scholarly journals Sequentially Delineation of Rooftops with Holes from VHR Aerial Images Using a Convolutional Recurrent Neural Network

2021 ◽  
Vol 13 (21) ◽  
pp. 4271
Author(s):  
Wei Huang ◽  
Zeping Liu ◽  
Hong Tang ◽  
Jiayi Ge
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Irregular Shapes ◽  
Segmentation Methods ◽  
High Resolution Images ◽  
Novel Method ◽  
Statistical Indicators ◽  
Instance Segmentation

Semantic and instance segmentation methods are commonly used to build extraction from high-resolution images. The semantic segmentation method involves assigning a class label to each pixel in the image, thus ignoring the geometry of the building rooftop, which results in irregular shapes of the rooftop edges. As for instance segmentation, there is a strong assumption within this method that there exists only one outline polygon along the rooftop boundary. In this paper, we present a novel method to sequentially delineate exterior and interior contours of rooftops with holes from VHR aerial images, where most of the buildings have holes, by integrating semantic segmentation and polygon delineation. Specifically, semantic segmentation from the Mask R-CNN is used as a prior for hole detection. Then, the holes are used as objects for generating the internal contours of the rooftop. The external and internal contours of the rooftop are inferred separately using a convolutional recurrent neural network. Experimental results showed that the proposed method can effectively delineate the rooftops with both one and multiple polygons and outperform state-of-the-art methods in terms of the visual results and six statistical indicators, including IoU, OA, F1, BoundF, RE and Hd.

scholarly journals Improved Anchor-Free Instance Segmentation for Building Extraction from High-Resolution Remote Sensing Images

2020 ◽  
Vol 12 (18) ◽  
pp. 2910
Author(s):  
Tong Wu ◽  
Yuan Hu ◽  
Ling Peng ◽  
Ruonan Chen
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Building Extraction ◽  
Remote Sensing Images ◽  
Highly Sensitive ◽  
Segmentation Methods ◽  
Speed And Accuracy ◽  
Instance Segmentation

Building extraction from high-resolution remote sensing images plays a vital part in urban planning, safety supervision, geographic databases updates, and some other applications. Several researches are devoted to using convolutional neural network (CNN) to extract buildings from high-resolution satellite/aerial images. There are two major methods, one is the CNN-based semantic segmentation methods, which can not distinguish different objects of the same category and may lead to edge connection. The other one is CNN-based instance segmentation methods, which rely heavily on pre-defined anchors, and result in the highly sensitive, high computation/storage cost and imbalance between positive and negative samples. Therefore, in this paper, we propose an improved anchor-free instance segmentation method based on CenterMask with spatial and channel attention-guided mechanisms and improved effective backbone network for accurate extraction of buildings in high-resolution remote sensing images. Then we analyze the influence of different parameters and network structure on the performance of the model, and compare the performance for building extraction of Mask R-CNN, Mask Scoring R-CNN, CenterMask, and the improved CenterMask in this paper. Experimental results show that our improved CenterMask method can successfully well-balanced performance in terms of speed and accuracy, which achieves state-of-the-art performance at real-time speed.

scholarly journals Orchard Mapping with Deep Learning Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3813
Author(s):  
Athanasios Anagnostis ◽  
Aristotelis C. Tagarakis ◽  
Dimitrios Kateris ◽  
Vasileios Moysiadis ◽  
Claus Grøn Sørensen ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Automated Detection ◽  
Aerial Images ◽  
Training Dataset ◽  
Field Boundary ◽  
Different Seasons ◽  
Detection And Localization ◽  
Different Levels

This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.

Intelligent Identification of Flow Regime Based on a Novel Neural Network

2014 ◽  
Vol 635-637 ◽  
pp. 1715-1718
Author(s):  
Qiang Wang
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Flow Regime ◽  
Recurrent Neural Network ◽  
Pressure Difference ◽  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Elman Neural Network ◽  
Flow Regime Identification ◽  
Novel Method

A noveol neural network of Elman is typically dynamic recurrent neural network. A novel method of flow regime identification based on Elman neural network and wavelet packet decomposition is proposed in this paper. Above all, the collected pressure-difference fluctuation signals are decomposed by the four-layer wavelet packet, and the decomposed signals in various frequency bands are obtained within the frequency domain. Then the wavelet packet energy eigenvectors of flow regimes are established. At last the wavelet packet energy eigenvectors are input into Elman neural network and flow regime intelligent identification can be performed.

scholarly journals Patch-Wise Semantic Segmentation for Hyperspectral Images via a Cubic Capsule Network with EMAP Features

2021 ◽  
Vol 13 (17) ◽  
pp. 3497
Author(s):  
Le Sun ◽  
Xiangbo Song ◽  
Huxiang Guo ◽  
Guangrui Zhao ◽  
Jinwei Wang
Keyword(s):  
Neural Network ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Semantic Segmentation ◽  
Hyperspectral Images ◽  
Experimental Comparison ◽  
Spatial Features ◽  
Segmentation Methods ◽  
High Layer ◽  
Morphological Attribute

