Anomaly detection using remote sensing for the archaeological heritage registration

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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COSMO-SkyMed SAR for Detection and Monitoring of Archaeological and Cultural Heritage Sites

Remote Sensing ◽

10.3390/rs11111326 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1326 ◽

Cited By ~ 15

Author(s):

Deodato Tapete ◽

Francesca Cigna

Keyword(s):

Remote Sensing ◽

Cultural Heritage ◽

Land Surface ◽

Cultural Landscapes ◽

Research Unit ◽

Use Cases ◽

Archaeological Heritage ◽

Heritage Sites ◽

Research Activities ◽

Sar Imagery

Synthetic aperture radar (SAR) imagery has long been used in archaeology since the earliest space radar missions in the 1980s. In the current scenario of SAR missions, the Italian Space Agency (ASI)’s COnstellation of small Satellites for Mediterranean basin Observation (COSMO-SkyMed) has peculiar properties that make this mission of potential use by archaeologists and heritage practitioners: high to very high spatial resolution, site revisit of up to one day, and conspicuous image archives over cultural heritage sites across the globe. While recent literature and the number of research projects using COSMO-SkyMed data for science and applied research suggest a growing interest in these data, it is felt that COSMO-SkyMed still needs to be further disseminated across the archaeological remote sensing community. This paper therefore offers a portfolio of use-cases that were developed in the last two years in the Scientific Research Unit of ASI, where COSMO-SkyMed data were analysed to study and monitor cultural landscapes and heritage sites. SAR-based applications in archaeological and cultural heritage sites in Peru, Syria, Italy, and Iraq, provide evidence on how subsurface and buried features can be detected by interpreting SAR backscatter, its spatial and temporal changes, and interferometric coherence, and how SAR-derived digital elevation models (DEM) can be used to survey surface archaeological features. The use-cases also showcase how high temporal revisit SAR time series can support environmental monitoring of land surface processes, and condition assessment of archaeological heritage and landscape disturbance due to anthropogenic impact (e.g., agriculture, mining, looting). For the first time, this paper provides an overview of the capabilities of COSMO-SkyMed imagery in StripMap Himage and Spotlight-2 mode to support archaeological studies, with the aim to encourage remote sensing scientists and archaeologists to search for and exploit these data for their investigations and research activities. Furthermore, some considerations are made with regard to the perspectives opened by the upcoming launch of ASI’s COSMO-SkyMed Second Generation constellation.

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Endangered archaeology in Libya: recording damage and destruction

Libyan Studies ◽

10.1017/lis.2017.7 ◽

2017 ◽

Vol 48 ◽

pp. 23-49 ◽

Cited By ~ 9

Author(s):

Louise Rayne ◽

Nichole Sheldrick ◽

Julia Nikolaus

Keyword(s):

Remote Sensing ◽

Middle East ◽

Natural Resource ◽

North Africa ◽

Coastal Plain ◽

Agricultural Development ◽

Urban Expansion ◽

Archaeological Sites ◽

Mena Region ◽

Archaeological Heritage

AbstractLibya's archaeological heritage is under serious threat, not only because of recent conflict, but also due to other factors such as urban expansion, agricultural development, natural resource prospection, vandalism, looting and natural deterioration. The Endangered Archaeology in the Middle East and North Africa Project (EAMENA) has developed a database and methodology using remote sensing and other techniques to rapidly document archaeological sites and any disturbances and threats to them in Libya and across the MENA region. This paper will demonstrate this methodology and highlight the various types of disturbances and threats affecting the archaeology of Libya, concentrating on four case studies in different areas of the country, including the coastal plain around Zliten, a section of the Wadi Sofeggin in the pre-desert, and the desert oases of Jufra and Murzuq.

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

10.1117/12.514165 ◽

2004 ◽

Author(s):

Antonio Jose Rodriguez Perez ◽

El Mostafa Louakfaoui ◽

Antonio Munoz Rastrero ◽

Luis Alberto Rubio Perez ◽

Carmen de Pablos Epalza

Keyword(s):

Remote Sensing ◽

Anomaly Detection ◽

Crop Evolution

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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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