Detecting changes in impervious surface from remote sensing images helps evaluate urbanization and environmental impacts and provides an important basis for urban planning and ecological protection. Most existing impervious surface change detection (CD) methods are supervised, which makes them time-consuming and laborious in practical applications. In this letter, a coarse-to-fine hierarchical approach, RIDKDC, which refines initial land cover CD using a domain knowledge driven constraint (DKDC) method, was proposed for unsupervised impervious surface CD. First, the initial land cover CD map containing all types of changes is generated by iteratively reweighted multivariate alteration detection (IRMAD) method. Then, DKDC is applied to mask the nonimpervious surface changes and pseudo changes, retaining only the changes in impervious surface. Finally, the desired result is obtained by mathematical morphology processing. Experimental results show that the proposed RIDKDC outperformed the compared methods.

Remote sensing change detection (CD) has garnered extensive research and application due to its ability to identify changes in land features within the same area across different periods. CD tasks require features with strong intraclass distinctions and precise spatial boundary details. Existing methods enhance the extraction of difference features but significantly increase computational complexity in high-resolution remote sensing imagery. Moreover, these methods focus on pixel-level difference extraction while neglecting feedback from the overall change object. As a result, they lack global information perception, leading to blurred edges and fragmented interiors in the change areas. To address these challenges, we propose a feature enhancement and feedback network (FEFNet) for CD. First, we designed a multilevel dual-feature fusion enhancement module (DFFM) to improve the representation of latent features between the bitemporal images. Second, we developed a feature coupling feedback module (FCFM) that efficiently decodes multiscale change features to generate extraction results. The experimental results show that FEFNet outperforms recent models in both computational efficiency and detection performance. With only 10.56G FLOPs and 2.26M parameters, FEFNet achieves an${F}_{1}$score of 92.32% on the LEVIR-CD dataset and 93.77% on the WHU-CD dataset. The code will be available athttps://github.com/XiaoJ058/RS-CD.

Deep learning-based high-resolution remote sensing for farmland extraction is a crucial method for obtaining large-scale farmland information. However, variations in crop types, growth conditions, and factors such as narrow edges in farmland lead to lower extraction accuracy and inaccurate boundaries in high-resolution remote sensing. Therefore, this letter proposes a multibranch convolutional neural network (FFENet) that employs a dense-feature overlay fusion module (FFM) and an information homogeneity enhancement module. This network facilitates rapid extraction and dense fusion of information at various scales through the implementation of the dense FM, thereby enhancing the model’s representation of global consistency and local features. The information homogeneity enhancement module further strengthens the information exchange between the bottom and top layers, improves the fusion of feature information across branches, and ensures consistent representation of internal farmland features while enhancing differentiation at the edges. The experimental results demonstrate that the proposed method effectively considers both internal global consistency and local variations in edge information, thereby ensuring the integrity of farmland plots and the continuity of the farmland edges. The quantitative evaluation of the dataset shows that the model performs well in farmland extraction, with overall accuracy (OA) and intersection over union (IoU) reaching 95.41% and 93.74% on the GF-2 dataset and 94.75% and 88.28% on the JL-1 dataset.

