1,448 research outputs found
Sketched Multi-view Subspace Learning for Hyperspectral Anomalous Change Detection
In recent years, multi-view subspace learning has been garnering increasing
attention. It aims to capture the inner relationships of the data that are
collected from multiple sources by learning a unified representation. In this
way, comprehensive information from multiple views is shared and preserved for
the generalization processes. As a special branch of temporal series
hyperspectral image (HSI) processing, the anomalous change detection task
focuses on detecting very small changes among different temporal images.
However, when the volume of datasets is very large or the classes are
relatively comprehensive, existing methods may fail to find those changes
between the scenes, and end up with terrible detection results. In this paper,
inspired by the sketched representation and multi-view subspace learning, a
sketched multi-view subspace learning (SMSL) model is proposed for HSI
anomalous change detection. The proposed model preserves major information from
the image pairs and improves computational complexity by using a sketched
representation matrix. Furthermore, the differences between scenes are
extracted by utilizing the specific regularizer of the self-representation
matrices. To evaluate the detection effectiveness of the proposed SMSL model,
experiments are conducted on a benchmark hyperspectral remote sensing dataset
and a natural hyperspectral dataset, and compared with other state-of-the art
approaches
EMS-Net: Efficient Multi-Temporal Self-Attention For Hyperspectral Change Detection
Hyperspectral change detection plays an essential role of monitoring the
dynamic urban development and detecting precise fine object evolution and
alteration. In this paper, we have proposed an original Efficient
Multi-temporal Self-attention Network (EMS-Net) for hyperspectral change
detection. The designed EMS module cuts redundancy of those similar and
containing-no-changes feature maps, computing efficient multi-temporal change
information for precise binary change map. Besides, to explore the clustering
characteristics of the change detection, a novel supervised contrastive loss is
provided to enhance the compactness of the unchanged. Experiments implemented
on two hyperspectral change detection datasets manifests the out-standing
performance and validity of proposed method.Comment: 4 pages, 5 figures, submitted to IGARSS202
Characterization of the spectral distribution of hyperspectral imagery for improved exploitation
Widely used methods of target, anomaly, and change detection when applied to spectral imagery provide less than desirable results due to the complex nature of the data. In the case of hyperspectral data, dimension reduction techniques are employed to reduce the amount of data used in the detection algorithms in order to produce better results and/or decreased computation time. This essentially ignores a significant amount of the data collected in k unique spectral bands. Methods presented in this work explore using the distribution of the collected data in the full k dimensions in order to identify regions of interest contained in spatial tiles of the scene. Here, interest is defined as small and large scale manmade activity. The algorithms developed in this research are primarily data driven with a limited number of assumptions. These algorithms will individually be applied to spatial subsets or tiles of the full scene to indicate the amount of interest contained. Each tile is put through a series of tests using the algorithms based on the full distribution of the data in the hyperspace. The scores from each test will be combined in such a way that each tile is labeled as either interesting or not interesting. This provides a cueing mechanism for image analysts to visually inspect locations within a hyperspectral scene with a high likelihood of containing manmade activity
GlobalMind: Global Multi-head Interactive Self-attention Network for Hyperspectral Change Detection
High spectral resolution imagery of the Earth's surface enables users to
monitor changes over time in fine-grained scale, playing an increasingly
important role in agriculture, defense, and emergency response. However, most
current algorithms are still confined to describing local features and fail to
incorporate a global perspective, which limits their ability to capture
interactions between global features, thus usually resulting in incomplete
change regions. In this paper, we propose a Global Multi-head INteractive
self-attention change Detection network (GlobalMind) to explore the implicit
correlation between different surface objects and variant land cover
transformations, acquiring a comprehensive understanding of the data and
accurate change detection result. Firstly, a simple but effective Global Axial
Segmentation (GAS) strategy is designed to expand the self-attention
computation along the row space or column space of hyperspectral images,
allowing the global connection with high efficiency. Secondly, with GAS, the
global spatial multi-head interactive self-attention (Global-M) module is
crafted to mine the abundant spatial-spectral feature involving potential
correlations between the ground objects from the entire rich and complex
hyperspectral space. Moreover, to acquire the accurate and complete
cross-temporal changes, we devise a global temporal interactive multi-head
self-attention (GlobalD) module which incorporates the relevance and variation
of bi-temporal spatial-spectral features, deriving the integrate potential same
kind of changes in the local and global range with the combination of GAS. We
