scholarly journals Cloud and Snow Segmentation in Satellite Images Using an Encoder–Decoder Deep Convolutional Neural Networks

2021 ◽  
Vol 10 (7) ◽  
pp. 462
Author(s):  
Kai Zheng ◽  
Jiansheng Li ◽  
Lei Ding ◽  
Jianfeng Yang ◽  
Xucheng Zhang ◽  
...  
Keyword(s):  
Satellite Images ◽  
Segmentation Method ◽  
Deep Convolutional Neural Networks ◽  
Segmentation Accuracy ◽  
Decoder Architecture ◽  
Spatial Pyramid Pooling ◽  
The Relationship ◽  
Th 1 ◽  
Spatial Pyramid ◽  
Better Than

The segmentation of cloud and snow in satellite images is a key step for subsequent image analysis, interpretation, and other applications. In this paper, a cloud and snow segmentation method based on a deep convolutional neural network (DCNN) with enhanced encoder–decoder architecture—ED-CNN—is proposed. In this method, the atrous spatial pyramid pooling (ASPP) module is used to enhance the encoder, while the decoder is enhanced with the fusion of features from different stages of the encoder, which improves the segmentation accuracy. Comparative experiments show that the proposed method is superior to DeepLabV3+ with Xception and ResNet50. Additionally, a rough-labeled dataset containing 23,520 images and fine-labeled data consisting of 310 images from the TH-1 satellite are created, where we studied the relationship between the quality and quantity of labels and the performance of cloud and snow segmentation. Through experiments on the same network with different datasets, we found that the cloud and snow segmentation performance is related more closely to the quantity of labels rather than their quality. Namely, under the same labeling consumption, using rough-labeled images only performs better than rough-labeled images plus 10% fine-labeled images.

Multi-Channel Mapping Image Segmentation Method Based on LDA

2020 ◽  
pp. 2154012
Author(s):  
Qiu-Xia Hu ◽  
Jie Tian ◽  
Dong-Jian He
Keyword(s):  
Plant Disease ◽  
Experimental Result ◽  
Cluster Center ◽  
Segmentation Method ◽  
Linear Discriminant ◽  
Linear Discrimination ◽  
Initial Cluster ◽  
Segmentation Accuracy ◽  
Eigen Values ◽  
Better Than

In order to improve the segmentation accuracy of plant lesion images, multi-channels segmentation algorithm of plant disease image was proposed based on linear discriminant analysis (LDA) method’s mapping and K-means’ clustering. Firstly, six color channels from RGB model and HSV model were obtained, and six channels of all pixels were laid out to six columns. Then one of these channels was regarded as label and the others were regarded as sample features. These data were grouped for linear discrimination analysis, and the mapping values of the other five channels were applied to the eigen vector space according to the first three big eigen values. Secondly, the mapping value was used as the input data for K-means and the points with minimum and maximum pixel values were used as the initial cluster center, which overcame the randomness for selecting the initial cluster center in K-means. And the segmented pixels were changed into background and foreground, so that the proposed segmentation method became the clustering of two classes for background and foreground. Finally, the experimental result showed that the segmentation effect of the proposed LDA mapping-based method is better than those of K-means, ExR and CIVE methods.

scholarly journals A Spatial Pyramid Pooling-Based Deep Convolutional Neural Network for the Classification of Electrocardiogram Beats

2018 ◽  
Vol 8 (9) ◽  
pp. 1590 ◽  
Author(s):  
Jia Li ◽  
Yujuan Si ◽  
Liuqi Lang ◽  
Lixun Liu ◽  
Tao Xu
Keyword(s):  
Classification Accuracy ◽  
Massachusetts Institute Of Technology ◽  
Deep Convolutional Neural Networks ◽  
Beat Classification ◽  
Spatial Pyramid Pooling ◽  
Institute Of Technology ◽  
Fixed Input ◽  
Fully Connected ◽  
Spatial Pyramid

