Anomalous behaviour detection using one-class support vector machine and remote sensing images: a case study of algal bloom occurrence in inland waters

Ananias, Pedro Henrique Moraes; Negri, Rogério Galante

doi:10.1080/17538947.2021.1907462

Cited by 16 publications

(5 citation statements)

References 50 publications

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“…It is important to point out that the anomaly detection scheme modeled in our approach follows concepts that are similar to those behind other recently published methods [21,42], which preserve anomalous patterns within a sequence of remote sensing images. In fact, the created time series generates pixels whose values fluctuate around zero, regardless of the target assigned to the pixels.…”

Section: Analysis Configurationmentioning

confidence: 94%

“…The Local Outlier Factor [37], Elliptic Envelope [38], One-Class Support Vector Machine (OC-SVM) [39], and Isolation Forest (IF) [40] are representatives of anomaly detection methods that are commonly found in the scientific literature. In particular, the OC-SVM and IF methods have been successfully employed in remote sensing studies [21,41,42].…”

Section: Anomaly Detectionmentioning

confidence: 99%

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Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series

et al. 2023

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The synergistic use of remote sensing and unsupervised machine learning has emerged as a potential tool for addressing a variety of environmental monitoring applications, such as detecting disaster-affected areas and deforestation. This paper proposes a new machine-intelligent approach to detecting and characterizing spatio-temporal changes on the Earth’s surface by using remote sensing data and unsupervised learning. Our framework was designed to be fully automatic by integrating unsupervised anomaly detection models, remote sensing image series, and open data extracted from the Google Earth Engine platform. The methodology was evaluated by taking both simulated and real-world environmental data acquired from several imaging sensors, including Landsat-8 OLI, Sentinel-2 MSI, and Terra MODIS. The experimental results were measured with the kappa and F1-score metrics, and they indicated an assertiveness level of 0.85 for the change detection task, demonstrating the accuracy and robustness of the proposed approach when addressing distinct environmental monitoring applications, including the detection of disaster-affected areas and deforestation mapping.

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Section: Analysis Configurationmentioning

confidence: 94%

Section: Anomaly Detectionmentioning

confidence: 99%

Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series

et al. 2023

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“…To evaluate our method, the NDVI threshold method, NDWI threshold method, FAI threshold method [9,17,35], RF (NDVI & NDWI) method, Anomalous Behavior Detection-RF (ABD-RF) method [48], WVA-RF method, and WVA-QPSO-RF-A method were used to extract cyanobacteria to compare with our model. The WVA-RF method used the same remote sensing indices as our method, but only used an RF model.…”

Section: Wva-qpso-rf Methodsmentioning

confidence: 99%

Spatio-Temporal Variation of Cyanobacteria Blooms in Taihu Lake Using Multiple Remote Sensing Indices and Machine Learning

Pan,

Yuan,

Yang

et al. 2024

Remote Sensing

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In view of the ecological threat posed by cyanobacteria blooms in Taihu Lake (China), this paper presents a study on the area of cyanobacteria extent based on MODIS data using the quantum particle swarm optimization–random forest (QPSO-RF) machine learning algorithm. This paper selects multiple remote sensing input indices that can represent the characteristics of the primary underlying type in Taihu Lake. The proposed method performs best, with an F1 score of 0.91–0.98. Based on this method, the spatio-temporal variation of cyanobacteria blooms in the Taihu Lake complex was analyzed. During 2010–2022, the average area of cyanobacteria blooms in Taihu Lake increased slightly. Severe-scale cyanobacteria blooms occurred in 2015–2019. Cyanobacteria blooms were normally concentrated from May to November. However, the most prolonged extended duration occurred in 2017, lasting for eight months. Spatially, cyanobacteria blooms were mainly identified in the northwestern part of Taihu Lake, with an average occurrence frequency of about 10.0%. The cyanobacteria blooms often began to grow in the northwestern part of the lake and then spread to the Center of the Lake, and also dissipated earliest in the northwestern part of the lake. Our study is also beneficial for monitoring the growth of cyanobacteria blooms in other similar large lakes in long time series.

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“…(2) Algae targets on the sea surface exhibit huge scale differences, necessitating the network to have a robust multi-scale feature learning capability. (3) The construction of the student network requires simultaneously achieving faster speeds and lower deployment costs. To address these challenges, we propose a real-time floating algae segmentation network based on the distillation theory using RGB images, called ADNet, designed to enhance performance while maintaining efficiency.…”

Section: Semantic Segmentation Distillationmentioning

confidence: 99%

“…The widespread presence of floating algae (Ulva prolifera and Sargassum) in the East China Sea poses a serious threat to the marine ecological economy [1][2][3]. Firstly, the dense growth of green algae obstructs the sunlight, impeding the photosynthesis of submerged organisms and thereby disrupting the marine food chain.…”

Section: Introductionmentioning

confidence: 99%

ADNet: A Real-Time Floating Algae Segmentation Using Distillation Network

Xu,

Wang

2024

JMSE

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The segmentation of floating algae is a hot topic in the field of marine environmental research. Given the vastness of coastal areas and complex environments, algae detection models must have both higher performance and lower deployment costs. However, relying solely on a single Convolutional Neural Network (CNN) or transformer structure fails to achieve this objective. In this paper, a novel real-time floating algae segmentation method using a distillation network (ADNet) is proposed, based on the RGB images. ADNet can effectively transfer the performance of the transformer-based teacher network to the CNN-based student model while preserving its lightweight design. Faced with complex marine environments, we introduce a novel Channel Purification Module (CPM) to simultaneously strengthen algae features and purify interference responses. Importantly, the CPM achieves this operation without increasing any learnable parameters. Moreover, considering the huge scale differences among algae targets in surveillance RGB images, we propose a lightweight multi-scale feature fusion network (L-MsFFN) to improve the student’s modeling ability across various scales. Additionally, to mitigate interference from low-level noises on higher-level semantics, a novel position purification module (PPM) is proposed. The PPM can achieve more accurate weight attention calculation between different pyramid levels, thereby enhancing the effectiveness of fusion. Compared to CNNs and transformers, our ADNet strikes an optimal balance between performance and speed. Extensive experimental results demonstrate that our ADNet achieves higher application performance in the field of floating algae monitoring tasks.

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Anomalous behaviour detection using one-class support vector machine and remote sensing images: a case study of algal bloom occurrence in inland waters

Cited by 16 publications

References 50 publications

Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series

Integrating Unsupervised Machine Intelligence and Anomaly Detection for Spatio-Temporal Dynamic Mapping Using Remote Sensing Image Series

Spatio-Temporal Variation of Cyanobacteria Blooms in Taihu Lake Using Multiple Remote Sensing Indices and Machine Learning

ADNet: A Real-Time Floating Algae Segmentation Using Distillation Network

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