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PPCNET: A Combined Patch-Level and Pixel-Level End-to-End Deep Network for High-Resolution Remote Sensing Image change detection

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2020年第10期17卷 1797-1801页

作者： Bao, Tengfei Fu, Chenqin Fang, Tao Huo, Hong Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China Shanghai Jiao Tong Univ Coll Elect Informat & Elect Engn Shanghai Peoples R China

Extracting change regions from bitemporal images is crucial to urban planning, land, and resources survey. In the literature, many methods obtaining difference between bitemporal remote sensing images have been proposed. However, there are still some problems due to the complexity of change conditions. In order to solve the above-mentioned problems, we propose a novel network called PPCNET, combining patch-level and pixel-level change detection for bitemporal remote sensing images. This network is divided into three branches: the dual structure is used to extract features of bitemporal images, respectively;changed or unchanged image regions are then detected through fully connected layers, and a soft-max layer at patch level. Once a change is detected at patch level, feature encoder and decoder at pixel level are activated to obtain accurate change boundary. Furthermore, a feature pyramid network-based architecture is employed to fuse information in different layers to further improve change detection effectiveness. Experiments on both satellite and aerial remote sensing images have verified that PPCNET network yields higher change detection accuracy with faster detection speed.

关键词： Feature extraction Remote sensing Satellites Training Convolution change detection algorithms Decoding Bitemporal images change or unchange pairs combining patch-level and pixel-level information pixel-patch combination net

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A Unified Approach to change detection Using an Adaptive Ensemble of Extreme Learning Machines

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2020年第5期17卷 794-798页

作者： Khurana, Madhu Saxena, Vikas Jaypee Inst Informat Technol Dept Comp Sci Engn & Informat Technol Noida 201309 India

change detection for remote sensing (RS) images is a challenging task. The variation in the spatial, radiometric, spectral, and temporal resolution of the images adds to the complexity of the change-detection process. The application domain also has an impact on the decision to use a particular change-detection technique. There is no generic classification algorithm, which can be used for different application domains using the RS images like green cover, land use, forest fires, and so on. This letter proposes an adaptive ensemble of extreme learning machines (ELMs) for classification of RS images into change/no-change classes. ELM has good generalization capability and a fast learning phase. Therefore, an adaptive ensemble of different ELMs has been proposed. The proposed algorithm has been implemented in five sets of data. It has been used for earth monitoring applications, viz. green cover, flat fires, water bodies, and so on. Here the results for two data sets, viz. Sardinia Island and Mexico Fire have been presented. The results thus obtained are highly promising. They show an average accuracy of 90.5% in detecting the change.

关键词： Training Feature extraction Testing Earth Image reconstruction Remote sensing change detection algorithms change detection extreme learning machines (ELMs) remote sensing (RS) images

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EDGE-detection USING SEQUENTIAL-METHODS FOR change IN LEVEL .2. SEQUENTIAL detection OF change IN MEAN

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING 1981年第1期29卷 32-50页

作者： BASSEVILLE, M Laboratoire d'Automatique IRISA Rennes France

The first part of this paper has been devoted to the presentation of a sequential edge detection algorithm, the local detector of which uses sequential estimators of change in mean grey level. A detailed study of some sequential detectors of change in mean is presented in this second part; this study includes some comparisons issued from simulations and new theoretical results concerning the best of them.

关键词： Image edge detection Detectors Testing Bayesian methods Sequential analysis Statistical analysis Statistical distributions Maximum likelihood detection Maximum likelihood estimation change detection algorithms

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Video retrieval based on scene change detection in compressed streams

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 1999年第3期45卷 932-936页

作者： Kang, EK Kim, SJ Choi, JS Chung Ang Univ Dept Elect Engn Tongjak Ku Seoul 156756 South Korea

This paper presents a new key frame extraction algorithm as well as a novel video-indexing scheme for a fast content-based browsing and retrieval in a video database. We first extract key frames by matching AHIM (Accumulated Histogram Intersection Measure) of DC image sequence constructed from the MPEG video sequence. Then we use the region segmentation-based projective histogram and its moments as database indices for video retrieval.

