In the fast-growing field of Remote Sensing (RS) image analysis, the gap
between massive unlabeled datasets and the ability to fully utilize these
datasets for advanced RS analytics presents a significant challenge. To fill
the gap, our work introduces an innovative auto-labeling framework named ALPS
(Automatic Labeling for Pre-training in Segmentation), leveraging the Segment
Anything Model (SAM) to predict precise pseudo-labels for RS images without
necessitating prior annotations or additional prompts. The proposed pipeline
significantly reduces the labor and resource demands traditionally associated
with annotating RS datasets. By constructing two comprehensive pseudo-labeled
RS datasets via ALPS for pre-training purposes, our approach enhances the
performance of downstream tasks across various benchmarks, including iSAID and
ISPRS Potsdam. Experiments demonstrate the effectiveness of our framework,
showcasing its ability to generalize well across multiple tasks even under the
scarcity of extensively annotated datasets, offering a scalable solution to
automatic segmentation and annotation challenges in the field. In addition, the
proposed a pipeline is flexible and can be applied to medical image
segmentation, remarkably boosting the performance. Note that ALPS utilizes
pre-trained SAM to semi-automatically annotate RS images without additional
manual annotations. Though every component in the pipeline has bee well
explored, integrating clustering algorithms with SAM and novel pseudo-label
alignment significantly enhances RS segmentation, as an off-the-shelf tool for
pre-training data preparation. Our source code is available at:
this https URL

我们提出了一种名为 ALPS 的创新自动标注框架，利用 Segment Anything Model (SAM) 预测遥感图像的准确伪标签，从而填补了遥感图像分析领域中海量未标注数据集和充分利用这些数据集进行高级 RS 分析之间的鸿沟。

ALPS：遥感分割的自动标注和预训练方案

ALPS: An Auto-Labeling and Pre-training Scheme for Remote Sensing  Segmentation With Segment Anything Model

Deep neural networks (DNNs) have achieved tremendous success in many remote
sensing (RS) applications. However, their vulnerability to the threat of
adversarial perturbations should not be neglected. Unfortunately, current
adversarial defense approaches in RS studies usually suffer from performance
fluctuation and unnecessary re-training costs due to the need for prior
knowledge of the adversarial perturbations among RS data. To circumvent these
challenges, we propose a universal adversarial defense approach in RS imagery
(UAD-RS) using pre-trained diffusion models to defend the common DNNs against
multiple unknown adversarial attacks. Specifically, the generative diffusion
models are first pre-trained on different RS datasets to learn generalized
representations in various data domains. After that, a universal adversarial
purification framework is developed using the forward and reverse process of
the pre-trained diffusion models to purify the perturbations from adversarial
samples. Furthermore, an adaptive noise level selection (ANLS) mechanism is
built to capture the optimal noise level of the diffusion model that can
achieve the best purification results closest to the clean samples according to
their Frechet Inception Distance (FID) in deep feature space. As a result, only
a single pre-trained diffusion model is needed for the universal purification
of adversarial samples on each dataset, which significantly alleviates the
re-training efforts for each attack setting and maintains high performance
without the prior knowledge of adversarial perturbations. Experiments on four
heterogeneous RS datasets regarding scene classification and semantic
segmentation verify that UAD-RS outperforms state-of-the-art adversarial
purification approaches with a universal defense against seven commonly
existing adversarial perturbations.

利用预训练扩散模型进行通用对抗防御，净化遥感数据中的对抗干扰，不需要事先了解对抗干扰性质，提高性能并减少重新训练的工作量。