IMAFD: An Interpretable Multi-stage Approach to Flood Detection from time series Multispectral Data

• URL: http://arxiv.org/abs/2405.07916v1
• Date: Mon, 13 May 2024 16:47:53 GMT
• Title: IMAFD: An Interpretable Multi-stage Approach to Flood Detection from time series Multispectral Data
• Authors: Ziyang Zhang, Plamen Angelov, Dmitry Kangin, Nicolas Longépé,
• Abstract summary: We propose an interpretable multi-stage approach to flood detection, IMAFD. It provides an automatic, efficient and interpretable solution suitable for large-scale remote sensing tasks. For all the above datasets, the proposed framework demonstrates a competitive performance compared to other methods offering also interpretability and insight.
• Score: 9.960331908396379
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we address two critical challenges in the domain of flood detection: the computational expense of large-scale time series change detection and the lack of interpretable decision-making processes on explainable AI (XAI). To overcome these challenges, we proposed an interpretable multi-stage approach to flood detection, IMAFD has been proposed. It provides an automatic, efficient and interpretable solution suitable for large-scale remote sensing tasks and offers insight into the decision-making process. The proposed IMAFD approach combines the analysis of the dynamic time series image sequences to identify images with possible flooding with the static, within-image semantic segmentation. It combines anomaly detection (at both image and pixel level) with semantic segmentation. The flood detection problem is addressed through four stages: (1) at a sequence level: identifying the suspected images (2) at a multi-image level: detecting change within suspected images (3) at an image level: semantic segmentation of images into Land, Water or Cloud class (4) decision making. Our contributions are two folder. First, we efficiently reduced the number of frames to be processed for dense change detection by providing a multi-stage holistic approach to flood detection. Second, the proposed semantic change detection method (stage 3) provides human users with an interpretable decision-making process, while most of the explainable AI (XAI) methods provide post hoc explanations. The evaluation of the proposed IMAFD framework was performed on three datasets, WorldFloods, RavAEn and MediaEval. For all the above datasets, the proposed framework demonstrates a competitive performance compared to other methods offering also interpretability and insight.

Related papers

• Mismatched: Evaluating the Limits of Image Matching Approaches and Benchmarks [9.388897214344572]
  Three-dimensional (3D) reconstruction from two-dimensional images is an active research field in computer vision. Traditionally, parametric techniques have been employed for this task. Recent advancements have seen a shift towards learning-based methods.
  arXiv  Detail & Related papers  (2024-08-29T11:16:34Z)
• Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange [44.845959222180866]
  We propose an unsupervised single-temporal CD framework based on intra- and inter-image patch exchange (I3PE) The I3PE framework allows for training deep change detectors on unpaired and unlabeled single-temporal remote sensing images. I3PE outperforms representative unsupervised approaches and achieves F1 value improvements of 10.65% and 6.99% to the SOTA method.
  arXiv  Detail & Related papers  (2023-10-01T14:50:54Z)
• COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection [56.7599217711363]
  Face forgery recognition methods can only process one face at a time. Most face forgery recognition methods can only process one face at a time. We propose COMICS, an end-to-end framework for multi-face forgery detection.
  arXiv  Detail & Related papers  (2023-08-03T03:37:13Z)
• MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential Deepfake Detection [81.59191603867586]
  Sequential deepfake detection aims to identify forged facial regions with the correct sequence for recovery. The recovery of forged images requires knowledge of the manipulation model to implement inverse transformations. We propose Multi-Collaboration and Multi-Supervision Network (MMNet) that handles various spatial scales and sequential permutations in forged face images.
  arXiv  Detail & Related papers  (2023-07-06T02:32:08Z)
• Learning to search for and detect objects in foveal images using deep learning [3.655021726150368]
  This study employs a fixation prediction model that emulates human objective-guided attention of searching for a given class in an image. The foveated pictures at each fixation point are then classified to determine whether the target is present or absent in the scene. We present a novel dual task model capable of performing fixation prediction and detection simultaneously, allowing knowledge transfer between the two tasks.
  arXiv  Detail & Related papers  (2023-04-12T09:50:25Z)
• Active Gaze Control for Foveal Scene Exploration [124.11737060344052]
  We propose a methodology to emulate how humans and robots with foveal cameras would explore a scene. The proposed method achieves an increase in detection F1-score of 2-3 percentage points for the same number of gaze shifts.
  arXiv  Detail & Related papers  (2022-08-24T14:59:28Z)
• Object Class Aware Video Anomaly Detection through Image Translation [1.2944868613449219]
  This paper proposes a novel two-stream object-aware VAD method that learns the normal appearance and motion patterns through image translation tasks. The results show that, as significant improvements to previous methods, detections by our method are completely explainable and anomalies are localized accurately in the frames.
  arXiv  Detail & Related papers  (2022-05-03T18:04:27Z)
• Unsupervised Change Detection in Hyperspectral Images using Feature Fusion Deep Convolutional Autoencoders [15.978029004247617]
  The proposed work aims to build a novel feature extraction system using a feature fusion deep convolutional autoencoder. It is found that the proposed method clearly outperformed the state of the art methods in unsupervised change detection for all the datasets.
  arXiv  Detail & Related papers  (2021-09-10T16:52:31Z)
• LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation [52.63874513999119]
  Cross-resolution image alignment is a key problem in multiscale giga photography. Existing deep homography methods neglecting the explicit formulation of correspondences between them, which leads to degraded accuracy in cross-resolution challenges. We propose a local transformer network embedded within a multiscale structure to explicitly learn correspondences between the multimodal inputs.
  arXiv  Detail & Related papers  (2021-06-08T02:51:45Z)
• M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection [74.19291916812921]
  forged images generated by Deepfake techniques pose a serious threat to the trustworthiness of digital information. In this paper, we aim to capture the subtle manipulation artifacts at different scales for Deepfake detection. We introduce a high-quality Deepfake dataset, SR-DF, which consists of 4,000 DeepFake videos generated by state-of-the-art face swapping and facial reenactment methods.
  arXiv  Detail & Related papers  (2021-04-20T05:43:44Z)
• Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders [81.30960319178725]
  We propose DivNoising, a denoising approach based on fully convolutional variational autoencoders (VAEs) First we introduce a principled way of formulating the unsupervised denoising problem within the VAE framework by explicitly incorporating imaging noise models into the decoder. We show that such a noise model can either be measured, bootstrapped from noisy data, or co-learned during training.
  arXiv  Detail & Related papers  (2020-06-10T21:28:13Z)

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.