An accurate electrocardiogram (ECG) beat classification can benefit the diagnosis of the cardiovascular disease. Deep convolutional neural networks (CNN) can automatically extract valid features from data, which is an effective way for the classification of the ECG beats. However, the fully-connected layer in CNNs requires a fixed input dimension, which limits the CNNs to receive fixed-scale inputs. Signals of different scales are generally processed into the same size by segmentation and downsampling. If information loss occurs during a uniformly-sized process, the classification accuracy will ultimately be affected. To solve this problem, this paper constructs a new CNN framework spatial pyramid pooling (SPP) method, which solves the deficiency caused by the size of input data. The Massachusetts Institute of Technology-Biotechnology (MIT-BIH) arrhythmia database is employed as the training and testing data for the classification of heartbeat signals into six categories. Compared with the traditional method, which may lose a large amount of important information and easy to be over-fitted, the robustness of the proposed method can be guaranteed by extracting data features from different sizes. Experimental results show that the proposed architecture network can extract more high-quality features and exhibits higher classification accuracy (94%) than the traditional deep CNNs (90.4%).

scholarly journals Spatial Pyramid Pooling in Deep Convolutional Networks for Automatic Tuberculosis Diagnosis

2020 ◽  
Vol 37 (6) ◽  
pp. 1075-1084
Author(s):  
Pike Msonda ◽  
Sait Ali Uymaz ◽  
Seda Soğukpınar Karaağaç
Keyword(s):  
Neural Networks ◽  
Developing Countries ◽  
Convolutional Neural Networks ◽  
Automated System ◽  
Machine Learning Techniques ◽  
World Health ◽  
Deep Convolutional Neural Networks ◽  
Automatic Diagnosis ◽  
Spatial Pyramid Pooling ◽  
Spatial Pyramid

In recent decades, automatic diagnosis using machine-learning techniques have been the focus of research. Mycobacterium Tuberculosis (TB) is a deadly disease that has plagued most developing countries presents a problem that can be tackled by automatic diagnosis. The World Health Organization (WHO) set years 2030 and 2035 as milestones for a significant reduction in new infections and deaths although lack of well-trained professionals and insufficient or fragile public health systems (in developing countries) are just some of the major factors that have slowed the eradication of the TB endemic. Deep convolutional neural networks (DCNNs) have demonstrated remarkable results across problem domains dealing with grid-like data (i.e., images and videos). Traditionally, a methodology for detecting TB is through radiology combined with previous success DCNN have achieved in image classification makes them the perfect candidate to classify Chest X-Ray (CXR) images. In this study, we propose three types of DCNN trained using two public datasets and another new set which we collected from Konya Education and Research Hospital, Konya, Turkey. Also, the DCNN architectures were integrated with an extra layer called Spatial Pyramid Pooling (SPP) a methodology that equips convolutional neural networks with the ability for robust feature pooling by using spatial bins. The result indicates the potential for an automated system to diagnose tuberculosis with accuracies above a radiologist professional.

scholarly journals Bridge Crack Detection Based on SSENets

2020 ◽  
Vol 10 (12) ◽  
pp. 4230 ◽  
Author(s):  
Haotian Li ◽  
Hongyan Xu ◽  
Xiaodong Tian ◽  
Yi Wang ◽  
Huaiyu Cai ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computational Complexity ◽  
Crack Detection ◽  
Contextual Information ◽  
Detection Accuracy ◽  
Multi Scale ◽  
Surrounding Environment ◽  
Spatial Pyramid Pooling ◽  
Spatial Pyramid ◽  
Better Than

Bridge crack detection is essential to prevent transportation accidents. However, the surrounding environment has great interference with the detection of cracks, which makes it difficult to ensure the accuracy of the detection. In order to accurately detect bridge cracks, we proposed an end-to-end model named Skip-Squeeze-and-Excitation Networks (SSENets). It is mainly composed of the Skip-Squeeze-Excitation (SSE) module and the Atrous Spatial Pyramid Pooling (ASPP) module. The SSE module uses skip-connection strategy to enhance the gradient correlation between the shallow network and deeper network, alleviating the vanishing gradient caused by the deepening of the network. The ASPP module can extract multi-scale contextual information of images, while the depthwise separable convolution reduces computational complexity. In order to avoid destroying the topology of crack, we used atrous convolution instead of the pooling layer. The proposed SSENets achieved a detection accuracy of 97.77%, which performed better than the models we compared it with. The designed SSE module which used skip-connection strategy can be embedded in other convolutional neural networks (CNNs) to improve their performance.