perform extensive experiments on five mostly used hyperspectral datasets, and
our method outperforms the state-of-the-art algorithms with high accuracy and
efficiency.Comment: 14 page, 18 figure
Optimized Hyperspectral Imagery Anomaly Detection Through Robust Parameter Design
Anomaly detection algorithms for hyperspectral imagery (HSI) are an important first step in the analysis chain which can reduce the overall amount of data to be processed. The actual amount of data reduced depends greatly on the accuracy of the anomaly detection algorithm implemented. Most, if not all, anomaly detection algorithms require a user to identify some initial parameters. These parameters (or controls) affect overall algorithm performance. Regardless of the anomaly detector being utilized, algorithm performance is often negatively impacted by uncontrollable noise factors which introduce additional variance into the process. In the case of HSI, the noise variables are embedded in the image under consideration. Robust parameter design (RPD) offers a method to model the controls as well as the noise variables and identify robust parameters. This research identifies image noise characteristics necessary to perform RPD on HSI. Additionally, a small sample training and test algorithm is presented. Finally, the standard RPD model is extended to consider higher order noise coefficients. Mean and variance RPD models are optimized in a dual response function suggested by Lin and Tu. Results are presented from simulations and two anomaly detection algorithms, the Reed-Xiaoli anomaly detector and the autonomous global anomaly detector
Reconstruction Error and Principal Component Based Anomaly Detection in Hyperspectral imagery
The rapid expansion of remote sensing and information collection capabilities demands methods to highlight interesting or anomalous patterns within an overabundance of data. This research addresses this issue for hyperspectral imagery (HSI). Two new reconstruction based HSI anomaly detectors are outlined: one using principal component analysis (PCA), and the other a form of non-linear PCA called logistic principal component analysis. Two very effective, yet relatively simple, modifications to the autonomous global anomaly detector are also presented, improving algorithm performance and enabling receiver operating characteristic analysis. A novel technique for HSI anomaly detection dubbed multiple PCA is introduced and found to perform as well or better than existing detectors on HYDICE data while using only linear deterministic methods. Finally, a response surface based optimization is performed on algorithm parameters such as to affect consistent desired algorithm performance
Efficient object tracking in WAAS data streams
Wide area airborne surveillance (WAAS) systems are a new class of remote sensing imagers which have many military and civilian applications. These systems are characterized by long loiter times (extended imaging time over fixed target areas) and large footprint target areas. These characteristics complicate moving object detection and tracking due to the large image size and high number of moving objects. This thesis evaluates existing object detection and tracking algorithms with WAAS data and provides enhancements to the processing chain which decrease processing time and increase tracking accuracy. Decreases in processing time are needed to perform real-time or near real-time tracking either on the WAAS sensor platform or in ground station processing centers. Increased tracking accuracy benefits real-time users and forensic (off-line) users. The original contribution of this thesis increases tracking efficiency and accuracy by breaking a WAAS scene into hierarchical areas of interest (AOIs) and through the use of hyperspectral cueing
Surface and Buried Landmine Scene Generation and Validation Using the Digital Imaging and Remote Sensing Image Generation Model
Detection and neutralization of surface-laid and buried landmines has been a slow and dangerous endeavor for military forces and humanitarian organizations throughout the world. In an effort to make the process faster and safer, scientists have begun to exploit the ever-evolving passive electro-optical realm, both from a broadband perspective and a multi or hyperspectral perspective. Carried with this exploitation is the development of mine detection algorithms that take advantage of spectral features exhibited by mine targets, only available in a multi or hyperspectral data set. Difficulty in algorithm development arises from a lack of robust data, which is needed to appropriately test the validity of an algorithm’s results. This paper discusses the development of synthetic data using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. A synthetic landmine scene has been modeled after data collected at a US Army arid testing site by the University of Hawaii’s Airborne Hyperspectral Imager (AHI). The synthetic data has been created and validated to represent the surrogate minefield thermally, spatially, spectrally, and temporally over the 7.9 to 11.5 micron region using 70 bands of data. Validation of the scene has been accomplished by direct comparison to the AHI truth data using qualitative band to band visual analysis, Rank Order Correlation comparison, Principle Components dimensionality analysis, and an evaluation of the R(x) algorithm’s performance. This paper discusses landmine detection phenomenology, describes the steps taken to build the scene, modeling methods utilized to overcome input parameter limitations, and compares the synthetic scene to truth data
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