In this article, we first tackle a more realistic domain adaptation (DA) setting: source-free blending-target DA (SF-BTDA), where we cannot access to source-domain data while facing mixed multiple target domains without any domain labels in prior. Compared to existing DA scenarios, SF-BTDA generally faces the coexistence of different label shifts in different targets, along with noisy target pseudolabels generated from the source model. In this article, we propose a new method called evidential graph contrastive alignment (EGCA) to decouple the blending-target domain and alleviate the effect of noisy target pseudolabels. First, to improve the quality of pseudo target labels, we propose a calibrated evidential learning (CEL) module to iteratively improve both the accuracy and certainty of the resulting model and adaptively generate high-quality pseudo target labels. Second, we design a graph contrastive learning with the domain distance matrix and confidence-uncertainty criterion, to minimize the distribution gap of samples of the same class in the blending-target domain, which alleviates the coexistence of different label shifts in blended targets. We conduct a new benchmark based on three standard DA datasets, and EGCA outperforms other methods with considerable gains and achieves comparable results compared with those that have domain labels or source data in prior.

Self-training is widely used in unsupervised domain adaptation (UDA) by assigning pseudo labels to unlabeled samples. However, existing self-training strategies bring bias, while potentially inaccurate pseudo labels may accumulate errors during self-training (self-training shift) and the inability to accurately distinguish features may bring prediction bias (class shift). To address these issues, we propose Shift-Driven Learning (SDL). First, we decouple the generation and utilization of pseudo labels to mitigate the direct error accumulation. Second, we measure the maximum training shift of data, where the classifier achieves high accuracy on labeled data while making as many mistakes as possible on unlabeled data. Then we adversarially optimize the feature representations generation to indirectly decrease the self-training shift. Third, we minimize the class shift by data rearrangement strategy and joint contrastive learning, which find class-level discriminative feature representations. Extensive experiments justify that SDL outperforms SOTA methods on three UDA datasets with considerable gains.

In this paper, we firstly tackle a more realistic Domain Adaptation (DA) setting: Source-Free Blending-Target Domain Adaptation (SF-BTDA), where we can not access to source domain data while facing mixed multiple target domains without any domain labels in prior. Compared to existing DA scenarios, SF-BTDA generally faces the co-existence of different label shifts in different targets, along with noisy target pseudo labels generated from the source model. In this paper, we propose a new method called Evidential Graph Contrastive Alignment (EGCA) to decouple the blending target domain and alleviate the effect from noisy target pseudo labels. First, to improve the quality of pseudo target labels, we propose a Calibrated Evidential Learning module to iteratively improve both the accuracy and certainty of the resulting model and adaptively generate high-quality pseudo target labels. Second, we design a Graph Contrastive Learning with the domain distance matrix and confidence-uncertainty criterion, to minimize the distribution gap of samples of a same class in the blended target domains, which alleviates the co-existence of different label shifts in blended targets. We conduct a new benchmark based on three standard DA datasets and EGCA outperforms other methods with considerable gains and achieves comparable results compared with those have domain labels or source data in prior.

Large Multimodal Models (LMMs) has demonstrated capabilities across various domains, but comprehensive benchmarks for agricultural remote sensing (RS) remain scarce. Existing benchmarks designed for agricultural RS scenarios exhibit notable limitations, primarily in terms of insufficient scene diversity in the dataset and oversimplified task design. To bridge this gap, we introduce AgroMind, a comprehensive agricultural remote sensing benchmark covering four task dimensions: spatial perception, object understanding, scene understanding, and scene reasoning, with a total of 13 task types, ranging from crop identification and health monitoring to environmental analysis. We curate a high-quality evaluation set by integrating nine public datasets and one private global parcel dataset, containing 28,482 QA pairs and 20,850 images. The pipeline begins with multi-source data pre-processing, including collection, format standardization, and annotation refinement. We then generate a diverse set of agriculturally relevant questions through the systematic definition of tasks. Finally, we employ LMMs for inference, generating responses, and performing detailed examinations. We evaluated 20 open-source LMMs and 4 closed-source models on AgroMind. Experiments reveal significant performance gaps, particularly in spatial reasoning and fine-grained recognition, it is notable that human performance lags behind several leading LMMs. By establishing a standardized evaluation framework for agricultural RS, AgroMind reveals the limitations of LMMs in domain knowledge and highlights critical challenges for future work. Data and code can be accessed at https://rssysu.github.io/AgroMind/.

