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International Workshop on the Analysis of Multi-temporal Remote Sensing Images (MultiTemp)

作者： Xin Wang Peijun Du Sicong Liu Yaping Meng Cong Lin School of Geography and Ocean Science Nanjing University Nanjing China College of Surveying and Geoinformatics Tongji University Shanghai China

VHR remote sensing image change detection with pixel-based method often results in some problems that have negative effects on accuracy, such as the salt-and-pepper noise. In order to achieve a better result under this circumstance, an unsupervised sequential strategy combining Morphological Profiles and automated training sample extraction is introduced. change detection with two real multi-temporal VHR datasets were carried out to test the effectiveness of the proposed approach. The experimental results showed that this approach outperformed the traditional unsupervised change detection methods in terms of accuracy and visual effect.

关键词： Training Remote sensing Feature extraction change detection algorithms Support vector machines Spatial resolution Image segmentation

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Nonparametric change detection in 2D random sensor field

Nonparametric change detection in 2D random sensor field

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ting He S. Ben-David Lang Tong School of Electrical and Computer Engineering Cornell University Ithaca USA School of Computer Science University of Waterloo Waterloo Canada

The problem of detecting changes from data collected from a large-scale randomly deployed 2D sensor field is considered. Under a nonparametric change detection framework, we propose detection algorithms using two measures of change. The theoretical performance guarantee is derived from the Vapnik-Chervonenkis theory. By exploiting the structures of the search domain, we design a suboptimal recursive algorithm to detect the area of largest change which, for M sample points, runs in time O(M/sup 2/logM) (compared to an O(M/sup 4/) required for a straightforward exhaustive search). The lost of performance diminishes as M increases.

关键词： Chemical sensors Sensor phenomena and characterization change detection algorithms Testing Helium Large-scale systems Sensor fusion Government detection algorithms Algorithm design and analysis

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A Density-Based Clustering Approach for Behavior change detection in Data Streams

A Density-Based Clustering Approach for Behavior Change Dete...

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Brazilian Symposium on Neural Networks

作者： Rosane M.M. Vallim José A. Andrade Filho André C.P.L.F. de Carvalho João Gama ICMC - USP São Carlos SP Brasil Universidade de Sao Paulo Sao Paulo SÃ£o Paulo BR LIAAD-INESC Porto University of Porta Portugal

Mining data streams poses many challenges to existing Machine Learning algorithms. algorithms designed to learn in this scenario need to constantly update their decision models in accordance with current data behavior. Therefore, the ability to detect when the behavior of the stream is changing is an important feature of any learning technique approaching data streams. This work is concerned with unsupervised behavior change detection. It suggests the use of density-based clustering and an entropy measurement for change detection that is independent of the number and format of clusters. The proposed approach uses a modified version of the Den Stream algorithm that is designed to better cope with the entropy calculation. Experimental results using synthetic data provide insight on how clustering and novelty detection algorithms can be used for change detection in data streams.

关键词： Entropy Clustering algorithms Data models Algorithm design and analysis change detection algorithms Delay Neural networks

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Adaptive GOP Size Control in AVS-P2 Based on Scene change detection

Adaptive GOP Size Control in AVS-P2 Based on Scene Change De...

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International Conference on Convergence Information Technology

作者： Z. Y. Wu H. Y. Yu B. Tang University of Electronic Science and Technology China

The coding efficiency of AVS-P2 for the video sequences, which have many scene changes, is generally low. If the scene changes are taken into consider for video compression, the encoding performance can be improved. An adaptive group of picture (GOP) size structure based on scene change is proposed here in AVS-P2 encoding. With a little additional computation, the proposed algorithm has improved the PSNR by 0.15~2.2 dB in the video sequences with scene changes.

