Multiscale building segmentation based on deep learning for remote sensing RGB images from different sensors

Khoshboresh-Masouleh, Mehdi; Alidoost, F.; Arefi, Hossein

doi:10.1117/1.jrs.14.034503

Cited by 36 publications

(14 citation statements)

References 41 publications

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“…Deep learning is a part of machine learning and can play an important role in real-world applications, such as bioinformatics and computational biology [ 40 ], remote sensing [ 41 ], photogrammetric computer vision [ 42 ], medicine [ 43 ], and 3D modeling [ 44 ]. Digital signal and image analysis using deep learning methods, particularly convolutional neural networks, is an explosively growing field.…”

Section: Methodsmentioning

confidence: 99%

Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods

Muneeb

Henschel

2021

BMC Bioinformatics

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Background Genotype–phenotype predictions are of great importance in genetics. These predictions can help to find genetic mutations causing variations in human beings. There are many approaches for finding the association which can be broadly categorized into two classes, statistical techniques, and machine learning. Statistical techniques are good for finding the actual SNPs causing variation where Machine Learning techniques are good where we just want to classify the people into different categories. In this article, we examined the Eye-color and Type-2 diabetes phenotype. The proposed technique is a hybrid approach consisting of some parts from statistical techniques and remaining from Machine learning. Results The main dataset for Eye-color phenotype consists of 806 people. 404 people have Blue-Green eyes where 402 people have Brown eyes. After preprocessing we generated 8 different datasets, containing different numbers of SNPs, using the mutation difference and thresholding at individual SNP. We calculated three types of mutation at each SNP no mutation, partial mutation, and full mutation. After that data is transformed for machine learning algorithms. We used about 9 classifiers, RandomForest, Extreme Gradient boosting, ANN, LSTM, GRU, BILSTM, 1DCNN, ensembles of ANN, and ensembles of LSTM which gave the best accuracy of 0.91, 0.9286, 0.945, 0.94, 0.94, 0.92, 0.95, and 0.96% respectively. Stacked ensembles of LSTM outperformed other algorithms for 1560 SNPs with an overall accuracy of 0.96, AUC = 0.98 for brown eyes, and AUC = 0.97 for Blue-Green eyes. The main dataset for Type-2 diabetes consists of 107 people where 30 people are classified as cases and 74 people as controls. We used different linear threshold to find the optimal number of SNPs for classification. The final model gave an accuracy of 0.97%. Conclusion Genotype–phenotype predictions are very useful especially in forensic. These predictions can help to identify SNP variant association with traits and diseases. Given more datasets, machine learning model predictions can be increased. Moreover, the non-linearity in the Machine learning model and the combination of SNPs Mutations while training the model increases the prediction. We considered binary classification problems but the proposed approach can be extended to multi-class classification.

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Section: Methodsmentioning

confidence: 99%

Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods

Muneeb

Henschel

2021

BMC Bioinformatics

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“…However, the analyzed data sample included just a few buildings, so it is difficult to consider their results as representative. A profound and detailed accuracy analysis was presented in Khoshboresh-Masouleh et al [61]. The authors evaluated building footprints' results for different types of built-up areas, e.g., shadowed, vegetation-rich, complex roofs and high-density, obtaining a mean IoU value of 76%.…”

Section: Discussionmentioning

confidence: 99%

Polish Cadastre Modernization with Remotely Extracted Buildings from High-Resolution Aerial Orthoimagery and Airborne LiDAR

Wierzbicki¹,

Matuk²,

Bielecka³

2021

Remote Sensing

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Automatic building extraction from remote sensing data is a hot but challenging research topic for cadastre verification, modernization and updating. Deep learning algorithms are perceived as more promising in overcoming the difficulties of extracting semantic features from complex scenes and large differences in buildings’ appearance. This paper explores the modified fully convolutional network U-Shape Network (U-Net) for high resolution aerial orthoimagery segmentation and dense LiDAR data to extract building outlines automatically. The three-step end-to-end computational procedure allows for automated building extraction with an 89.5% overall accuracy and an 80.7% completeness, which made it very promising for cadastre modernization in Poland. The applied algorithms work well both in densely and poorly built-up areas, typical for peripheral areas of cities, where uncontrolled development had recently been observed. Discussing the possibilities and limitations, the authors also provide some important information that could help local authorities decide on the use of remote sensing data in land administration.

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“…Multi-size kernels have achieved decent results in building extraction [44] and cloud/cloud shadow segmentation [45]. This study used this technique to extract the fire's extent better and discriminate between fire and other objects with high reflectivity in the SWIR2 band.…”

Section: Effect Of Multi-size Kernels and Dclsmentioning

confidence: 99%

Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning

et al. 2022

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Active fires are devastating natural disasters that cause socio-economical damage across the globe. The detection and mapping of these disasters require efficient tools, scientific methods, and reliable observations. Satellite images have been widely used for active fire detection (AFD) during the past years due to their nearly global coverage. However, accurate AFD and mapping in satellite imagery is still a challenging task in the remote sensing community, which mainly uses traditional methods. Deep learning (DL) methods have recently yielded outstanding results in remote sensing applications. Nevertheless, less attention has been given to them for AFD in satellite imagery. This study presented a deep convolutional neural network (CNN) “MultiScale-Net” for AFD in Landsat-8 datasets at the pixel level. The proposed network had two main characteristics: (1) several convolution kernels with multiple sizes, and (2) dilated convolution layers (DCLs) with various dilation rates. Moreover, this paper suggested an innovative Active Fire Index (AFI) for AFD. AFI was added to the network inputs consisting of the SWIR2, SWIR1, and Blue bands to improve the performance of the MultiScale-Net. In an ablation analysis, three different scenarios were designed for multi-size kernels, dilation rates, and input variables individually, resulting in 27 distinct models. The quantitative results indicated that the model with AFI-SWIR2-SWIR1-Blue as the input variables, using multiple kernels of sizes 3 × 3, 5 × 5, and 7 × 7 simultaneously, and a dilation rate of 2, achieved the highest F1-score and IoU of 91.62% and 84.54%, respectively. Stacking AFI with the three Landsat-8 bands led to fewer false negative (FN) pixels. Furthermore, our qualitative assessment revealed that these models could detect single fire pixels detached from the large fire zones by taking advantage of multi-size kernels. Overall, the MultiScale-Net met expectations in detecting fires of varying sizes and shapes over challenging test samples.

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Multiscale building segmentation based on deep learning for remote sensing RGB images from different sensors

Cited by 36 publications

References 41 publications

Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods

Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods

Polish Cadastre Modernization with Remotely Extracted Buildings from High-Resolution Aerial Orthoimagery and Airborne LiDAR

Active Fire Detection from Landsat-8 Imagery Using Deep Multiple Kernel Learning

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