Agricultural parcels serve as basic units for conducting agricultural practices and applications, which is vital for land ownership registration, food security assessment, soil erosion monitoring, etc. However, existing agriculture parcel extraction studies only focus on mid-resolution mapping or regular plain farmlands while lacking representation of complex terraced terrains due to the demands of precision agriculture. In this paper, we introduce a more fine-grained terraced parcel dataset named GTPBD (Global Terraced Parcel and Boundary Dataset), which is the first fine-grained dataset covering major worldwide terraced regions with more than 200,000 complex terraced parcels with manually annotation. GTPBD comprises 47,537 high-resolution images with three-level labels, including pixel-level boundary labels, mask labels, and parcel labels. It covers seven major geographic zones in China and transcontinental climatic regions around the world. Compared to the existing datasets, the GTPBD dataset brings considerable challenges due to the: (1) terrain diversity; (2) complex and irregular parcel objects; and (3) multiple domain styles. Our proposed GTPBD dataset is suitable for four different tasks, including semantic segmentation, edge detection, terraced parcel extraction and unsupervised domain adaptation (UDA) tasks. Accordingly, we benchmark the GTPBD dataset on eight semantic segmentation methods, four edge extraction methods, three parcel extraction methods and five UDA methods, along with a multi-dimensional evaluation framework integrating pixel-level and object-level metrics. GTPBD fills a critical gap in terraced remote sensing research, providing a basic infrastructure for fine-grained agricultural terrain analysis and cross-scenario knowledge transfer. The code and data are available at https://github.com/Z-ZW-WXQ/GTPBD/.

While domain adaptation (DA) methods have made significant strides in remote sensing community, most current works assume that the source domain labels are accurate. However, limited emphasis has been placed on the scenario where source data are mislabeled with noisy annotations, which is more common in real applications and referred to as noisy DA (NDA). This article formulates remote sensing cross-scene classification on NDA scenarios and proposes a novel network called bilateral adaptation network (BAN), which consists of two parts: 1) forward learning (FL), which utilizes a model learning from the noisy source domain and transfers knowledge to target domain; and 2) backward learning (BL), which utilizes a dual model to acquire knowledge from the target domain and transfer it to source domain. We conduct two parts alternately and adopt a symmetrical Kullback-Leibler (KL) loss to align predictions of the model and its dual model in the same domain. This interactive strategy is able to explore bilateral relationships between domains, implicitly reducing label noise in the source domain. In addition, BAN could serve as a universal paradigm to not only improve the existing NDA methods but also enhance recent DA approaches. Comprehensive evaluations on three publicly available RGB-band remote sensing datasets and two hyperspectral datasets validate the superior effectiveness of our proposed BAN. BAN improves the average accuracy by 6.70%–15.70% on RGB datasets and overall accuracy (OA) by 1.36%–3.14% on hyperspectral datasets with flip-20% noise compared to other state-of-the-art DA and NDA approaches. Promising results indicate the potential of our approach in tackling more general and practical problems with noisy source domain.

Although enormous Domain Adaptation (DA) approaches have been proposed for cross-scene hyperspectral image (HSI) classification, most of them strongly rely on prior knowledge of the relationship between the label sets of source and target domains (including closed-set, partial and open-set DA), which significantly limits their applications. In a real-world application scenario, we often transfer knowledge between domains without any constraints on the label sets, which is called Universal Domain Adaptation (UniDA). In this paper, we propose HyUniDA, which is the first attempt to address UniDA scenario from HSIs. HyUniDA contains two major parts: the Shared Semantic Pairing (SSP) and Domain Similarity Score (DSS). The SSP identifies pairs of clusters that have coincident semantic features as the common classes. By examining the consistency level of samples across source and target domains, DSS can estimate the number of target clusters and generate distinct clusters without prior knowledge. We evaluate our proposed method on two transfer learning tasks for four typical HSI datasets, it turns out that our proposed method yields 6.41%∼34.71% improvements compared to other state-of-the-art DA methods.

Although enormous Domain Adaptation (DA) approaches have been proposed for cross-scene hyperspectral image (HSI) classification, majority DA methods strongly depend on much prior knowledge of the association among the label sets of source and target domains (encompassing closed set, partial and open set DA), thereby significantly hindering their applications. Realistic application scenarios often require knowledge transfer between domains without restrictions on the label space, which is called Universal Domain Adaptation (UniDA). In this paper, we propose HyUniDA, which is the first attempt to address UniDA scenario from HSIs. HyUniDA contains two major parts: the Shared Semantic Pairing (SSP) and Domain Similarity Score (DSS). We group both source and target domains to form discriminative clusters. The SSP identifies pairs of clusters that have coincident semantic features as the common classes. By examining the consistency level of samples across source and target domains, DSS can estimate the quantity of target clusters and generate distinct clusters without prior knowledge. Meanwhile, we apply the contrastive domain discrepancy to alleviate the offset of samples distribution, with a representative regularizer to assist distinguish target domain clusters. We evaluate our proposed method on three transfer learning tasks for six typical HSI datasets, it turns out that our proposed method yields 3.83%~37.57% improvements compared to other state-of-the-art DA methods.