scholarly journals Gain-Loss Evaluation-Based Generic Selection for Steganalysis Feature

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1775
Author(s):  
Ruixia Jin ◽  
Yihao Wang ◽  
Yuanyuan Ma ◽  
Tao Li ◽  
Xintao Duan
Keyword(s):  
Information Gain ◽  
Detection Accuracy ◽  
Distortion Factor ◽  
Temporal Complexity ◽  
Spatio Temporal ◽  
The Difference ◽  
Feature Dimension ◽  
Gain Loss ◽  
Information Gain Ratio ◽  
Asymmetric Distortion

Fewer contribution feature components in the image high-dimensional steganalysis feature are able to increase the spatio-temporal complexity of detecting the stego images, and even reduce the detection accuracy. In order to maintain or even improve the detection accuracy while effectively reducing the dimension of the DCTR steganalysis feature, this paper proposes a new selection approach for DCTR feature. First, the asymmetric distortion factor and information gain ratio of each feature component are improved to measure the difference between the symmetric cover and stego features, which provides the theoretical basis for selecting the feature components that contribute to a great degree to detecting the stego images. Additionally, the feature components are arranged in descending order rely on the two measurement criteria, which provides the basis for deleting the components. Based on the above, removing feature components that are ranked larger differently according to two criteria. Ultimately, the preserved feature components are used as the final selected feature for training and detection. Comparison experiments with existing classical approaches indicate that this approach can effectively reduce the feature dimension while maintaining or even improving the detection accuracy. At the same time, it can reduce the detection spatio-temporal complexity of the stego images.

scholarly journals Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

Insects ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 565
Author(s):  
Zhiliang Zhang ◽  
Wei Zhan ◽  
Zhangzhang He ◽  
Yafeng Zou
Keyword(s):  
Neural Network ◽  
Insect Behavior ◽  
Convolution Neural Network ◽  
Temporal Context ◽  
Detection Accuracy ◽  
Grooming Behavior ◽  
Behavior Detection ◽  
Detection Model ◽  
Spatio Temporal ◽  
The Difference

Statistical analysis and research on insect grooming behavior can find more effective methods for pest control. Traditional manual insect grooming behavior statistical methods are time-consuming, labor-intensive, and error-prone. Based on computer vision technology, this paper uses spatio-temporal context to extract video features, uses self-built Convolution Neural Network (CNN) to train the detection model, and proposes a simple and effective Bactrocera minax grooming behavior detection method, which automatically detects the grooming behaviors of the flies and analysis results by a computer program. Applying the method training detection model proposed in this paper, the videos of 22 adult flies with a total of 1320 min of grooming behavior were detected and analyzed, and the total detection accuracy was over 95%, the standard error of the accuracy of the behavior detection of each adult flies was less than 3%, and the difference was less than 15% when compared with the results of manual observation. The experimental results show that the method in this paper greatly reduces the time of manual observation and at the same time ensures the accuracy of insect behavior detection and analysis, which proposes a new informatization analysis method for the behavior statistics of Bactrocera minax and also provides a new idea for related insect behavior identification research.

scholarly journals Smoke recognition network based on dynamic characteristics

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092566
Author(s):  
Dahan Wang ◽  
Sheng Luo ◽  
Li Zhao ◽  
Xiaoming Pan ◽  
Muchou Wang ◽  
...  
Keyword(s):  
Dynamic Characteristics ◽  
State Of The Art ◽  
The State ◽  
Detection Accuracy ◽  
Static Characteristics ◽  
Good Tool ◽  
Early Signal ◽  
Fuzzy Objects ◽  
The Difference ◽  
Smoke Recognition

