Hyperspectral Remote Sensing Benchmark Database for Oil Spill Detection with an Isolation Forest-Guided Unsupervised Detector

• URL: http://arxiv.org/abs/2209.14971v1
• Date: Wed, 28 Sep 2022 02:26:42 GMT
• Title: Hyperspectral Remote Sensing Benchmark Database for Oil Spill Detection with an Isolation Forest-Guided Unsupervised Detector
• Authors: Puhong Duan and Xudong Kang and Pedram Ghamisi
• Abstract summary: Oil spill detection has attracted increasing attention in recent years since marine oil spill accidents severely affect environments, natural resources, and the lives of coastal inhabitants. Most of the existing approaches are based on supervised and semi-supervised frameworks to detect oil spills from hyperspectral images (HSIs) In this study, we make the first attempt to develop an unsupervised oil spill detection method based on isolation forest for HSIs.
• Score: 13.739881592455044
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Oil spill detection has attracted increasing attention in recent years since marine oil spill accidents severely affect environments, natural resources, and the lives of coastal inhabitants. Hyperspectral remote sensing images provide rich spectral information which is beneficial for the monitoring of oil spills in complex ocean scenarios. However, most of the existing approaches are based on supervised and semi-supervised frameworks to detect oil spills from hyperspectral images (HSIs), which require a huge amount of effort to annotate a certain number of high-quality training sets. In this study, we make the first attempt to develop an unsupervised oil spill detection method based on isolation forest for HSIs. First, considering that the noise level varies among different bands, a noise variance estimation method is exploited to evaluate the noise level of different bands, and the bands corrupted by severe noise are removed. Second, kernel principal component analysis (KPCA) is employed to reduce the high dimensionality of the HSIs. Then, the probability of each pixel belonging to one of the classes of seawater and oil spills is estimated with the isolation forest, and a set of pseudo-labeled training samples is automatically produced using the clustering algorithm on the detected probability. Finally, an initial detection map can be obtained by performing the support vector machine (SVM) on the dimension-reduced data, and then, the initial detection result is further optimized with the extended random walker (ERW) model so as to improve the detection accuracy of oil spills. Experiments on airborne hyperspectral oil spill data (HOSD) created by ourselves demonstrate that the proposed method obtains superior detection performance with respect to other state-of-the-art detection approaches.

Related papers

• Dynamic Loss Decay based Robust Oriented Object Detection on Remote Sensing Images with Noisy Labels [17.211935951030114]
  We propose a robust oriented remote sensing object detection method through dynamic loss decay (DLD) mechanism. Our solution has won the 2st place in the "fine-grained object detection based on sub-meter remote sensing imagery" track with noisy labels of 2023 National Big Data and Computing Intelligence Challenge.
  arXiv  Detail & Related papers  (2024-05-15T01:29:28Z)
• SoftPatch: Unsupervised Anomaly Detection with Noisy Data [67.38948127630644]
  This paper considers label-level noise in image sensory anomaly detection for the first time. We propose a memory-based unsupervised AD method, SoftPatch, which efficiently denoises the data at the patch level. Compared with existing methods, SoftPatch maintains a strong modeling ability of normal data and alleviates the overconfidence problem in coreset.
  arXiv  Detail & Related papers  (2024-03-21T08:49:34Z)
• Exploring Hyperspectral Anomaly Detection with Human Vision: A Small Target Aware Detector [20.845503528474328]
  Hyperspectral anomaly detection (HAD) aims to localize pixel points whose spectral features differ from the background. Existing HAD methods aim to objectively detect and distinguish background and anomalous spectra. In this paper, we analyze hyperspectral image (HSI) features under human visual perception. We propose a small target aware detector (STAD), which introduces saliency maps to capture HSI features closer to human visual perception.
  arXiv  Detail & Related papers  (2024-01-02T08:28:38Z)
• Exploring Sound vs Vibration for Robust Fault Detection on Rotating Machinery [13.480792901281047]
  This study presents the new benchmark Qatar University Dual-Machine Bearing Fault Benchmark dataset (QU-DMBF) We draw the focus on the major limitations and drawbacks of vibration-based fault detection due to numerous installation and operational conditions. A wide range of experimental results shows that the sound-based fault detection method is significantly more robust than its vibration-based counterpart.
  arXiv  Detail & Related papers  (2023-12-17T15:27:32Z)
• FS-BAND: A Frequency-Sensitive Banding Detector [55.59101150019851]
  Banding artifact, as known as staircase-like contour, is a common quality annoyance that happens in compression, transmission, etc. We propose a no-reference banding detection model to capture and evaluate banding artifacts, called the Frequency-Sensitive BANding Detector (FS-BAND) Experimental results show that the proposed FS-BAND method outperforms state-of-the-art image quality assessment (IQA) approaches with higher accuracy in banding classification task.
  arXiv  Detail & Related papers  (2023-11-30T03:20:42Z)
• Learning Heavily-Degraded Prior for Underwater Object Detection [59.5084433933765]
  This paper seeks transferable prior knowledge from detector-friendly images. It is based on statistical observations that, the heavily degraded regions of detector-friendly (DFUI) and underwater images have evident feature distribution gaps. Our method with higher speeds and less parameters still performs better than transformer-based detectors.
  arXiv  Detail & Related papers  (2023-08-24T12:32:46Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Evaluation of the potential of Near Infrared Hyperspectral Imaging for monitoring the invasive brown marmorated stink bug [53.682955739083056]
  The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops. The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens.
  arXiv  Detail & Related papers  (2023-01-19T11:37:20Z)
• Deep learning based automatic detection of offshore oil slicks using SAR data and contextual information [0.0]
  This paper presents the automation of offshore oil slicks on an extensive database with both kinds of slicks. It builds upon the slick annotations of specialized photo-interpreters on Sentinel-1 SAR data for 4 years over 3 exploration and monitoring areas worldwide. The main results of this study show the effectiveness of slick detection by deep learning approaches.
  arXiv  Detail & Related papers  (2022-04-13T13:30:16Z)
• Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain [88.7339322596758]
  We present a novel Spatial-Phase Shallow Learning (SPSL) method, which combines spatial image and phase spectrum to capture the up-sampling artifacts of face forgery. SPSL can achieve the state-of-the-art performance on cross-datasets evaluation as well as multi-class classification and obtain comparable results on single dataset evaluation.
  arXiv  Detail & Related papers  (2021-03-02T16:45:08Z)
• Large-scale detection and categorization of oil spills from SAR images with deep learning [4.716034416800441]
  We propose a deep learning framework to detect and categorize oil spills in synthetic aperture radar (SAR) images at a large scale. We also introduce a classification task, which is novel in the context of oil spill detection in SAR.
  arXiv  Detail & Related papers  (2020-06-24T09:32:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.