From Prompts to Self-Prompts: Parameter-Efficient Multi-Label Remote Sensing via Mask-Guided Classification

Remote Sensing 2026 Open access

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Authors: Ge Qu, Xiongwei Guan, Fei Wen, Xinyu Zou
DOI: 10.3390/rs18030518
Cited by: 1
References: 33

Abstract

Multi-label remote sensing scene classification (MLRSSC) requires autonomous discovery of all relevant land-cover categories without human guidance. Conventional expert classifiers return only label vectors without spatial evidence, while foundation segmenters (e.g., SAM, RemoteSAM) remain passively dependent on external prompts—misaligned with autonomous interpretation. We introduce SAFI-XRS, a parameter-efficient self-prompted framework that transforms passive prompting into active scene parsing. By training only

Fields of Study & Subjects

Remote-Sensing Image Classification Domain Adaptation and Few-Shot Learning Machine Learning and Data Classification

Publication details

Journal / source
Remote Sensing
Publisher
MDPI AG
Volume
18
Issue
3
Pages
518–518
Publication date
5 February 2026
Language
EN