Fire is a fierce disaster, and smoke is the early signal of fire. Since such features as chrominance, texture, and shape of smoke are very special, a lot of methods based on these features have been developed. But these static characteristics vary widely, so there are some exceptions leading to low detection accuracy. On the other side, the motion of smoke is much more discriminating than the aforementioned features, so a time-domain neural network is proposed to extract its dynamic characteristics. This smoke recognition network has these advantages:(1) extract the spatiotemporal with the 3D filters which work on dynamic and static characteristics synchronously; (2) high accuracy, 87.31% samples being classified rightly, which is the state of the art even in a chaotic environments, and the fuzzy objects for other methods, such as haze, fog, and climbing cars, are distinguished distinctly; (3) high sensitiveness, smoke being detected averagely at the 23rd frame, which is also the state of the art, which is meaningful to alarm early fire as soon as possible; and (4) it is not been based on any hypothesis, which guarantee the method compatible. Finally, a new metric, the difference between the first frame in which smoke is detected and the first frame in which smoke happens, is proposed to compare the algorithms sensitivity in videos. The experiments confirm that the dynamic characteristics are more discriminating than the aforementioned static characteristics, and smoke recognition network is a good tool to extract compound feature.

scholarly journals Spectral Complexity of Hyperspectral Images: A New Approach for Mangrove Classification

2021 ◽  
Vol 13 (13) ◽  
pp. 2604
Author(s):  
Patrick Osei Darko ◽  
Margaret Kalacska ◽  
J. Pablo Arroyo-Mora ◽  
Matthew E. Fagan
Keyword(s):  
Statistical Physics ◽  
Near Infrared ◽  
Information Gain ◽  
Hyperspectral Imagery ◽  
Full Range ◽  
Surface Reflectance ◽  
Mangrove Species ◽  
New Approach ◽  
Spatio Temporal ◽  
Shortwave Infrared

Hyperspectral remote sensing across multiple spatio-temporal scales allows for mapping and monitoring mangrove habitats to support urgent conservation efforts. The use of hyperspectral imagery for assessing mangroves is less common than for terrestrial forest ecosystems. In this study, two well-known measures in statistical physics, Mean Information Gain (MIG) and Marginal Entropy (ME), have been adapted to high spatial resolution (2.5 m) full range (Visible-Shortwave-Infrared) airborne hyperspectral imagery. These two spectral complexity metrics describe the spatial heterogeneity and the aspatial heterogeneity of the reflectance. In this study, we compare MIG and ME with surface reflectance for mapping mangrove extent and species composition in the Sierpe mangroves in Costa Rica. The highest accuracy for separating mangroves from forest was achieved with visible-near infrared (VNIR) reflectance (98.8% overall accuracy), following by shortwave infrared (SWIR) MIG and ME (98%). Our results also show that MIG and ME can discriminate dominant mangrove species with higher accuracy than surface reflectance alone (e.g., MIG–VNIR = 93.6% vs. VNIR Reflectance = 89.7%).

scholarly journals Simplicity or complexity? Important aspects of high nature value grassland management in nature conservation

2021 ◽  
Author(s):  
Róbert Kun ◽  
Dániel Babai ◽  
András István Csathó ◽  
Csaba Vadász ◽  
Nikoletta Kálmán ◽  
...  
Keyword(s):  
Nature Conservation ◽  
Plant Diversity ◽  
Management Practices ◽  
Grassland Management ◽  
Plant Functional Types ◽  
Higher Plant ◽  
Temporal Complexity ◽  
Functional Types ◽  
Management Factors ◽  
Spatio Temporal

AbstractLocal, adaptive traditional grassland management systems have played a fundamental role in the creation, maintenance and conservation of high nature value (HNV) grasslands. The state of diverse HNV grasslands has deteriorated across Europe in conjunction with changes in various management factors, such as management type and management intensity. To conserve the species-rich vegetation of HNV grasslands and to avoid undesirable shifts in plant functional type dominance, it is important to explore the effects of management factors crucial for nature conservation and to adapt them to local circumstances. In our study, we focus on three of the main factors in the management of valuable meadow steppes in the Great Hungarian Plain region (Central Hungary). We studied management types (mowing, grazing and combined), different levels of herbage removal intensity (low, medium, high) and spatio-temporal complexity (low, medium and high) of grassland management. Altogether 172 plots (1 m × 1 m) were designated in 17 sites. Plant diversity indexes and plant functional types were calculated according to the presence and percentage cover of plant species in the plots. Regarding plant diversity and the dominance of plant functional types, herbage removal intensity and spatio-temporal complexity of management had, for the most part, stronger effects than the type of management. Higher spatio-temporal complexity of management resulted in higher plant diversity, while higher intensity of management led to significantly lower diversity. Proper application of type, intensity and spatio-temporal complexity of management practices (separately and in combination) proved to be determining factors in the long-term maintenance and conservation of diversity and species composition of HNV grasslands.

