scholarly journals Superpixel-Based Attention Graph Neural Network for Semantic Segmentation in Aerial Images

2022 ◽  
Vol 14 (2) ◽  
pp. 305
Author(s):  
Qi Diao ◽  
Yaping Dai ◽  
Ce Zhang ◽  
Yan Wu ◽  
Xiaoxue Feng ◽  
...  
Keyword(s):  
Neural Network ◽  
Spatial Resolution ◽  
Nearest Neighbor ◽  
High Spatial Resolution ◽  
Semantic Segmentation ◽  
Attention Mechanism ◽  
Aerial Images ◽  
K Nearest Neighbor ◽  
Object Boundary ◽  
Semantic Class

Semantic segmentation is one of the significant tasks in understanding aerial images with high spatial resolution. Recently, Graph Neural Network (GNN) and attention mechanism have achieved excellent performance in semantic segmentation tasks in general images and been applied to aerial images. In this paper, we propose a novel Superpixel-based Attention Graph Neural Network (SAGNN) for semantic segmentation of high spatial resolution aerial images. A K-Nearest Neighbor (KNN) graph is constructed from our network for each image, where each node corresponds to a superpixel in the image and is associated with a hidden representation vector. On this basis, the initialization of the hidden representation vector is the appearance feature extracted by a unary Convolutional Neural Network (CNN) from the image. Moreover, relying on the attention mechanism and recursive functions, each node can update its hidden representation according to the current state and the incoming information from its neighbors. The final representation of each node is used to predict the semantic class of each superpixel. The attention mechanism enables graph nodes to differentially aggregate neighbor information, which can extract higher-quality features. Furthermore, the superpixels not only save computational resources, but also maintain object boundary to achieve more accurate predictions. The accuracy of our model on the Potsdam and Vaihingen public datasets exceeds all benchmark approaches, reaching 90.23% and 89.32%, respectively.

scholarly journals Comparison of supervised classifiers in the discrimin ation of preservation areas in a hydroelectric reservoir

2019 ◽  
Vol 11 (3) ◽  
Author(s):  
Jefferson Francisco Soares ◽  
Gláucia Miranda Ramirez ◽  
Mirléia Aparecida de Carvalho ◽  
Marcelo de Carvalho Alves ◽  
Christiany Mattioli Sarmiento ◽  
...  
Keyword(s):  
Land Use ◽  
Spatial Resolution ◽  
Nearest Neighbor ◽  
High Spatial Resolution ◽  
Object Oriented ◽  
Visual Object ◽  
Hydroelectric Power ◽  
K Nearest Neighbor ◽  
High Spatial Resolution Image ◽  
Resolution Image

The maintenance of riparian forests is considered one of the main vegetative practices for mitigating the degradation of water resources and is mandatory by law. However, in Brazil there is still a progressive and constant decharacterization of these areas. Facing this reality, it is necessary to broaden researches that identify the occurring changes and provide efficient solutions at a fast pace and low cost. Remote sensing techniques show great application potential in characterizing natural resources. The objective of this work was to map, to characterize the land use and occupation and to verify the best method of high spatial resolution image classification of the Permanent Preservation Areas of the Funil Hydroelectric Power Plant reservoir, located between the municipalities of Lavras, Perdões, Bom Sucesso, Ibituruna, Ijací and Itumirim, in the state of Minas Gerais. The methods used to classify the high spatial resolution image from the Quickbird satellite were visual, object-oriented and pixel-by-pixel. Results showed the best method for mapping land use and occupation of the study area was object-oriented classification using the K-nearest neighbor algorithm, with kappa coefficient of 0.88 and global accuracy of 91.40%.

scholarly journals IDENTIFYING OIL PADS IN HIGH SPATIAL RESOLUTION AERIAL IMAGES USING FASTER R-CNN

2021 ◽  
Vol XLIV-M-3-2021 ◽  
pp. 155-161
Author(s):  
A. Sunil ◽  
V. V. Sajithvariyar ◽  
V. Sowmya ◽  
R. Sivanpillai ◽  
K. P. Soman
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Independent Set ◽  
Object Identification ◽  
Optimal Number ◽  
Aerial Images ◽  
Recognition Method ◽  
True Color

Abstract. Deep learning (DL) methods are used for identifying objects in aerial and ground-based images. Detecting vehicles, roads, buildings, and crops are examples of object identification applications using DL methods. Identifying complex natural and man-made features continues to be a challenge. Oil pads are an example of complex built features due to their shape, size, and presence of other structures like sheds. This work applies Faster Region-based Convolutional Neural Network (R-CNN), a DL-based object recognition method, for identifying oil pads in high spatial resolution (1m), true-color aerial images. Faster R-CNN is a region-based object identification method, consisting of Regional Proposal Network (RPN) that helps to find the area where the target can be possibly present in the images. If the target is present in the images, the Faster R-CNN algorithm will identify the area in an image as foreground and the rest as background. The algorithm was trained with oil pad locations that were manually annotated from orthorectified imagery acquired in 2017. Eighty percent of the annotated images were used for training and the number of epochs was increased from 100 to 1000 in increments of 100 with a fixed length of 1000. After determining the optimal number of epochs the performance of the algorithm was evaluated with an independent set of validation images consisting of frames with and without oil pads. Results indicate that the Faster R-CNN algorithm can be used for identifying oil pads in aerial images.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