关键词： Programmable control Adaptive control Size control Layout Encoding Video coding Video sequences Bit rate change detection algorithms Information technology

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Improved Fast-ICA for change detection of Multi Temporal Remote Sensing Images

Improved Fast-ICA for Change Detection of Multi Temporal Rem...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Jingyu Yang Zhengdong Ma Jianwu Dang Linjing Wei Yangping Wang Lanzhou Jiaotong University Lanzhou Gansu CN Gansu Agricultural University Lanzhou Gansu CN

When the selection of the initial points in the Fast-Independent Component Analysis (Fast-ICA) algorithm is far from the minimum point, the problem of convergence may not be convergent, and an improved Fast-ICA algorithm based on the damped Newton method is proposed. The algorithm adds one dimension search along the direction of Newton iteration, thus ensuring the convergence of Fast-ICA algorithm. Secondly, the Contourlet transform is combined with the improved Fast-ICA. When using the combined method for change detection, the initial transformation of remote sensing image is first used by Contourlet transform; secondly, the modified Fast-ICA algorithm is used to detect the change; then the image after the change detection is invert with Contourlet; finally, the image is segmented by threshold value, and the change results are obtained. The experimental results show that, compared with the change detection results based on the traditional Fast-ICA algorithm, the proposed method can effectively separate the information of the change, and the detection precision is higher.

关键词： Remote sensing change detection algorithms Entropy Transforms Convergence Jacobian matrices Newton method

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Improving change detection using Vertical Surface Normal Histograms and Gaussian Mixture Models in structured environments

Improving change detection using Vertical Surface Normal His...

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International Conference on Advanced Robotics (ICAR)

作者： P. Drews L.J. Manso S.C. da Silva Filho P. Núñez Robotics and Artificial Vision Laboratory (RoboLab) University of Extremadura Intelligent Robotics and Automation Group (NAUTEC) Computational Sciences Center Universidade Federal do Rio Grande Brazil

The interest in change detection techniques for autonomous robots has increased considerably during recent years. This is partly due to the fact that changes in robot's working environments are relevant for most tasks and robotics applications. changes or Novelties are usually detected by comparing the current data acquired by the robot with its previous knowledge (a map or any other model of its surroundings). Gaussian Mixture Models (GMM) have been satisfactorily used for detecting changes in 3D point clouds. However, these methods have drawbacks such as a long computational times and strong dependence on the parameters of the algorithms. In structured environments like offices or homes, it is possible to reduce the number of points to be processed by filtering unlikely-to-change regions of the scene. This paper introduces the concept of Vertical Surface Normal Histogram (VSNH). VSNH provides a method for removing from the point clouds acquired those points associated to the main planes: ceiling, walls and floor. Removing these points decreases the size of the problem and improves the segmentation of the environment into Gaussian Mixture Models. The experimental results demonstrate that the proposed method based on GMM and VSNH achieves change detection in structured indoor environments faster and more accurately than previous approaches.

关键词： Vectors Histograms Robots Three-dimensional displays Measurement change detection algorithms Gaussian mixture model

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Deep Transformer-Based Network Deforestation detection in the Brazilian Amazon Using Sentinel-2 Imagery

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2024年 21卷 1页

作者： Alshehri, Mariam Ouadou, Anes Scott, Grant J. Univ Missouri Dept Elect Engn & Comp Sci Columbia MO 65211 USA Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Riyadh 84428 Saudi Arabia

Deforestation poses a critical environmental challenge with far-reaching impacts on climate change, biodiversity, and local communities. As such, detecting and monitoring deforestation are crucial, and recent advancements in deep learning (DL) and remote sensing technologies offer a promising solution to this challenge. In this study, we adapt changeFormer, a transformer-based framework, to detect deforestation in the Brazilian Amazon, employing the attention mechanism to analyze spatial and temporal patterns in bitemporal satellite images. To assess the model's effectiveness, we employed a robust approach to create a deforestation detection (DD) dataset, utilizing Sentinel-2 imagery from select conservation areas in the Brazilian Amazon throughout 2020 and 2021. Our dataset comprises 7734 pairs of bitemporal image chips with a resolution of 256 x 256 pixels and 1406 pairs of image chips with a resolution of 512 x 512 pixels. The model achieved an overall accuracy (OA) of 93% with a corresponding F1 score of 90% and an intersection over union (IoU) score of 82%. These results demonstrate the potential of transformer-based networks for accurate and efficient DD.