scholarly journals Object-oriented change detection based on weighted polarimetric scattering differences on POLSAR images

2015 ◽  
Vol XL-7/W4 ◽  
pp. 149-154 ◽  
Author(s):  
X. Shi ◽  
L. Lu ◽  
S. Yang ◽  
G. Huang ◽  
Z. Zhao
Keyword(s):  
Change Detection ◽  
Covariance Matrix ◽  
Polarization State ◽  
Object Oriented ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Processing Technologies ◽  
The Difference ◽  
Sar Imagery ◽  
Reasonable Choice

For wide application of change detection with SAR imagery, current processing technologies and methods are mostly based on pixels. It is difficult for pixel-based technologies to utilize spatial characteristics of images and topological relations of objects. Object-oriented technology takes objects as processing unit, which takes advantage of the shape and texture information of image. It can greatly improve the efficiency and reliability of change detection. Recently, with the development of polarimetric synthetic aperture radar (PolSAR), more backscattering features on different polarization state can be available for usage of object-oriented change detection study. In this paper, the object-oriented strategy will be employed. Considering the fact that the different target or target's state behaves different backscattering characteristics dependent on polarization state, an object-oriented change detection method that based on weighted polarimetric scattering difference of PolSAR images is proposed. The method operates on the objects generated by generalized statistical region merging (GSRM) segmentation processing. The merit of GSRM method is that image segmentation is executed on polarimetric coherence matrix, which takes full advantages of polarimetric backscattering features. And then, the measurement of polarimetric scattering difference is constructed by combining the correlation of covariance matrix and the difference of scattering power. Through analysing the effects of the covariance matrix correlation and the scattering echo power difference on the polarimetric scattering difference, the weighted method is used to balance the influences caused by the two parts, so that more reasonable weights can be chosen to decrease the false alarm rate. The effectiveness of the algorithm that proposed in this letter is tested by detection of the growth of crops with two different temporal radarsat-2 fully PolSAR data. First, objects are produced by GSRM algorithm based on the coherent matrix in the pre-processing. Then, the corresponding patches are extracted in two temporal images to measure the differences of objects. To detect changes of patches, a difference map is created by means of weighted polarization scattering difference. Finally, the result of change detection can be obtained by threshold determining. The experiments show that this approach is feasible and effective, and a reasonable choice of weights can improve the detection accuracy significantly.

scholarly journals Sensing as a Service for the Internet of Things

2016 ◽  
Vol 4 (3) ◽  
pp. 28
Author(s):  
Omar Subhi Aldabbas
Keyword(s):  
Internet Of Things ◽  
Cost Effective ◽  
Sensor Data ◽  
Detection Accuracy ◽  
Heterogeneous Services ◽  
Coordination Languages ◽  
Spatio Temporal ◽  
Temporal Events ◽  
New Applications ◽  
Common Application

Internet of Things (IoT) is a ubiquitous embedded ecosystem known for its capability to perform common application functions through coordinating resources, which are distributed on-object or on-network domains. As new applications evolve, the challenge is in the analysis and usage of multimodal data streamed by diverse kinds of sensors. This paper presents a new service-centric approach for data collection and retrieval. This approach considers objects as highly decentralized, composite and cost effective services. Such services can be constructed from objects located within close geographical proximity to retrieve spatio-temporal events from the gathered sensor data. To achieve this, we advocate Coordination languages and models to fuse multimodal, heterogeneous services through interfacing with every service to achieve the network objective according to the data they gather and analyze. In this paper we give an application scenario that illustrates the implementation of the coordination models to provision successful collaboration among IoT objects to retrieve information. The proposed solution reduced the communication delay before service composition by up to 43% and improved the target detection accuracy by up to 70%, while maintaining energy consumption 20% lower than its best rivals in the literature.

