This work introduces composed image retrieval to remote sensing. It allows to
query a large image archive by image examples alternated by a textual
description, enriching the descriptive power over unimodal queries, either
visual or textual. Various attributes can be modified by the textual part, such
as shape, color, or context. A novel method fusing image-to-image and
text-to-image similarity is introduced. We demonstrate that a vision-language
model possesses sufficient descriptive power and no further learning step or
training data are necessary. We present a new evaluation benchmark focused on
color, context, density, existence, quantity, and shape modifications. Our work
not only sets the state-of-the-art for this task, but also serves as a
foundational step in addressing a gap in the field of remote sensing image
retrieval. Code at: this https URL

本文介绍了一种应用于遥感的组合图像检索方法，该方法允许通过图像示例和文本描述在大型图像库中进行查询，丰富了对单模态查询（无论是视觉还是文本）的描述能力。我们介绍了一种融合了图像 - 图像和文本 - 图像相似度的新方法，证明了视觉 - 语言模型具有足够的描述能力，无需进一步的学习步骤或训练数据。我们提出了一个新的评估基准，重点关注颜色、上下文、密度、存在性、数量和形状的修改。本工作不仅在此任务中取得了最新的技术成果，同时也填补了遥感图像检索领域的空白。

遥感图像的组合检索

Composed Image Retrieval for Remote Sensing

Image retrieval enables an efficient search through vast amounts of satellite
imagery and returns similar images to a query. Deep learning models can
identify images across various semantic concepts without the need for
annotations. This work proposes to use Geospatial Foundation Models, like
Prithvi, for remote sensing image retrieval with multiple benefits: i) the
models encode multi-spectral satellite data and ii) generalize without further
fine-tuning. We introduce two datasets to the retrieval task and observe a
strong performance: Prithvi processes six bands and achieves a mean Average
Precision of 97.62\% on BigEarthNet-43 and 44.51\% on ForestNet-12,
outperforming other RGB-based models. Further, we evaluate three compression
methods with binarized embeddings balancing retrieval speed and accuracy. They
match the retrieval speed of much shorter hash codes while maintaining the same
accuracy as floating-point embeddings but with a 32-fold compression. The code
is available at this https URL

使用地理空间基础模型 Prithvi 进行遥感图像检索，在多光谱卫星数据上取得了出色的性能，提出了三种压缩方法，能够在保持浮点数嵌入精度的同时实现 32 倍的压缩比，而且与更短哈希码的检索速度相匹配。

利用地理空间基础模型的多光谱遥感图像检索

Multi-Spectral Remote Sensing Image Retrieval Using Geospatial  Foundation Models

Remote sensing image retrieval(RSIR), which aims to efficiently retrieve data
of interest from large collections of remote sensing data, is a fundamental
task in remote sensing. Over the past several decades, there has been
significant effort to extract powerful feature representations for this task
since the retrieval performance depends on the representative strength of the
features. Benchmark datasets are also critical for developing, evaluating, and
comparing RSIR approaches. Current benchmark datasets are deficient in that 1)
they were originally collected for land use/land cover classification and not
image retrieval, 2) they are relatively small in terms of the number of classes
as well the number of sample images per class, and 3) the retrieval performance
has saturated. These limitations have severely restricted the development of
novel feature representations for RSIR, particularly the recent deep-learning
based features which require large amounts of training data. We therefore
present in this paper, a new large-scale remote sensing dataset termed
"PatternNet" that was collected specifically for RSIR. PatternNet was collected
from high-resolution imagery and contains 38 classes with 800 images per class.
We also provide a thorough review of RSIR approaches ranging from traditional
handcrafted feature based methods to recent deep learning based ones. We
evaluate over 35 methods to establish extensive baseline results for future
RSIR research using the PatternNet benchmark.

本研究提供了一种新型用于遥感图像检索 (RSIR) 的大规模数据集称为 PatternNet, 同时对传统的手工特征方法和近期的深度学习方法进行了全面评估，以建立 PatternNet 基准测试结果作为未来遥感数据检索算法研究的基础。