关键词： Video compression Layout Information retrieval Image databases Histograms Image coding change detection algorithms Content based retrieval Image sequences Transform coding

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EDGE-detection USING SEQUENTIAL-METHODS FOR change IN LEVEL .1. A SEQUENTIAL EDGE-detection ALGORITHM

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING 1981年第1期29卷 24-31页

作者： BASSEVILLE, M ESPIAU, B GASNIER, J Laboratoire d'Automatique IRISA Rennes France

A sequential algorithm for edge detection using a line-byline detector of edge elements connected to a recursive edge-following scheme is presented. On each line, edge elements are detected by means of a filtering operation in order to follow the slow variations of the gray level and some sequential and recursive estimators for locating jumps in this level. The edge-following problem is solved by a Kalman filter, the state model corresponding to a noisy straight line. In this first part, the complete edge detection algorithm is presented after a brief survey of edge detection methods available in the literature. Two main examples of applications are given: detection of white and black targets in the landscape in order to perform automatic driving of vehicles and detection of blood vessels in stereographic images of the brain. In the second part, a detailed study of the sequential estimators for change in mean, which are used in the line-by-line detection, will be found.

关键词： Image edge detection change detection algorithms Detectors Vehicle detection Statistics Iterative algorithms Recursive estimation Kalman filters Vehicle driving Blood vessels

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Optimizing SAR change detection based on log-ratio features 9

Optimizing SAR change detection based on log-ratio features

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9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp)

作者： Garzelli, Andrea Zoppetti, Claudia Univ Siena Dept Informat Engn & Math Via Roma 56 I-53100 Siena Italy

ISBN: (纸本)9781538633274

The paper investigates how to optimize the performances of unsupervised log-ratio based change detection algorithms for two-date 1-look amplitude SAR images. The usual approach of pre-processing the SAR images at different dates with state-of-the-art despeckling filters is critically discussed. Those adaptive filters are very efficient, also for the challenging case of 1-look images, for speckle reduction of single-date image data and then for providing reliable classification, detection, or parameter estimation results. However, they are not able to ease the discrimination of statistical from structural changes in 1-look SAR images for which reliable point-target detection is nearly impractical. A simple, yet very effective, multiscale method for change detection and automatic change mapping is proposed and tested on simulated 1-look SAR images. The adopted preprocessing is based on guided image filtering with different window sizes. It improves the detection of changed regions without introducing any geometrical constraint and significantly reduces the false alarm rate. Experimental tests on simulated SAR images and Spotlight COSMO-SkyMed images demonstrate the advantages of the proposed algorithm.

关键词： Synthetic aperture radar Remote sensing Speckle change detection algorithms Reliability Backscatter

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change detection in Unmanned Aerial Vehicle Images for Industrial Infrastructure Rooftop Monitoring 19

Change Detection in Unmanned Aerial Vehicle Images for Indus...

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19th International Conference on Ubiquitous Robots (UR)

作者： Jang, Inmo Lim, Suckhyun Jeon, Hyunkook Samsung Elect Environm & Safety Res Ctr Hwaseongi 18448 Gyeonggi South Korea

ISBN: (纸本)9781665482530

This work proposes a change detection algorithm based on aerial images from an UAV (Unmanned Aerial Vehicle) for infrastructure rooftop monitoring. Taking pixel-wise differences simply between different temporal aerial images for the same scene may cause many false positives because of (a) the residual misalignments even after an image alignment preprocess;and (b) shadow and lightness differences in the images. To address such inherent characteristics of UAV images that are unfavourable in image comparing, we propose to use (a) a colour distance metric based on a weighted LAB space, which can not only mitigate the shadow effects but also enable users to be flexibly involved in detecting specific-coloured objects;(b) a Gaussian-blur filtering to emphasize major changes, while neutralising subtle changes;and (c) long-edge removal and local refinement process to reduce major false positives caused by the residual misalignments. We also conduct a mock-up experiment at a real infrastructure plant to evaluate the proposed method in terms of detecting smoke, liquid leakage, and cracking ducts, which are major phonomena of industrial malfunctions and accidents. The results show that the algorithm is able to spot the smoke and liquid leakage. On the contrary, detecting cracks is found to be not straightforward as they are viewed relatively small to be detected at the drone-filming height. We also discuss how to overcome this limitation and suggest potential approaches to improve the proposed algorithm further.

