A Framework for Automatic Building Detection from Low-Contrast Satellite Images

Aamir, Muhammad; Pu, YoungGun; Rahman, Ziaur; Tahir, Muhammad Faizan; Naeem, Hamad; Dai, Qiang

doi:10.3390/sym11010003

Cited by 32 publications

(12 citation statements)

References 64 publications

(119 reference statements)

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“…Furthermore, in order to demonstrate the link between the accuracy and the appropriate method selection for satellite building detection the work presented at the survey papers and frameworks [36,37] demonstrates the difference performance expectations in relation to the selected methods and the corresponding datasets and applications. is an option to apply 3D reconstruction methods [38,39] aiming to obtain an estimate of the buildings' height map.…”

Section: Related Workmentioning

confidence: 99%

A modular CNN-based building detector for remote sensing images

et al. 2020

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Related Workmentioning

confidence: 99%

A modular CNN-based building detector for remote sensing images

et al. 2020

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“…Therefore, it is challenging to realize state-of-the-art speed and quality in object detection. To overcome this challenge, numerous approaches have been proposed to detect objects in images efficiently [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Region-Based Efficient Network for Accurate Object Detection

Guan¹,

Aamir²,

Hu³

et al. 2021

Self Cite

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Object detection in images is an important task in image processing and computer vision. Many approaches are available for object detection. For example, there are numerous algorithms for object positioning and classification in images. However, the current methods perform poorly and lack experimental verification. Thus, it is a fascinating and challenging issue to position and classify image objects. Drawing on the recent advances in image object detection, this paper develops a region-baed efficient network for accurate object detection in images. To improve the overall detection performance, image object detection was treated as a twofold problem, involving object proposal generation and object classification. First, a framework was designed to generate high-quality, class-independent, accurate proposals. Then, these proposals, together with their input images, were imported to our network to learn convolutional features. To boost detection efficiency, the number of proposals was reduced by a network refinement module, leaving only a few eligible candidate proposals. After that, the refined candidate proposals were loaded into the detection module to classify the objects. The proposed model was tested on the test set of the famous PASCAL Visual Object Classes Challenge 2007 (VOC2007). The results clearly demonstrate that our model achieved robust overall detection efficiency over existing approaches using fewer or more proposals, in terms of recall, mean average best overlap (MABO), and mean average precision (mAP).

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“…Satellite imagery has a wide variety of texture types that represent different target areas: roads, rivers, lakes, land, vegetation, buildings, and more [2]. Many applications for sustainable project management aim to distinguish these areas in grayscale and/or color images, and edge detection has been used as the pre-processing step before the other subsequent stages of data processing [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Algorithm Selection for Edge Detection in Satellite Images by Neutrosophic WASPAS Method

Baušys

Kazakeviciute-Januskeviciene

Cavallaro

et al. 2020

Sustainability

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Nowadays, integrated land management is generally governed by the principles of sustainability. Land use management usually is grounded in satellite image information. The detection and monitoring of areas of interest in satellite images is a difficult task. We propose a new methodology for the adaptive selection of edge detection algorithms using visual features of satellite images and the multi-criteria decision-making (MCDM) method. It is not trivial to select the most appropriate method for the chosen satellite images as there is no proper algorithm for all cases as it depends on many factors, like acquisition and content of the raster images, visual features of real-world images, and humans’ visual perception. The edge detection algorithms were ranked according to their suitability for the appropriate satellite images using the neutrosophic weighted aggregated sum product assessment (WASPAS) method. The results obtained using the created methodology were verified with results acquired in an alternative way—using the edge detection algorithms for specific images. This methodology facilitates the selection of a proper edge detector for the chosen image content.

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A Framework for Automatic Building Detection from Low-Contrast Satellite Images

Cited by 32 publications

References 64 publications

A modular CNN-based building detector for remote sensing images

A modular CNN-based building detector for remote sensing images

A Region-Based Efficient Network for Accurate Object Detection

Algorithm Selection for Edge Detection in Satellite Images by Neutrosophic WASPAS Method

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