disaster and wars wreak havoc not only on individuals and critical infrastructure, but also leave behind ruined residential buildings and housings. The size, type and location of damaged houses are essential data sources for the post-disaster reconstruction process. Building damage detection due to war activities has not been thoroughly discussed in the literature. In this work, an automated building damage detection technique that relies on both pre-and post-war aerial images is proposed. Building damage estimation is done using shadow information and Gray Level Co-occurrence Matrix features. Accuracy assessment applied over a Syrian war-affected zone near Damascus, reveals the excellent performance of the proposed technique.

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Autonomous Building Detection Using Edge Properties and Image Color Invariants

Cited by 20 publications

References 26 publications

SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images

SVA-SSD: saliency visual attention single shot detector for building detection in low contrast high-resolution satellite images

DCRN: An Optimized Deep Convolutional Regression Network for Building Orientation Angle Estimation in High-Resolution Satellite Images

Post-War Building Damage Detection

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