In order to overcome the disadvantages of convolution neural network (CNN) in the current hyperspectral image (HSI) classification/segmentation methods, such as the inability to recognize the rotation of spatial objects, the difficulty to capture the fine spatial features and the problem that principal component analysis (PCA) ignores some important information when it retains few components, in this paper, an HSI segmentation model based on extended multi-morphological attribute profile (EMAP) features and cubic capsule network (EMAP–Cubic-Caps) was proposed. EMAP features can effectively extract various attributes profile features of entities in HSI, and the cubic capsule neural network can effectively capture complex spatial features with more details. Firstly, EMAP algorithm is introduced to extract the morphological attribute profile features of the principal components extracted by PCA, and the EMAP feature map is used as the input of the network. Then, the spectral and spatial low-layer information of the HSI is extracted by a cubic convolution network, and the high-layer information of HSI is extracted by the capsule module, which consists of an initial capsule layer and a digital capsule layer. Through the experimental comparison on three well-known HSI datasets, the superiority of the proposed algorithm in semantic segmentation is validated.

scholarly journals Prediction of PCR amplification from primer and template sequences using recurrent neural network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kotetsu Kayama ◽  
Miyuki Kanno ◽  
Naoto Chisaki ◽  
Misaki Tanaka ◽  
Reika Yao ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Pcr Amplification ◽  
Nucleotide Sequences ◽  
Specific Primer ◽  
First Report ◽  
Novel Method ◽  
Primer Sequence ◽  
Dna Template ◽  
The Relationship

AbstractWe have developed a novel method to predict the success of PCR amplification for a specific primer set and DNA template based on the relationship between the primer sequence and the template. To perform the prediction using a recurrent neural network, the usual double-stranded formation between the primer and template nucleotide sequences was herein expressed as a five-lettered word. The set of words (pseudo-sentences) was placed to indicate the success or failure of PCR targeted to learn recurrent neural network (RNN). After learning pseudo-sentences, RNN predicted PCR results from pseudo-sentences which were created by primer and template sequences with 70% accuracy. These results suggest that PCR results could be predicted using learned RNN and the trained RNN could be used as a replacement for preliminary PCR experimentation. This is the first report which utilized the application of neural network for primer design and prediction of PCR results.

scholarly journals Optimizing the Recognition and Feature Extraction of Wind Turbines through Hybrid Semantic Segmentation Architectures

2020 ◽  
Vol 12 (22) ◽  
pp. 3743
Author(s):  
Miguel-Ángel Manso-Callejo ◽  
Calimanut-Ionut Cira ◽  
Ramón Alcarria ◽  
José-Juan Arranz-Justel
Keyword(s):  
Wind Turbines ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Training Dataset ◽  
Network Architectures ◽  
Positional Accuracy ◽  
High Resolution Images ◽  
Alternative Sources ◽  
Geometric Quality

Updating the mapping of wind turbines farms—found in constant expansion—is important to predict energy production or to minimize the risk of these infrastructures during storms. This geoinformation is not usually provided by public mapping agencies, and the alternative sources are usually consortiums or individuals interested in mapping and study. However, they do not offer metadata or genealogy, and their quality is unknown. This article presents a methodology oriented to optimize the recognition and extraction of features (wind turbines) using hybrid architectures of semantic segmentation. The aim is to characterize the quality of these datasets and help to improve and update them automatically at a large-scale. To this end, we intend to evaluate the capacity of hybrid semantic segmentation networks trained to extract features representing wind turbines from high-resolution images and to characterize the positional accuracy and completeness of a dataset whose genealogy and quality are unknown. We built a training dataset composed of 5140 tiles of aerial images and their cartography to train six different neural network architectures. The networks were evaluated on five test areas (covering 520 km2 of the Spanish territory) to identify the best segmentation architecture (in our case, LinkNet as base architecture and EfficientNet-b3 as the backbone). This hybrid segmentation model allowed us to characterize the completeness—both by commission and by omission—of the available georeferenced wind turbine dataset, as well as its geometric quality.

scholarly journals Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images

2017 ◽  
Vol 9 (5) ◽  
pp. 446 ◽  
Author(s):  
Hongzhen Wang ◽  
Ying Wang ◽  
Qian Zhang ◽  
Shiming Xiang ◽  
Chunhong Pan
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Semantic Segmentation ◽  
High Resolution Images

scholarly journals Learning Segmentation Masks with the Independence Prior

2019 ◽  
Vol 33 ◽  
pp. 3429-3436 ◽  
Author(s):  
Songmin Dai ◽  
Xiaoqiang Li ◽  
Lu Wang ◽  
Pin Wu ◽  
Weiqin Tong ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Generative Adversarial Networks ◽  
Composite Image ◽  
Specific Instance ◽  
Adversarial Networks ◽  
Segmentation Methods ◽  
Multiple Category ◽  
Complete Category ◽  
Weakly Supervised ◽  
Instance Segmentation

An instance with a bad mask might make a composite image that uses it look fake. This encourages us to learn segmentation by generating realistic composite images. To achieve this, we propose a novel framework that exploits a new proposed prior called the independence prior based on Generative Adversarial Networks (GANs). The generator produces an image with multiple category-specific instance providers, a layout module and a composition module. Firstly, each provider independently outputs a category-specific instance image with a soft mask. Then the provided instances’ poses are corrected by the layout module. Lastly, the composition module combines these instances into a final image. Training with adversarial loss and penalty for mask area, each provider learns a mask that is as small as possible but enough to cover a complete category-specific instance. Weakly supervised semantic segmentation methods widely use grouping cues modeling the association between image parts, which are either artificially designed or learned with costly segmentation labels or only modeled on local pairs. Unlike them, our method automatically models the dependence between any parts and learns instance segmentation. We apply our framework in two cases: (1) Foreground segmentation on category-specific images with box-level annotation. (2) Unsupervised learning of instance appearances and masks with only one image of homogeneous object cluster (HOC). We get appealing results in both tasks, which shows the independence prior is useful for instance segmentation and it is possible to unsupervisedly learn instance masks with only one image.

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