Knowledge-Guided Few-Shot Earth Surface Anomalies Detection

JI Hong; GAO Zhi; CHEN Boan; AO Wei; CAO Min; WANG Qiao

doi:10.11999/JEIT251000

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JI Hong, GAO Zhi, CHEN Boan, AO Wei, CAO Min, WANG Qiao. Knowledge-Guided Few-Shot Earth Surface Anomalies Detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251000

Citation:

JI Hong, GAO Zhi, CHEN Boan, AO Wei, CAO Min, WANG Qiao. Knowledge-Guided Few-Shot Earth Surface Anomalies Detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251000

JI Hong, GAO Zhi, CHEN Boan, AO Wei, CAO Min, WANG Qiao. Knowledge-Guided Few-Shot Earth Surface Anomalies Detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251000

Citation:

JI Hong, GAO Zhi, CHEN Boan, AO Wei, CAO Min, WANG Qiao. Knowledge-Guided Few-Shot Earth Surface Anomalies Detection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251000

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Knowledge-Guided Few-Shot Earth Surface Anomalies Detection

doi: 10.11999/JEIT251000 cstr: 32379.14.JEIT251000

JI Hong¹,
GAO Zhi^{2
,
,},
CHEN Boan²,
AO Wei²,
CAO Min³,
WANG Qiao⁴

1.
College of Geomatics, Xi'an University of Science and Technology, Xi’an 710054, China
2.
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
3.
Wuhan Guanggu Zoyon Science and Technology Company Ltd., Wuhan 430223, China
4.
Beijing Normal University, Beijing 100091, China

Funds: The National Natural Science Foundation of China Major Program (42192580, 42192583), The National Natural Science Foundation of China (42501503)

Received Date: 2025-09-26
Accepted Date: 2025-11-05
Rev Recd Date: 2025-11-05

Available Online: 2025-11-13

Abstract

Abstract

Objective Earth Surface Anomalies (ESAs), referring to sudden natural or human-induced disasters on the Earth’s surface, pose severe risks and widespread impacts. Timely and accurate earth surface anomalies detection is therefore crucial for social security and sustainable development. Remote sensing provides an effective means for earth surface anomalies detection. However, the performance of existing deep learning models remains constrained due to the scarcity of labeled data, the complexity of anomaly backgrounds, and the distribution shift across multi-source remote sensing imagery. To address these challenges, this paper proposes a knowledge-guided few-shot learning method. The method leverages large language models to generate abstract textual descriptions of normal and anomalous geospatial features, which are encoded and fused with visual prototypes to form a cross-modal joint representation. This integration improves prototype discriminability in few-shot settings and demonstrates the necessity of incorporating linguistic knowledge into earth surface anomalies detection, offering a promising direction for reliable disaster monitoring when annotated data are scarce. Methods The knowledge-guided few-shot learning method is built on a metric-based paradigm, where each episode consists of support and query sets and classification is achieved by comparing query features with class prototypes using distance-based similarity and cross-entropy optimization (Figure 1). To supplement limited visual prototypes, class-level textual descriptions are generated with ChatGPT through carefully designed prompts, producing semantic sentences that characterize the appearance, attributes, and contextual relations of both normal and anomalous categories (Figures 2–3). These descriptions encode domain-specific properties such as anomaly extent, morphology, and environmental impact, which are otherwise difficult to capture with scarce visual samples. The sentences are encoded with a CLIP (Contrastive Language–Image Pre-training) text encoder, and task-adaptive soft prompts are introduced by generating tokens from support features and concatenating them with static embeddings, yielding adaptive word embeddings. Encoded sentence vectors are then processed by a lightweight self-attention module to model dependencies across multiple descriptions, resulting in a coherent paragraph-level semantic representation (Figure 4). The obtained semantic prototypes are fused with the visual prototypes through weighted addition, producing cross-modal prototypes that combine visual grounding and linguistic abstraction. During training, query samples are compared with cross-modal prototypes, and optimization is guided by two objectives: a classification loss that enforces accurate query–prototype alignment, and a prototype regularization loss that ensures semantic prototypes are discriminative and well separated. The entire process is implemented in an episodic training framework (Algorithm 1). Results and Discussions The proposed method is evaluated under both cross-domain and in-domain few-shot settings. In the cross-domain case, models are trained on NWPU45 or AID and tested on ESAD to assess earth surface anomalies recognition. As shown in the comparisons (Table 2), traditional meta-learning methods such as MAML and Meta-SGD achieve accuracies below 50%, while metric-based baselines like ProtoNet and RelationNet are more stable but still limited. The proposed method reach 61.99% on NWPU45→ESAD and 59.79% on AID→ESAD settings, outperforming ProtoNet by 4.72% and 2.67% respectively. In the in-domain setting, training and testing on the same dataset, the method achieve 76.94% on NWPU45 and 72.98% on AID, consistently surpassing state-of-the-art baselines such as S2M2, and IDLN (Table 3). Ablation experiments further validate the contribution of each component. Using only visual prototypes produce accuracies of 57.74% and 72.16%, while progressively incorporating simple class names, task-oriented templates, and ChatGPT-generated descriptions improved results. The best performance is obtained by combining ChatGPT descriptions, learnable tokens, and attention-based mechanism, reaching 61.99% and 76.94% (Table 4). Parameter sensitivity analysis confirms that an appropriate weight for language features ($\alpha $=0.2) and two learnable tokens yield optimal performance (Figure 5). Conclusions This paper addresses the task of earth surface anomalies detection in remote sensing imagery by introducing a knowledge-guided few-shot learning method. The method exploits large language models to automatically generate abstract textual descriptions for both anomaly categories and conventional remote sensing scenes, thereby constructing multimodal training and testing resources. These descriptions are encoded into semantic feature vectors through a pretrained text encoder. To extract task-specific knowledge, a dynamic token learning strategy is designed, in which a small number of learnable parameters are guided by visual samples within few-shot tasks to generate adaptive semantic vectors. An attention-based semantic knowledge module then models dependencies among language features, producing cross-modal semantic vectors for each class. By fusing these vectors with visual prototypes, the method forms joint multimodal representations that are used for query–prototype matching and network optimization. Experimental evaluations demonstrate that the proposed method effectively leverages prior knowledge from pretrained models, compensates for the limitations of scarce visual data, and enhances feature discriminability for anomalies recognition. Both cross-domain and in-domain results confirm that the method achieves consistent improvements over competitive baselines, highlighting its potential for reliable application in real-world remote sensing anomalies detection scenarios.
- Earth Surface Anomalies,
- Remote Sensing Imagery,
- Few-Shot Learning,
- Language Knowledge,
- Multimodal Features

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References(37)

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