Subpixel Change Detection Based on Radial Basis Function with Abundance Image Difference Measure for Remote Sensing Images

Recently, land cover change detection has become a research focus of remote sensing. To obtain the change information from remote sensing images at fine spatial and temporal resolutions, subpixel change detection is widely studied and applied. In this paper, a new subpixel change detection method based on radial basis function (RBF) for remote sensing images is proposed, in which the abundance image difference measure (AIDM) is designed and utilized to enhance the subpixel mapping (SPM) by borrowing the fine spatial distribution of the fine spatial resolution image to decrease the influence of the spectral unmixing error. First, the fine and coarse spatial resolution images are used to develop subpixel change detection. Second, linear spectral mixing modeling and the degradation procedure are conducted on the coarse and fine spatial resolution image to produce two temporal abundance images, respectively. Then, the designed AIDM is utilized to enhance the RBF-based SPM by comparing the two temporal abundance images. At last, the proposed RBF-AIDM method is applied for SPM and subpixel change detection. The synthetic images based on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and real case images based on two temporal Landsat-8 Operational Land Imager (OLI) images and one Moderate Resolution Imaging Spectroradiometer (MODIS) image are undertaken to validate the proposed method. The experimental results indicate that the proposed method can sufficiently decrease the influence of the spectral unmixing error and improve the subpixel change detection results.

An assessment of the algorithm difference measure in a multivariate system for production plans scheduling

In the process of designing a production plan, one of the important steps is scheduling the execution of technological operations. The schedule can be created either manually or by using software. If the schedule is compiled by software, then several schedule generation algorithms are used to eliminate possible errors. A set of such algorithms is called a "batch". It is advisable that only different algorithms should be included in the batch. This is necessary to eliminate errors of the same type. Therefore, the search for clones of algorithms in the batch is an urgent production task. To solve it a diversity metric of algorithms was developed in the course of this work. Such a metric numerically (as a percentage) determines how much the algorithms differ. This metric is based on the properties of the algorithm execution. Algorithm traces are constructed in the N-dimensional space using the obtained points. The coordinates of the trace points are the values with which the algorithm works at each step of its execution or each of the control points of the algorithm execution. An experiment was performed to confirm the correctness of this metric. Within this experiment, the trace properties of three sorting algorithms were calculated. Based on the properties obtained, indicators were determined for comparing algorithms in the metric space. The experiment confirmed the effectiveness of using the diversity metric to find clones in the algorithms batch. The scope of this metric is not limited to clone searches. It can be used as an independent indicator of software quality.

Comparing and Clustering Residential Layouts Using a Novel Measure of Grating Difference

Clustering is widely used as a knowledge discovery method in scientific studies but is not often used in architectural research. This paper applies clustering to a dataset of 129 residential layouts, which were collected from contemporary architectural practices, to reveal underlying design patterns. To achieve this, this paper introduces a novel measure for the topological properties of layouts: ‘grating difference measure’. It was benchmarked against an alternative that measures geometrical properties and the advantages are explained. The grating difference measure indicates the extent of design differences, which is used in the clustering method to obtain the distance between datapoints. The results from clustering were grouped into design schematics and qualitatively assessed, showing a convincing separation of characteristics. The method demonstrated in this paper may be used to reveal topological patterns in datasets of existing designs for both academic and practical purposes.

Semantic Segmentation Algorithm Based on Attention Mechanism and Transfer Learning

In this paper, we propose a semantic segmentation algorithm (RoadNet) for auxiliary edge detection tasks with an attention mechanism. RoadNet improves the dispersion of the low-level features of the network model and further enhances the performance and applicability of the semantic segmentation algorithm. In RoadNet, a fully convolutional neural network is used as the basic model, an auxiliary loss in the image classification, multitask learning in machine learning, and attention mechanism in natural language processing. To improve the generalization of the model, we select and analyze a proper domain difference measure. Subsequently, the context semantic distribution module and the annotation distribution loss are designed based on the context semantic encoding structure. The domain discriminator based on the adversarial training and the adversarial training algorithm based on transfer learning are then well integrated to provide a transfer learning-based semantic segmentation algorithm (TransRoadNet). The experimental results indicate that the proposed TransRoadNet and RoadNet overperform their equivalent comparison models.

A Study of Feature Reduction Techniques and Classification for Network Anomaly Detection

Due to the launch of new applications the behavior of internet traffic is changing. Hackers are always looking for sophisticated tools to launch attacks and damage the services. Researchers have been working on intrusion detection techniques involving machine learning algorithms for supervised and unsupervised detection of these attacks. However, with newly found attacks these techniques need to be refined. Handling data with large number of attributes adds to the problem. Therefore, dimensionality based feature reduction of the data is required. In this work three reduction techniques, namely, Principal Component Analysis (PCA), Artificial Neural Network (ANN), and Nonlinear Principal Component Analysis (NLPCA) have been studied and analyzed. Secondly, performance of four classifiers, namely, Decision Tree (DT), Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Naïve Bayes (NB) has been studied for the actual and reduced datasets. In addition, novel performance measurement metrics, Classification Difference Measure (CDM), Specificity Difference Measure (SPDM), Sensitivity Difference Measure (SNDM), and F1 Difference Measure (F1DM) have been defined and used to compare the outcomes on actual and reduced datasets. Comparisons have been done using new Coburg Intrusion Detection Data Set (CIDDS-2017) dataset as well widely referred NSL-KDD dataset. Successful results were achieved for Decision Tree with 99.0 percent and 99.8 percent accuracy on CIDDS and NSLKDD datasets respectively.

Approximate Solutions of Nonlinear Smoking Habit Model

The work in this paper focuses on solving numerically and analytically a nonlinear social epidemic model that represents an initial value problem of ordinary differential equations. A recent moking habit model from Spain is applied and studied here. The accuracy and convergence of the numerical and approximation results are investigated for various methods; for example, Adomian decomposition, variation iteration, Finite difference and Runge-Kutta. The discussion of the present results has been tabulated and graphed. Finally, the comparison between the analytic and numerical solutions from the period 2006-2009 has been obtained by absolute and difference measure error.