Hybrid DNN-Dirichlet Anomaly Detection and Ranking: Case of Burned Areas Discovery

Coca, Mihai; Neagoe, Iulia Coca; Datcu, Mihai

doi:10.1109/tgrs.2022.3207311

Cited by 8 publications

(5 citation statements)

References 45 publications

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“…The most common architectures for this task is the U-Net and simple multi-layer Convolutional Neural Networks (CNNs). Finally, a single work has tackled the task through an anomaly detection approach with a CNN trained in a self-supervised way to classify a whole image patch as burnt/unburnt [42].…”

Section: B Methods For Burnt Area Mappingmentioning

confidence: 99%

Deep Learning for Downscaling Remote Sensing Images: Fusion and super-resolution

Sdraka

Papoutsis

Psomas

et al. 2022

IEEE Geosci. Remote Sens. Mag.

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Over the last decade there has been an increasing frequency and intensity of wildfires across the globe, posing significant threats to human and animal lives, ecosystems, and socio-economic stability. Therefore urgent action is required to mitigate their devastating impact and safeguard Earth's natural resources. Robust Machine Learning methods combined with the abundance of high-resolution satellite imagery can provide accurate and timely mappings of the affected area in order to assess the scale of the event, identify the impacted assets and prioritize and allocate resources effectively for the proper restoration of the damaged region. In this work, we create and introduce a machine-learning ready dataset we name FLOGA (Forest wiLdfire Observations for the Greek Area). This dataset is unique as it comprises of satellite imagery acquired before and after a wildfire event, it contains information from Sentinel-2 and MODIS modalities with variable spatial and spectral resolution, and contains a large number of events where the corresponding burnt area ground truth has been annotated by domain experts. FLOGA covers the wider region of Greece, which is characterized by a Mediterranean landscape and climatic conditions. We use FLOGA to provide a thorough comparison of multiple Machine Learning and Deep Learning algorithms for the automatic extraction of burnt areas, approached as a change detection task. We also compare the results to those obtained using standard specialized spectral indices for burnt area mapping. Finally, we propose a novel Deep Learning model, namely BAM-CD. Our benchmark results demonstrate the efficacy of the proposed technique in the automatic extraction of burnt areas, outperforming all other methods in terms of accuracy and robustness. Our dataset and code are publicly available at: https://github.com/Orion-AI-Lab/FLOGA.

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Section: B Methods For Burnt Area Mappingmentioning

confidence: 99%

Deep Learning for Downscaling Remote Sensing Images: Fusion and super-resolution

Sdraka

Papoutsis

Psomas

et al. 2022

IEEE Geosci. Remote Sens. Mag.

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“…To generate the ground truth (GT) map, i.e., to identify burned and unburned patches, necessary to evaluate the proposed method, a classical spectral indices method was utilized. The way the classical spectral index method is applied is described in [10]. The proposed datasets were described below.…”

Section: B Proposed Datasetsmentioning

confidence: 99%

“…Event detection in earth science is often critical for immediately addressing negative impacts on natural resources, e.g., drought-related vegetation disturbances [5], devastating floods [8], active fire detection [9]. Wildfire is the most extreme natural hazard that has caused serious damages to human safety and natural ecosystems in recent years, i.e., Australian areas affected by wildfire in 2019 [10], ravage outbreak of wildfires in Bolivia in 2019 [11], large fire events in California in 2020 [12]. This disastrous event has an annual cycle in predisposed places, but new places are constantly appearing around the world due to climate change.…”

Section: Introductionmentioning

confidence: 99%

FPGA Accelerator for Meta-Recognition Anomaly Detection: Case of Burned Area Detection

Coca

Datcu

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Optical remote sensing instruments accumulate abundant data from across all of the earth's land surfaces, making it possible both to understand the effects of climate change and to monitor, investigate, and manage ground-level events in detail. Processing data using resources located near on-board satellite sensors can bring major benefits in terms of minimizing analysis time and quickly initiating active actions in critical situations. In satellite missions, long-term production on-board algorithms may encounter unexplored samples, i.e., abnormal ground-level events, and need to be able to discriminate and take the correct action. In this matter, the authors present a field programmable gate array (FPGA)-based solution for natural anomaly detection in multispectral imagery using deep convolutional neural networks. The effects of weather-induced hazards and natural disasters, considered anomalies in this sense, are discovered by modeling an anomaly detector on a hybrid system that is hardware efficient. The proposed approach is assembled on a Xilinx Zynq UltraScale+ XCZU9EG multiprocessor system-on-chip (MPSoC) device, where a deep convolutional model is scaled into the FPGA logic, followed by a downstream statistical meta-recognition predictor. The proposed anomaly detection accelerator has produced notable results in identifying a contemporary natural hazard, i.e., burned areas, in scenes acquired by Sentinel-2 over Europe, i.e., Spain and France. The implemented algorithm achieved on the FPGA accelerator an equivalent speedup of 4.46× and 4.5× lower power consumption than the equivalent implementation on the Tesla K80 GPU.

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“…Specifically, they fully utilized the underlying physical characteristics to train the unsupervised network [22]. Mihai et al proposed a deep convolutional model to detect anomalies in burned area contexts of Sentinel-2 scenes, which adopt a selfsupervised paradigm to learn the image representations [23]. The convolution neural network (CNN) is the typical representative of deep learning, which needs sufficient samples for supervised training [24], [25].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection of Hyperspectral Images Based on Transformer With Spatial–Spectral Dual-Window Mask

Xiao

Zhang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hybrid DNN-Dirichlet Anomaly Detection and Ranking: Case of Burned Areas Discovery

Cited by 8 publications

References 45 publications

Deep Learning for Downscaling Remote Sensing Images: Fusion and super-resolution

Deep Learning for Downscaling Remote Sensing Images: Fusion and super-resolution

FPGA Accelerator for Meta-Recognition Anomaly Detection: Case of Burned Area Detection

Anomaly Detection of Hyperspectral Images Based on Transformer With Spatial–Spectral Dual-Window Mask

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