scholarly journals Tomato detection based on modified YOLOv3 framework

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mubashiru Olarewaju Lawal
Keyword(s):  
Environmental Conditions ◽  
State Of The Art ◽  
Detection Time ◽  
Illumination Variation ◽  
Vital Part ◽  
Spatial Pyramid Pooling ◽  
Robotic Harvesting ◽  
Spatial Pyramid ◽  
Better Than ◽  
Tomato Detection

AbstractFruit detection forms a vital part of the robotic harvesting platform. However, uneven environment conditions, such as branch and leaf occlusion, illumination variation, clusters of tomatoes, shading, and so on, have made fruit detection very challenging. In order to solve these problems, a modified YOLOv3 model called YOLO-Tomato models were adopted to detect tomatoes in complex environmental conditions. With the application of label what you see approach, densely architecture incorporation, spatial pyramid pooling and Mish function activation to the modified YOLOv3 model, the YOLO-Tomato models: YOLO-Tomato-A at AP 98.3% with detection time 48 ms, YOLO-Tomato-B at AP 99.3% with detection time 44 ms, and YOLO-Tomato-C at AP 99.5% with detection time 52 ms, performed better than other state-of-the-art methods.

Segmentation In Medical Resonance images to extract the cancerous nodule for early diagnosis on cancer

2012 ◽  
Vol 3 (2) ◽  
pp. 253-255
Author(s):  
Raman Brar
Keyword(s):  
Human Lung ◽  
Research Work ◽  
Vital Role ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Background Segmentation ◽  
Lung Mri

Image segmentation plays a vital role in several medical imaging programs by assisting the delineation of physiological structures along with other parts. The objective of this research work is to segmentize human lung MRI (Medical resonance Imaging) images for early detection of cancer.Watershed Transform Technique is implemented as the Segmentation method in this work. Some comparative experiments using both directly applied watershed algorithm and after marking foreground and computed background segmentation methods show the improved lung segmentation accuracy in some image cases.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

scholarly journals Advertising Media Impact in Consumer Buying Behavior

2019 ◽  
Vol 8 ◽  
pp. 54-56
Author(s):  
Ashmita Dahal Chhetri
Keyword(s):  
Consumer Behavior ◽  
Young Male ◽  
Electronic Media ◽  
Buying Behavior ◽  
Media Impact ◽  
High Degree ◽  
The Impact ◽  
Buying Behaviors ◽  
The Relationship ◽  
Better Than

Advertisements have been used for many years to influence the buying behaviors of the consumers. Advertisements are helpful in creating the awareness and perception among the customers of a product. This particular research was conducted on the 100 young male and female who use different brands of product to check the influence of advertisement on their buying behavior while creating the awareness and building the perceptions. Correlation, regression and other statistical tools were used to identify the relationship between these variables. The results revealed that the relationship between media and consumer behavior is positive. The adve1tising impact on sales and there is positive and high degree relationship between advertising and consumer behavior. The impact on advertising of a product of electronic media is better than non-electronic media.

scholarly journals Comparative Study on Relative Fossil Energy Carrying Capacity in China and the United States

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2972
Author(s):  
Zhili Zuo ◽  
Jinhua Cheng ◽  
Haixiang Guo ◽  
Yonglin Li
Keyword(s):  
United States ◽  
Carrying Capacity ◽  
The United States ◽  
Reference Area ◽  
Energy Sustainability ◽  
Fossil Energy ◽  
The Relationship ◽  
Population And Environment ◽  
Better Than ◽  
Innovative Concept

Based on resource carrying capacity, this study used the revised theory of relative resource carrying capacity (RRCC) and introduced an innovative concept of relative fossil energy carrying capacity (RFECC), which evaluates the degree of fossil energy sustainability based on the relationship between economy, population, and environment. This study took China and the United States as the study objects, took the whole country as the reference area, and calculated the RFECC of population, economic, and environmental resources from 2000 to 2018. Therefore, based on the comparative analysis, the following conclusions were drawn: (i) there is a big difference in the RFECC between China and the United States, which is manifested in the inverted U-shaped trend in China and the U-shaped trend in the United States; (ii) the relative fossil energy carrying states in China and the United States are different, mainly reflected in the economy and environment; (iii) the gap in RFECC between China and the United States has gradually widened; in general, China’s economic RFECC is better than that of the United States, while environmental RFECC and population RFECC in the United States is better than that of China; and (iv) coal and oil should be used as a breakthrough point for the sustainable fossil energy and sustainable development for China and the United States, respectively.

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