关键词： Climate change Environmental monitoring Deforestation Forestry change detection algorithms Deep learning Transformers Biodiversity detection algorithms Spatiotemporal phenomena Satellite images South America change detection (CD) deep learning (DL) deforestation transformer

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A statistical feedforward feedback buffer control for the transmission of digital video signals compressed by DCT-based intrafield coding

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IEEE TRANSACTIONS ON IMAGE PROCESSING 1996年第3期5卷 527-529页

作者： Jo, SK Lee, YH Department of Electrical Engineering Korea Advanced Institute of Science and Technology Taejon South Korea

A new buffer-control policy for intrafield coding of video signals is presented. This method employs two statistical bit rate predictors-feedback and feedforward. It uses a feedback predictor in stationary portions of an image sequence and a feedforward predictor at scene changes, It is shown that this buffer-control policy is reasonably simple to implement and can effectively control output bit rate even at scene changes.

关键词： Bit rate Image coding Layout Video compression Output feedback Quantization change detection algorithms Discrete cosine transforms Partitioning algorithms Image sequences

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SYSTEMATIC-APPROACH FOR CLASSIFICATION OF MODERN SURVEILLANCE RADAR SYSTEMS

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1987年第2期23卷 281-285页

作者： RAFAEL, IR GERTNER, I TECHNION ISRAEL INST TECHNOL DEPT ELECT ENGNIL-32000 HAIFAISRAEL

Inherent search volume is proposed as a tool for systematic comparison of modern phased-array surveillance radar systems. The advantage of the inherent search volume over the existing approaches based on single-scan and cumulative probability of detection is that the inherent search volume depends only on the unadjustable parameters of the radar. A comparison algorithm based on the inherent search volume criterion is presented.

关键词： Surveillance Radar cross section Radar detection Probability Doppler radar Radar antennas Radar signal processing change detection algorithms Signal processing algorithms Character generation

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A Statistical Framework for Near-Real Time detection of Beetle Infestation in Pine Forests Using MODIS Data

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2014年第10期11卷 1717-1721页

作者： Anees, A. Aryal, J. Univ Tasmania Sch Engn Hobart Tas 7001 Australia Univ Tasmania Sch Land & Food Surveying & Spatial Sci Grp Hobart Tas 7001 Australia

Beetle infestations have caused significant damage to the pine forest in North America. Early detection of beetle infestation in near real time is crucial, in order to take appropriate steps to control the damage. In this letter, we consider near-real-time detection of beetle infestation in North American pine forests using high temporal resolution and coarse spatial resolution MODIS (eight-day 500-m) satellite data. We show that the parameter sequence of a stationary vegetation index time series, which is derived by fitting an underlying triply modulated cosine model over a sliding window using nonlinear least squares, resembles a martingale sequence. The advantage of such properties of the parameter sequence is that standard martingale central limit theorem and well-known Gaussian distribution statistics can be effectively used to detect any nonstationarity in the vegetation index time series with high accuracy. The proposed method exploits these properties of the parameter time series and, hence, does not require threshold tuning. The threshold is selected based on a well-documented procedure of z-value selection from the table of Gaussian distribution, depending upon the percentage of the distribution considered as outlier. The proposed framework is tested on different vegetation index data sets derived from MODIS eight-day 500-m image time series of beetle infestations in North America. The results show that the proposed framework can detect nonstationarities in the vegetation index time series accurately and performs the best on red-green index.

关键词： change detection algorithms least squares approximations time series analysis

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