Semantic Change Detection (SCD) plays a critical role in intelligent information extraction from remote sensing images. However, its performance in detecting weak-information targets, such as small-sized ground objects, is still constrained by semantic representation ambiguity, information loss, and complex background interference. This paper proposes a Semantic Guidance Inference Network (SGI-Net) that establishes a collaborative optimization mechanism through semantic-driven feature coupling, adaptive reasoning, and attention guidance to overcome these challenges. Specifically, we design a Hierarchical Feature Coupling Module (HFCM). This module enhances geometric detail representation for weak-information targets through cross-layer feature aggregation and dual-path decoupling. It generates discriminative semantic priors, establishing geometric-semantic correlations across multi-level features. Furthermore, a Multi-dimensional Adaptive Inference Module (MAIM) is developed to achieve adaptive feature aggregation of local-global patterns via non-overlapping patch partitioning, preserving spatial continuity of weak-information targets through multi-scale feature fusion. Additionally, the Change-Guided Attention (CGA) module dynamically adjusts channel-wise and spatial attention weights based on prior knowledge, amplifying cross-hierarchical responses to weak change signals. Experimental results on the SECOND and SJH_SCD datasets demonstrate that SGI-Net achieves values of 73.86% and 77.43%, respectively, surpassing state-of-the-art (SOTA) methods by 0.73-1.37% in mIoU and 1.26-1.77% in SeK. Visualization analysis further confirms that SGI-Net effectively enhances the feature responses of weak-information targets in complex object scenes, improving the discrimination accuracy of fine-grained change boundaries. These results provide reliable technical support for high-precision remote sensing change detection.

Multispectral information is crucial for remote sensing crop monitoring, but current methods struggle with inadequate feature extraction, leading to poor generalization and incomplete segmentation. The segment anything model (SAM) shows significant potential for generalization across fields, offering a promising solution for crop monitoring. This article introduces a crop segmentation method based on SAM using multistage adaptation fine-tuning, namely MAF-SAM, effectively utilizing information from multispectral remote sensing and the transfer and generalization abilities of SAM. In its first stage, MAF-SAM employs a prefix adapter to extract primary low-level multispectral features and compresses them into three channels to meet the requirements of subsequent stages. The second stage introduces a low-rank adaptation (LoRA) fine-tuning strategy to inject crop-specific knowledge into the image encoder, enhancing MAF-SAM’s adaptability in particular crop segmentation tasks. In the third stage, it utilizes a mask decoder with no-prompt embedding to automatically generate masks with accurate class information. MAF-SAM achieves F1 scores and Intersection over Union (IoU) for soybean and corn of 0.9294, 0.8680, 0.8760, and 0.7723, respectively, along with a Kappa coefficient of 0.9543. It demonstrates superior temporal and spatial transfer capabilities relative to five other advanced segmentation methods in our study area.

Scene change detection identifies functional changes at the scene level. Compared with pixel-level and object-level change detection, it can provide a higher level understanding of changes on the Earth’s surface. Triple-branch networks that perform scene binary change detection and scene classification tasks simultaneously are competitive in the field of scene change detection, as they consider both single-temporal scene semantic information and cross-temporal change features. However, some problems still exist. First, the temporal change feature extraction is insufficient, and the 1-D feature vector used for scene change detection and classification is lacking in representativeness. Second, the predicted scene binary change detection and classification results are often contradictory at the network prediction stage, leading to the unsatisfactory performance of change trajectory identification. To address these issues, a novel framework that integrates a differential aggregation network (DAN) and class probability-based fusion strategy (CPFS) was proposed. The designed DAN can fully capture the temporal change features using four advanced differential fusion modules (DFMs) to aggregate the multilevel difference information. In addition, it is able to generate more representative 1-D feature vectors by adopting two novel attention-aware adaptive pooling modules (AAPMs). The developed CPFS produces the final consistent scene binary change detection and classification maps by fusing three predicted class probability vectors. The proposed method was validated on two datasets, and the results demonstrated its superiority to the comparison methods.

Coal, as a traditional energy source, has made remarkable contributions to global economic development. However, surface coal mining brings a series of eco-environmental problems. Therefore, it is crucial to obtain the distribution information of coal mines. Due to the diverse appearance of coal mines and complex background environments, it is very challenging to identify coal mines at a large scale. Exposed coal is an important indicator of coal mining. Spectral indices based on satellite images possess the advantages of simplicity and high efficiency. In this study, the Exposed Coal Index (ECI) was proposed. It enables the accurate identification of exposed coal at a large scale. The effectiveness of the ECI was investigated in four typical surface coal mine distribution regions across the world. Through spectral analysis, two key characteristics of coal spectra were discovered (i.e., the flat spectral shape in the visible to near-infrared range and the low reflectance in the near-infrared band). The ECI utilized these two features to successfully differentiate coal from various background land cover types in all study cases. The results showed that the ECI was effective in visual evaluation, separability analysis, and coal mapping, with superior performance than the three previously proposed indices. The ECI can also be perfectly applied to Landsat 8 images, demonstrating its excellent generalization capability. In addition, compared with three global mining datasets, ECI provided more comprehensive information on coal mine distribution. The proposed ECI is simple, robust, and expected to provide strong support for regional resource management and sustainable development.