关键词： Liquids change detection algorithms Image color analysis Filtering Ducts Autonomous aerial vehicles Extraterrestrial measurements

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Robust Quickest change detection in Statistically Periodic Processes

Robust Quickest Change Detection in Statistically Periodic P...

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IEEE International Symposium on Information Theory (ISIT)

作者： Banerjee, Taposh Taha, Ahmad John, Eugene Univ Texas San Antonio San Antonio TX 78249 USA

ISBN: (纸本)9781538682098

The problem of detecting a change in the distribution of a statistically periodic process is investigated. The problem is posed in the framework of independent and periodically identically distributed (i.p.i.d.) processes, a recently introduced class of processes to model statistically periodic data. An algorithm is proposed that is shown to be robust against an uncertainty in the post-change law. The motivation for the problem comes from event detection problems in traffic data, social network data, electrocardiogram data, and neural data, where periodic statistical behavior has been observed.

关键词： Uncertainty Social networking (online) Event detection change detection algorithms Stochastic processes Distributed databases Robustness

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DATA DRIVEN STOCHASTIC APPROXIMATION FOR change detection

DATA DRIVEN STOCHASTIC APPROXIMATION FOR CHANGE DETECTION

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Winter Simulation Conference (WSC)

作者： Flynn, Thomas Hadjiliadis, Olympia Stamos, Ioannis Vazquez-Abad, Felisa J. CUNY Grad Ctr Dept Comp Sci 365 5th Ave New York NY 10016 USA CUNY Hunter Coll Dept Math & Stat 695 Pk Ave New York NY 10065 USA CUNY Hunter Coll Dept Comp Sci 695 Pk Ave New York NY 10065 USA

ISBN: (纸本)9781538634288

Online change detection has many applications, ranging from finance and manufacturing, to security and computer vision. Designing a change detector for use in a given domain can be very time consuming, and model-based algorithms often require knowledge of the underlying stochastic model. To address these issues, in this work we explore a supervised learning approach to a change detector. We implement a gradient based procedure to find the optimal parameters for a change detector. We demonstrate the methodology on both synthetic and real world data for classifying 3D laser range image data in real-time.

关键词： Detectors change detection algorithms Stochastic processes Three-dimensional displays Optimization Delays Computational modeling

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City-scale Scene change detection using Point Clouds

City-scale Scene Change Detection using Point Clouds

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yew, Zi Jian Lee, Gim Hee Natl Univ Singapore Dept Comp Sci Singapore Singapore

ISBN: (纸本)9781728190778

We propose a method for detecting structural changes in a city using images captured from vehicular mounted cameras over traversals at two different times. We first generate 3D point clouds for each traversal from the images and approximate GNSS/INS readings using Structure-from-Motion (SfM). A direct comparison of the two point clouds for change detection is not ideal due to inaccurate geo-location information and possible drifts in the SfM. To circumvent this problem, we propose a deep learning-based non-rigid registration on the point clouds which allows us to compare the point clouds for structural change detection in the scene. Furthermore, we introduce a dual thresholding check and post-processing step to enhance the robustness of our method. We collect two datasets for the evaluation of our approach. Experiments show that our method is able to detect scene changes effectively, even in the presence of viewpoint and illumination differences.

关键词： Three-dimensional displays Automation change detection algorithms Conferences Urban areas Lighting Cameras

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