Detecting Anomaly Regions in Satellite Image Time Series Based on Sesaonal Autocorrelation Analysis

Zhou, Zeng-Guang; Tang, Ping; Zhou, Man

doi:10.5194/isprs-annals-iii-3-303-2016

Cited by 5 publications

(13 citation statements)

References 24 publications

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“…However, [25] and [82] are very specific case studies. Whereas in this paper and in [66,[76][77][78][80][81][82][98][99][100] the areas of study are heterogeneous, in [25], results were presented only related to an ice shelf in the Antarctic region, which is a homogeneous area of study. According to [100], results tend to be higher for homogeneous areas of study compared to heterogeneous ones.…”

Section: Study Accuracy Precision Recall F-measurementioning

confidence: 91%

“…Nonetheless, it does not consider when they occurred, i.e., temporal analyses [13]. Therefore, there is no time-series [76][77][78], i.e., satellite images before and after the occurrence of an event [19] or disaster [31][32][33], involved in the context. Moreover, detection of anomaly of this type depends on other three conditions to be satisfied: high sensory data quality [26], contextual [9], and non-contextual [8,[27][28][29][30] classification [5,13,[15][16][17][18] of the same image, and incongruence [13,[15][16][17][18].…”

Section: The Differences Between Outlier Anomaly and Incongruencementioning

confidence: 99%

“…In contrast, for example, incongruent results from contextual and non-contextual classification of images qualified with low values indicate the presence of another type of anomaly [13]. In another example, incongruent results from contextual and non-contextual classification of time-series images [76][77][78] qualified with high values indicate the presence of a third type of anomaly [13].…”

Section: The Differences Between Outlier Anomaly and Incongruencementioning

confidence: 99%

“…Most current research focuses on outlier detection, such as [1,14,[19][20][21][22][23][24][25]44,[51][52][53][54]66,[76][77][78][80][81][82], however we discuss below only those research which are more relevant to this article. An outlier-based change detection method to detect abnormal points in multi-scale time-series images from remote sensing was proposed by Yin Shoujing et al in [76].…”

Section: Research On Outliers Detectionmentioning

confidence: 99%

“…A method for detecting anomaly regions in each image of satellite image time-series was proposed by Zhou et al in [77]. The method identifies spatial-temporal dynamic processes of unexpected changes of land cover based on seasonal autocorrelation analysis.…”

Section: Research On Outliers Detectionmentioning

confidence: 99%

See 4 more Smart Citations

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

et al. 2019

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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. Kittler et al. in [13] proposed a taxonomy of anomalies which expanded the concept of anomaly beyond the conventional meaning of outlier. They used sensory data quality assessment [26], contextual [9] and non-contextual [27][28][29][30] classifiers [5,[15][16][17][18], and an incongruence indicator [16][17][18] to identify each type of anomaly [13]. According to this taxonomy [13] anomalies can be, for example, of the types unknown object, measurement model drift, unknown structure, unexpected structural component, component model drift, and unexpected structure and structural components. This taxonomy [13] is well-known and widely accepted by the scientific community because it has the potential to be applied for solving problems in many different research areas [13]. Therefore, studies related to the application of the taxonomy [13] on synthetic data are common, such as in [16][17][18]. However, to the knowledge of the authors, studies have not addressed the practical application of the taxonomy [13] to solve real-world problems [2,[31][32][33], because it remains a challenge for all research areas.Anomaly detection [13] and incongruence [15][16][17][18] are two powerful computational tools from pattern recognition (PR) [3,[11][12][13][14][15][16][17][18]34,35] and computer vision (CV) [3,36]. PR is a scientific area of study dedicated to analyze patterns and regularities in data [3]. PR provides powerful tools [34] for many different applications and research areas [35] such as scientific research, private and public industries, military activities, etc., [14,[21][22][23][24]32,[37][38][39][40][41][42][43][44][45][46][47][48]. For example, PR is important for geosciences as its tools are used to analyze geographical features of environments in digital images from remote sensing, i.e., scenes [11,12,[49][50][51][52][53][54]. Additionally, PR also provides powerful tools to help machine perception...

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Section: Study Accuracy Precision Recall F-measurementioning

confidence: 91%

Section: The Differences Between Outlier Anomaly and Incongruencementioning

confidence: 99%

Section: The Differences Between Outlier Anomaly and Incongruencementioning

confidence: 99%

Section: Research On Outliers Detectionmentioning

confidence: 99%

Section: Research On Outliers Detectionmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2019

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Mapping of ferric (Fe ³⁺ ) and ferrous (Fe ²⁺ ) iron oxides distribution using ASTER and Landsat 8 OLI data, in Negash Lateritic iron deposit, Northern Ethiopia

Abay

Legesse

Suryabhagavan

et al. 2022

Geology, Ecology, and Landscapes

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The Use of LSTM-Based RNN and SVM Models to Detect Ludian Coseismic Landslides in Time Series Images

Zhou

Chen

2020

J. Phys.: Conf. Ser.

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With the increase of temporal frequency of satellite images, time series analysis based on Artificial Neural Network has become a tendency to detect land cover changes in images. We briefly introduces a case study that uses Long-Short Term Memory (LSTM), a specific Recurrent Neural Network (RNN) for time series modelling and forecasting, and Support Vector Machine (SVM) to detect landslides triggered by Ms.6.5 earthquake in Ludian, China in 2014. The study uses 72 available images with 16/30m spatial resolution from Landsat-7/8, GaoFen-1 and HJ-1A/1B satellites. Firstly, per-pixel LSTM models are trained by Normalized Difference Vegetation Index (NDVI) images before the earthquake. Secondly, the trained LSTM models are used to predict NDVI images after the earthquake. Then, anomalies or changes are detected by comparing predicted and observed NDVI images. Finally, anomalies related to landslides are separated from other changes with a SVM model which was trained by multi-spectral images in the study area. Experiment demonstrates that the recall rate and precision rate of landslides detection are 82.09% and 76.21%, respectively. The study shows a potential that the combination of LSTM and SVM models can be used to detect landslides in Landsat-like time series images with 30m resolution.

show abstract

Detecting Anomaly Regions in Satellite Image Time Series Based on Sesaonal Autocorrelation Analysis

Cited by 5 publications

References 24 publications

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

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

Mapping of ferric (Fe ³⁺ ) and ferrous (Fe ²⁺ ) iron oxides distribution using ASTER and Landsat 8 OLI data, in Negash Lateritic iron deposit, Northern Ethiopia

The Use of LSTM-Based RNN and SVM Models to Detect Ludian Coseismic Landslides in Time Series Images

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Detecting Anomaly Regions in Satellite Image Time Series Based on Sesaonal Autocorrelation Analysis

Cited by 5 publications

References 24 publications

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

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

Mapping of ferric (Fe 3+ ) and ferrous (Fe 2+ ) iron oxides distribution using ASTER and Landsat 8 OLI data, in Negash Lateritic iron deposit, Northern Ethiopia

The Use of LSTM-Based RNN and SVM Models to Detect Ludian Coseismic Landslides in Time Series Images

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Product

Resources

About

Mapping of ferric (Fe ³⁺ ) and ferrous (Fe ²⁺ ) iron oxides distribution using ASTER and Landsat 8 OLI data, in Negash Lateritic iron deposit, Northern Ethiopia