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.

我们提出了一种可解释的多阶段洪水检测方法，将动态时间序列图像序列分析与静态语义分割相结合，通过四个阶段解决洪水检测问题，提供了高效准确的解决方案，并且可以提供决策过程的见解。

IMAFD：一种从时间序列多光谱数据中进行洪水检测的可解释的多阶段方法

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

The detection of flooded areas using high-resolution synthetic aperture radar
(SAR) imagery is a critical task with applications in crisis and disaster
management, as well as environmental resource planning. However, the complex
nature of SAR images presents a challenge that often leads to an overestimation
of the flood extent. To address this issue, we propose a novel differential
attention metric-based network (DAM-Net) in this study. The DAM-Net comprises
two key components: a weight-sharing Siamese backbone to obtain multi-scale
change features of multi-temporal images and tokens containing high-level
semantic information of water-body changes, and a temporal differential fusion
(TDF) module that integrates semantic tokens and change features to generate
flood maps with reduced speckle noise. Specifically, the backbone is split into
multiple stages. In each stage, we design three modules, namely, temporal-wise
feature extraction (TWFE), cross-temporal change attention (CTCA), and
temporal-aware change enhancement (TACE), to effectively extract the change
features. In TACE of the last stage, we introduce a class token to record
high-level semantic information of water-body changes via the attention
mechanism. Another challenge faced by data-driven deep learning algorithms is
the limited availability of flood detection datasets. To overcome this, we have
created the S1GFloods open-source dataset, a global-scale high-resolution
Sentinel-1 SAR image pairs dataset covering 46 global flood events between 2015
and 2022. The experiments on the S1GFloods dataset using the proposed DAM-Net
showed top results compared to state-of-the-art methods in terms of overall
accuracy, F1-score, and IoU, which reached 97.8%, 96.5%, and 93.2%,
respectively. Our dataset and code will be available online at
this https URL

本文提出一种新的基于差分注意力度量的网络 --DAM-Net，用于检测洪水区域。DAM-Net 包括两个关键组件：一个共享权重的双子背骨结构，用于获得多时相图像的多尺度变化特征和具有高级语义信息的表示水体变化的令牌，以及一个融合语义令牌和变化特征的时态差分融合模块，以生成减少斑点噪声的洪水地图。通过在提出的 S1GFloods 数据集上进行实验，证明 DAM-Net 相比当前最先进的方法在总体精度、F1 分数和 IoU 三个指标上都有明显提升。