scholarly journals Ad hoc properties and locations in Maltese

2015 ◽  
Vol 1 ◽  
pp. 64-85 ◽  
Author(s):  
Gréte Dalmi
Keyword(s):  
Ad Hoc ◽  
Semantic Theory ◽  
Alternative States ◽  
Verbal Predicate ◽  
Spatio Temporal ◽  
The Difference ◽  
Copular Sentences ◽  
Event Variable ◽  
Temporal Variable ◽  
Account Variation

This paper aims to show that the four-way BE-system of Maltese can best be accommodated in a theory of non-verbal predication that builds on alternative states, without making any reference to the Davidsonian spatio-temporal event variable. The existing theories of non-verbal predicates put the burden of explaining the difference between the ad hoc vs. habitual interpretations either solely on the non-verbal predicate, by postulating an event variable in their lexical layer (see Kratzer 1995; Adger and Ramchand 2003; Magri 2009; Roy 2013), or solely on the copular or non-copular primary predicate, which contains an aspectual operator or an incorporated abstract preposition, responsible for such interpretive differences (Schmitt 2005, Schmitt and Miller 2007, Gallego and Uriagereka 2009, 2011, Marín 2010, Camacho 2012). The present proposal combines Maienborn’s (2003, 2005a,b, 2011) discourse-semantic theory of copular sentences with Richardson’s (2001, 2007) analysis of non-verbal adjunct predicates in Russian, based on alternative states. Under this combined account, variation between the ad hoc vs. habitual interpretations of non-verbal predicates is derived from the presence or absence of a modal OPalt operator that can bind the temporal variable of non-verbal predicates in accessible worlds, in the sense of Kratzer (1991). In the absence of this operator, the temporal variable is bound by the T0 head in the standard way. The proposal extends to non-verbal predicates in copular sentences as well as to argument and adjunct non-verbal predicates in non-copular sentences.

scholarly journals Modified S2CVA Algorithm Using Cross-Sharpened Images for Unsupervised Change Detection

2018 ◽  
Vol 10 (9) ◽  
pp. 3301 ◽  
Author(s):  
Honglyun Park ◽  
Jaewan Choi ◽  
Wanyong Park ◽  
Hyunchun Park
Keyword(s):  
Change Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Seasonal Effects ◽  
Detection Accuracy ◽  
Difference Image ◽  
Before And After ◽  
Change Detection Algorithms ◽  
The Difference ◽  
Direction Information

This study aims to reduce the false alarm rate due to relief displacement and seasonal effects of high-spatial-resolution multitemporal satellite images in change detection algorithms. Cross-sharpened images were used to increase the accuracy of unsupervised change detection results. A cross-sharpened image is defined as a combination of synthetically pan-sharpened images obtained from the pan-sharpening of multitemporal images (two panchromatic and two multispectral images) acquired before and after the change. A total of four cross-sharpened images were generated and used in combination for change detection. Sequential spectral change vector analysis (S2CVA), which comprises the magnitude and direction information of the difference image of the multitemporal images, was applied to minimize the false alarm rate using cross-sharpened images. Specifically, the direction information of S2CVA was used to minimize the false alarm rate when applying S2CVA algorithms to cross-sharpened images. We improved the change detection accuracy by integrating the magnitude and direction information obtained using S2CVA for the cross-sharpened images. In the experiment using KOMPSAT-2 satellite imagery, the false alarm rate of the change detection results decreased with the use of cross-sharpened images compared to that with the use of only the magnitude information from the original S2CVA.

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