HiSem: Hierarchical Semantic Disentangling for Remote Sensing Image Change Captioning
Authors: Man Wang, Chenyang Liu, Wenjun Li, Feng Ni, Bing Jia, Baoqi Huang, Riting Xia, Zhenwei Shi
Abstract
Remote sensing image change captioning (RSICC) aims to achieve high-level semantic understanding of genuine changes occurring between bi-temporal images. Despite notable progress, existing methods are fundamentally limited by a shared modeling assumption: changed and unchanged image pairs, which have intrinsically different semantic granularities, are processed under a unified modeling strategy. This modeling inconsistency leads to semantic entanglement between coarse-grained change existence judgment and fine-grained semantic understanding.To address the above limitation, we propose a novel hierarchical semantic disentangling network (HiSem) that explicitly disentangles semantic representations of different granularities. Specifically, we first introduce the Bidirectional Differential Attention Modulation (BDAM) module that leverages discrepancy-aware attention to enhance cross-temporal interactions, thereby amplifying true change signals while suppressing irrelevant variations. Building upon this, we design a Hierarchical Adaptive Semantic Disentanglement (HASD) module that performs adaptive routing at two hierarchical levels: a coarse-grained image-level routing mechanism distinguishes changed and unchanged image pairs, while a fine-grained token-level Mixture-of-Experts (MoE) block models diverse and heterogeneous change semantics for changed samples. Extensive experiments on two benchmark datasets demonstrate that HiSem outperfoms previous methods, achieving a significant improvement of +7.52% BLEU-4 on the WHU-CDC dataset. More importantly, our approach provides a structured perspective for RSICC by explicitly aligning model design with the intrinsic semantic heterogeneity of bi-temporal scenes. The code will be available at https://github.com/Man-Wang-star/HiSem
Remote sensing image change captioning (RSICC) converts bitemporal imagery into a sentence describing semantic changes. Most RSICC methods condition caption decoders directly on fused visual features, leaving intermediate change structure and decoder-interface consistency less studied. We present HIMEC, combining Directional Change Representation (DCR) with fixed-interface decoding. DCR separates signed differences into appearance-oriented, disappearance-oriented, and shared-context streams before fusion. A learned-query encoder converts the fused representation into visually conditioned change-query tokens that form the scene decoder's only sample-dependent memory. A training-only auxiliary phrase decoder supplies caption-derived supervision. With a fixed zero input, the scene decoder maintains the same interface during training and inference. Separately, we evaluate a local-to-scene cascade conditioned on teacher-forced local states during training and autoregressive states at inference. On changed LEVIR-CC validation pairs, these states have a mean cosine distance of 0.69. Regime-matched conditioning recovers most of the associated deficit, whereas permuting state correspondence causes no detectable penalty. These findings are limited to the evaluated cascade. In a matched three-seed comparison, HIMEC reaches a Consensus-based Image Description Evaluation (CIDEr) score of 142.81±0.60 on LEVIR-CC, versus 139.51±3.40 for direct fused-feature memory. On SECOND-CC, fixed-zero and regime-matched diagnostic conditioning reach 75.67 and 76.99 CIDEr, respectively, versus 60.77 for the mismatched cascade. The source code will be made publicly available at https://github.com/ayshaashra/HIMEC upon publication.
Aysha Ashraf, Shaina Ashraf, Wafaa I. M. Hussin +3
Remote sensing image change captioning (RSICC) aims to describe the difference between two remote sensing images. While recent methods have explored video modeling, they largely overlook the inherent ambiguities in viewpoint, scale, and prior knowledge, lacking effective constraints on the encoder. In this paper, we present STAND, a Semantic Anchoring Constraint with Dual-Granularity Disambiguation for RSICC, to progressively resolve these ambiguities. Specifically, to establish a reliable feature foundation, we first introduce an interpretable constraint to regularize temporal representations. Operating on these purified features, a dual-granularity disambiguation module resolves spatial uncertainties by coupling macro-level global context aggregation for viewpoint confusion with micro-level frequency-refocused attention for small-object scale enhancement. Ultimately, to translate these visually disambiguated features into precise text, a semantic concept anchoring module leverages language categorical priors to tackle knowledge ambiguity during decoding. Extensive experiments verify the superiority of STAND and its effectiveness in addressing ambiguities.
Remote Sensing Image Change Captioning (RSICC) aims to describe changes between bi-temporal remote sensing images and holds significant research and application value. However, most existing methods rely on conventional deep learning architectures, and the limited model capacity constrains performance. Although large-model post-training techniques have achieved great success in general domains, their direct transfer to RSICC remains challenging due to data scarcity and the need for fine-grained change understanding. To address this, we propose RSICCLLM, the first post-training framework for large vision-language models in RSICC. Specifically, we design a data generation paradigm, release the instruction dataset RSICI, and establish a task-specific RSICC benchmark. We further introduce Difference-aware Supervised Fine-tuning to explicitly extract change representations and guide the model in perceiving and understanding temporal differences. In addition, we propose Dual-Negative Preference Optimization (DNPO), which employs two complementary negative-sample construction strategies to construct the preference dataset RSICP and further refine model performance. Extensive experiments validate the superior capability of RSICCLLM, which achieves outstanding results with only 7B parameters, surpassing models of substantially larger scales. The code and dataset will be made publicly available at https://github.com/keaill/RSICCLLM.