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.

scholarly journals Classification of Very-High-Spatial-Resolution Aerial Images Based on Multiscale Features with Limited Semantic Information

2021 ◽  
Vol 13 (3) ◽  
pp. 364
Author(s):  
Han Gao ◽  
Jinhui Guo ◽  
Peng Guo ◽  
Xiuwan Chen
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Spatial Resolution ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Training Data ◽  
Aerial Images ◽  
Rural Landscapes ◽  
Feature Representations ◽  
Object Based

Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha).

scholarly journals k-Nearest Neighbor Learning with Graph Neural Networks

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 830
Author(s):  
Seokho Kang
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Weighting Function ◽  
High Sensitivity ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Main Challenge ◽  
Benchmark Datasets ◽  
Graph Neural Networks

k-nearest neighbor (kNN) is a widely used learning algorithm for supervised learning tasks. In practice, the main challenge when using kNN is its high sensitivity to its hyperparameter setting, including the number of nearest neighbors k, the distance function, and the weighting function. To improve the robustness to hyperparameters, this study presents a novel kNN learning method based on a graph neural network, named kNNGNN. Given training data, the method learns a task-specific kNN rule in an end-to-end fashion by means of a graph neural network that takes the kNN graph of an instance to predict the label of the instance. The distance and weighting functions are implicitly embedded within the graph neural network. For a query instance, the prediction is obtained by performing a kNN search from the training data to create a kNN graph and passing it through the graph neural network. The effectiveness of the proposed method is demonstrated using various benchmark datasets for classification and regression tasks.

scholarly journals Deriving VIIRS High-Spatial Resolution Water Property Data over Coastal and Inland Waters Using Deep Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1944
Author(s):  
Xiaoming Liu ◽  
Menghua Wang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Ocean Color ◽  
Super Resolution ◽  
Inland Waters ◽  
Coastal Oceans ◽  
Chl A ◽  
Color Data

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been a reliable source of ocean color data products, including five moderate (M) bands and one imagery (I) band normalized water-leaving radiance spectra nLw(λ). The spatial resolutions of the M-band and I-band nLw(λ) are 750 m and 375 m, respectively. With the technique of convolutional neural network (CNN), the M-band nLw(λ) imagery can be super-resolved from 750 m to 375 m spatial resolution by leveraging the high spatial resolution features of I1-band nLw(λ) data. However, it is also important to enhance the spatial resolution of VIIRS-derived chlorophyll-a (Chl-a) concentration and the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), as well as other biological and biogeochemical products. In this study, we describe our effort to derive high-resolution Kd(490) and Chl-a data based on super-resolved nLw(λ) images at the VIIRS five M-bands. To improve the network performance over extremely turbid coastal oceans and inland waters, the networks are retrained with a training dataset including ocean color data from the Bohai Sea, Baltic Sea, and La Plata River Estuary, covering water types from clear open oceans to moderately turbid and highly turbid waters. The evaluation results show that the super-resolved Kd(490) image is much sharper than the original one, and has more detailed fine spatial structures. A similar enhancement of finer structures is also found in the super-resolved Chl-a images. Chl-a filaments are much sharper and thinner in the super-resolved image, and some of the very fine spatial features that are not shown in the original images appear in the super-resolved Chl-a imageries. The networks are also applied to four other coastal and inland water regions. The results show that super-resolution occurs mainly on pixels of Chl-a and Kd(490) features, especially on the feature edges and locations with a large spatial gradient. The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd(490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images.

scholarly journals KVGCN: A KNN Searching and VLAD Combined Graph Convolutional Network for Point Cloud Segmentation

2021 ◽  
Vol 13 (5) ◽  
pp. 1003
Author(s):  
Nan Luo ◽  
Hongquan Yu ◽  
Zhenfeng Huo ◽  
Jinhui Liu ◽  
Quan Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Semantic Segmentation ◽  
K Nearest Neighbor ◽  
Topological Graph ◽  
Convolutional Network ◽  
Cloud Data ◽  
Nearest Neighbor Searching ◽  
Point Cloud Segmentation ◽  
Local Feature Extraction

Semantic segmentation of the sensed point cloud data plays a significant role in scene understanding and reconstruction, robot navigation, etc. This work presents a Graph Convolutional Network integrating K-Nearest Neighbor searching (KNN) and Vector of Locally Aggregated Descriptors (VLAD). KNN searching is utilized to construct the topological graph of each point and its neighbors. Then, we perform convolution on the edges of constructed graph to extract representative local features by multiple Multilayer Perceptions (MLPs). Afterwards, a trainable VLAD layer, NetVLAD, is embedded in the feature encoder to aggregate the local and global contextual features. The designed feature encoder is repeated for multiple times, and the extracted features are concatenated in a jump-connection style to strengthen the distinctiveness of features and thereby improve the segmentation. Experimental results on two datasets show that the proposed work settles the shortcoming of insufficient local feature extraction and promotes the accuracy (mIoU 60.9% and oAcc 87.4% for S3DIS) of semantic segmentation comparing to existing models.

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