S.I.I.A for monitoring crop evolution and anomaly detection in Andalusia by remote sensing

Automatic ship detection technology in optical remote sensing images has a wide range of applications in civilian and military fields. Among most important challenges encountered in ship detection, we focus on the following three selected ones: (a) ships with low contrast; (b) sea surface in complex situations; and (c) false alarm interference such as clouds and reefs. To overcome these challenges, this paper proposes coarse-to-fine ship detection strategies based on anomaly detection and spatial pyramid pooling pcanet (SPP-PCANet). The anomaly detection algorithm, based on the multivariate Gaussian distribution, regards a ship as an abnormal marine area, effectively extracting candidate regions of ships. Subsequently, we combine PCANet and spatial pyramid pooling to reduce the amount of false positives and improve the detection rate. Furthermore, the non-maximum suppression strategy is adopted to eliminate the overlapped frames on the same ship. To validate the effectiveness of the proposed method, GF-1 images and GF-2 images were utilized in the experiment, including the three scenarios mentioned above. Extensive experiments demonstrate that our method obtains superior performance in the case of complex sea background, and has a certain degree of robustness to external factors such as uneven illumination and low contrast on the GF-1 and GF-2 satellite image data.

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Exploring the performance–power–energy balance of low-power multicore and manycore architectures for anomaly detection in remote sensing

The Journal of Supercomputing ◽

10.1007/s11227-014-1372-x ◽

2014 ◽

Vol 71 (5) ◽

pp. 1893-1906 ◽

Cited By ~ 6

Author(s):

G. León ◽

J. M. Molero ◽

E. M. Garzón ◽

I. García ◽

A. Plaza ◽

...

Keyword(s):

Remote Sensing ◽

Energy Balance ◽

Anomaly Detection ◽

Low Power

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An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

Remote Sensing ◽

10.3390/rs12010043 ◽

2019 ◽

Vol 12 (1) ◽

pp. 43 ◽

Cited By ~ 2

Author(s):

Maurício Araújo Dias ◽

Erivaldo Antônio da Silva ◽

Samara Calçado de Azevedo ◽

Wallace Casaca ◽

Thiago Statella ◽

...

Keyword(s):

Water Pollution ◽

Remote Sensing ◽

Anomaly Detection ◽

Synthetic Data ◽

Open Water ◽

Surveillance Systems ◽

Environmental Disasters ◽

Detection Strategy ◽

Potential Applications ◽

Static Images

The potential applications of computational tools, such as anomaly detection and incongruence, for analyzing data attract much attention from the scientific research community. However, there remains a need for more studies to determine how anomaly detection and incongruence applied to analyze data of static images from remote sensing will assist in detecting water pollution. In this study, an incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing is presented. Our strategy semi-automatically detects occurrences of one type of anomaly based on the divergence between two image classifications (contextual and non-contextual). The results indicate that our strategy accurately analyzes the majority of images. Incongruence as a strategy for detecting anomalies in real-application (non-synthetic) data found in images from remote sensing is relevant for recognizing crude oil close to open water bodies or water pollution caused by the presence of brown mud in large rivers. It can also assist surveillance systems by detecting environmental disasters or performing mappings.

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Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications

Applied Sciences ◽

10.3390/app11114878 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4878

Author(s):

Ivan Racetin ◽

Andrija Krtalić

Keyword(s):

Remote Sensing ◽

Image Processing ◽

Anomaly Detection ◽

Target Detection ◽

Hyperspectral Image ◽

Hyperspectral Images ◽

Detection Methods ◽

Hyperspectral Image Processing ◽

Sensing Applications ◽

Remote Sensing Applications

Hyperspectral sensors are passive instruments that record reflected electromagnetic radiation in tens or hundreds of narrow and consecutive spectral bands. In the last two decades, the availability of hyperspectral data has sharply increased, propelling the development of a plethora of hyperspectral classification and target detection algorithms. Anomaly detection methods in hyperspectral images refer to a class of target detection methods that do not require any a-priori knowledge about a hyperspectral scene or target spectrum. They are unsupervised learning techniques that automatically discover rare features on hyperspectral images. This review paper is organized into two parts: part A provides a bibliographic analysis of hyperspectral image processing for anomaly detection in remote sensing applications. Development of the subject field is discussed, and key authors and journals are highlighted. In part B an overview of the topic is presented, starting from the mathematical framework for anomaly detection. The anomaly detection methods were generally categorized as techniques that implement structured or unstructured background models and then organized into appropriate sub-categories. Specific anomaly detection methods are presented with corresponding detection statistics, and their properties are discussed. This paper represents the first review regarding hyperspectral image processing for anomaly detection in remote sensing applications.

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Parallel and Distributed Computing for Anomaly Detection From Hyperspectral Remote Sensing Imagery

Proceedings of the IEEE ◽

10.1109/jproc.2021.3076455 ◽

2021 ◽

pp. 1-14

Author(s):

Qian Du ◽

Bo Tang ◽

Weiying Xie ◽

Wei Li

Keyword(s):

Remote Sensing ◽

Distributed Computing ◽

Anomaly Detection ◽

Hyperspectral Remote Sensing ◽

Parallel And Distributed Computing ◽

Remote Sensing Imagery

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npphen: an R-package for non-parametric reconstruction of vegetation phenology and anomaly detection using remote sensing

10.1101/301143 ◽

2018 ◽

Cited By ~ 4

Author(s):

Sergio A. Estay ◽

Roberto O. Chávez

Keyword(s):

Remote Sensing ◽

Anomaly Detection ◽

Land Surface ◽

Relevant Information ◽

R Package ◽

Plant Ecology ◽

Essential Biodiversity Variables ◽

Cost Efficient ◽

High Level ◽

Non Parametric

AbstractFor ecologists, the challenge at using remote sensing tools is to convert spectral data into ecologically relevant information like abundance, productivity or traits distribution. Among these features, plant phenology is one of the most used variables in any study applying remote sensing to plant ecology and it has formally considered as one of the Essential Biodiversity Variables. Currently, satellite imagery make possible cost-efficient monitoring of land surface phenology (LSP), but methods applicable to different ecosystems are not available. Here, we introduce the ‘npphen’ R-package developed for remote sensing LSP reconstruction and anomaly detection using non-parametric techniques. The package implements basic and high-level functions for manipulating vector and raster data to obtain high resolution spatial and temporal LSP reconstructions. Advantages of ‘npphen’ are: its flexibility to describe any LSP pattern (suitable for any ecosystem), it handles time series or raster stacks with and without gaps, and it provides confidence interval for the expected LSP at yearly basis, useful to judge anomaly magnitudes. We present two study cases to show how ‘npphen’ can successfully reconstruct and map LSP and anomalies for contrasting ecosystems.

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