Existing visual deep learning paradigms, which are based on labels, struggle to capture the intricate interrelationships between farmland and its surrounding environment, and fail to account for temporal variations associated with the phenological cycle. These limitations lead to omissions and confusion in the recognition process, greatly impacting the accuracy and efficiency of farmland recognition. Language can accurately depict the spatial attributes of farmland, profoundly express the unique phenological landscapes of farmland that change with seasons and growth stages, and express the intricate interactions between these changes and environmental factors. This capability can address the deficiencies of label-based visual deep learning in understanding the complex features of farmland. This study explored, for the first time, the application of language-guided vision-language models (VLMs) for farmland segmentation. Firstly, as current VLM research lack dedicated farmland image text pair (FIT) dataset, this study constructed a FIT dataset by two steps. Step1, design a semi-automatic text description annotation framework for farmland images based on 12 key factors influencing farmland segmentation. Step2, use the framework to construct the FIT dataset. Then, a VLM for farmland segmentation (FSVLM) was designed by combining a semantic segmentation model with a multimodal large language model. Comparative experiments demonstrated that the proposed method outperforms existing farmland segmentation methods in both generalization and segmentation accuracy. In addition, a series of ablation experiments were conducted to examine the impacts of language descriptions of different semantic levels on the model's farmland information extraction performance.

Hyperspectral image classification (HSIC) is one of the most important research topics in the field of remote sensing. However, it is difficult to label hyperspectral data, which limits the improvement of classification performance of hyperspectral images in the case of small samples. To alleviate this problem, in this paper, a dual-branch network which combines cross-channel dense connection and multi-scale dual aggregated attention (CDC_MDAA) is proposed. On the spatial branch, a cross-channel dense connections (CDC) module is designed. The CDC can effectively combine cross-channel convolution with dense connections to extract the deep spatial features of HSIs. Then, a spatial multi-scale dual aggregated attention module (SPA_MDAA) is constructed. The SPA_MDAA adopts dual autocorrelation for attention modeling to strengthen the differences between features and enhance the ability to pay attention to important features. On the spectral branch, a spectral multi-scale dual aggregated attention module (SPE_MDAA) is designed to capture important spectral features. Finally, the spatial spectral features are fused, and the classification results are obtained. The experimental results show that the classification performance of the proposed method is superior to some state-of-the-art methods in small samples and has good generalization.

In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image classification (HSIC). However, the size of the convolutional kernel in CNNs is fixed, which makes it difficult to capture the dependence of long-range feature information. In addition, the extracted features often contain a large amount of redundant information. In order to alleviate these issues, a feature complementary attention network based on adaptive knowledge filtering (FCAN_AKF) is proposed in this paper. First, in order to alleviate the problem that CNNs are difficult to capture the dependence between close-range and long-range spectral features due to the limited receptive field, a non-local band regrouping (NBR) strategy is designed. NBR enables CNN to capture nonlocal spectral features in a limited receptive field to establish the interdependence between close-range and long-range spectral features. In addition, the non-local features extracted after using NBR and the local features of the original hyperspectral image are integrated to achieve complementation between non-local features and local features. Then, in order to eliminate the interference of redundant information on the network, a dual pyramid spectral spatial attention (DPSSA) module is proposed and used to capture spectral spatial attention. Next, an adaptive knowledge filter (AKF) is designed, which can adaptively further filter out redundant information and enhance feature information that is beneficial for classification. Finally, extensive experiments were conducted on three challenging datasets, demonstrating that the proposed method has stronger competitiveness compared to some state-of-the-art HSIC methods.

Understanding and mastering the spatiotemporal characteristics of farmland are essential for accurate farmland segmentation. The traditional deep learning paradigm that solely relies on labeled data has limitations in representing the spatial relationships between farmland elements and the surrounding environment. It struggles to effectively model the dynamic temporal evolution and spatial heterogeneity of farmland. Language, as a structured knowledge carrier, can explicitly express the spatiotemporal characteristics of farmland, such as its shape, distribution, and surrounding environmental information. Therefore, a language-driven learning paradigm can effectively alleviate the challenges posed by the spatiotemporal heterogeneity of farmland. However, in the field of remote sensing imagery of farmland, there is currently no comprehensive benchmark dataset to support this research direction. To fill this gap, we introduced language-based descriptions of farmland and developed the FarmSeg-VL dataset – the first fine-grained image–text dataset designed for spatiotemporal farmland segmentation. Firstly, this article proposed a semi-automatic annotation method that can accurately assign captions to each image, ensuring a high data quality and semantic richness while improving the efficiency of dataset construction. Secondly, FarmSeg-VL exhibits significant spatiotemporal characteristics. In terms of the temporal dimension, it covers all four seasons. In terms of the spatial dimension, it covers eight typical agricultural regions across China, with a total area of approximately 4300 km². In addition, in terms of captions, FarmSeg-VL covers rich spatiotemporal characteristics of farmland, including its inherent properties, its phenological characteristics, its spatial distribution, its topographic and geomorphic features, and the distribution of surrounding environments. Finally, we perform a performance analysis of the vision language model and a deep learning model that relies only on labels trained on FarmSeg-VL. Models trained on the vision language model outperform deep learning models that rely only on labels by 10 %–20 %, demonstrating its potential as a standard benchmark for farmland segmentation. The FarmSeg-VL dataset will be publicly released at https://doi.org/10.5281/zenodo.15860191 (Tao et al., 2025).

Convolutional neural networks (CNNs) have been widely used in hyperspectral image classification due to their ability to extract image features effectively. However, under the condition of limited samples, the modeling ability of CNNs for the relationships among samples is limited. At present, research on the classification of hyperspectral images with a small number of samples remains an important challenge in the field of hyperspectral image processing. Recently, graph convolutional networks (GCNs) have been applied in hyperspectral image classification tasks. In this paper, a multi-hop graph rectifies attention and spectral overlap grouping convolutional fusion network (MRSGFN) for hyperspectral image classification is proposed. In the graph convolution branch, a multi-hop graph rectify attention (MHRA) is designed to weight and correct the features extracted by graph convolution. In the convolutional branch, to solve the problem of dimensionality disaster caused by high spectral dimension with a small number of samples, a spectral intra-inter group feature extraction module (SI2FEM) based on spectral overlap grouping is constructed. In order to better fuse the features extracted from CNNs and GCNs, a gaussian weighted fusion module (GWFM) is elaborately designed in this paper. The features extracted by different branches are assigned different weights by GWFM through a two-dimensional Gaussian map and then fused. Numerous experiments were conducted on three common datasets and showed that the classification performance of the proposed MRSFGN is superior to other advanced methods.