As an important agriculture technique, plastic greenhouse (PG) has been widely used to increase crop yield and improve food security status in the world. The high-resolution spatial information of PG is of great significance to precise agricultural management and quantitative environmental assessment. Many studies have examined the role that remote sensing technology could play in mapping and monitoring PG coverage. However, these methods, which employ either the traditional machine learning algorithms or the deep learning models, depend on massive manually labeled samples. To address this problem, this paper proposes a new cross-scale sample transferring method to generate high-resolution samples for automated PG mapping. The proposed method aims to transfer reliable label information from Sentinel-2 images (10-m) to high-resolution images (0.2-m) in a pixel-scene-pixel-object (PSPO) transferring process. In the proposed PG mapping workflow, the low-resolution label information of PG/non-PG can be obtained from an advanced plastic greenhouse index (APGI) which is calculated in Sentinel-2 images, and then the label information is transferred to the corresponding high-resolution images using the proposed PSPO transferring method. Finally, the transferred high-resolution samples are used to train the deep semantic segmentation model and produce PG mapping results. The whole process is labor-free which requires no manually labeled samples. The experimental results on three collected datasets show that the proposed approach can automatically generate accurate and reliable high-resolution samples, and the final PG mapping results can achieve an OA (overall accuracy) of 89.52% ~ 97.65% and F1 score of 84.13% ~ 94.03%, which is comparable to the fully supervised semantic segmentation model.

Accurate cropland mapping is significant for food security and sustainable development. The existing cropland map based on remote sensing mainly focus on moderate to coarse spatial resolution, and these products are generally unsuitable for precision agriculture due to the lack of spatial details. Therefore, there is an urgent need to produce high-resolution (HR) cropland maps to meet current application demands. Recently, the typical classification workflow of HR images employs deep learning models combined with manually annotated samples, and visual interpretation of samples is usually labor-intensive and time-consuming, which is not conducive to large-scale applications. To address this problem, this paper proposes an automated HR cropland extraction solution, namely RRE (Refinement-Reclassification-Extraction), including (i) Refinement of 10 m spatial resolution cropland products, (ii) Reclassifying cropland using the refined product as sample source, and (iii) Extracting HR cropland via designed cross-scale sample transfer. The strength of the proposed framework is that it leverages existing moderate-resolution public products as prior knowledge and provides cross-scale transferable samples for HR images. The whole process does not require manual labeling of samples and is highly automated. Specifically, the experimental results in the three main grain production regions show that, the RRE framework effectively reduces the interference of road networks and ridges, and F1 scores of extracted 1 m HR cropland reaches 87.71 %~94.16 %, which is comparable to the fully supervised cropland extraction method. In addition, the 10 m reclassified cropland, produced by the intermediate process of the RRE, outperforms current cropland product of ESRI Land Cover and ESA World Cover.

Satellite image time series classification has become a research focus with the launch of new remote sensing sensors capable of capturing images with high spatial, spectral, and temporal resolutions. In particular, in the field of crop classification, time dimension information is particularly important. Although some advanced machine learning algorithms, such as random forests (RFs), can achieve good results, they often ignore the time series information. To make full use of temporal and spectral information in multitemporal remote sensing images, a channel attention-based temporal convolutional network (CA-TCN) is proposed in this letter. Specifically, the proposed method is composed of two main modules: temporal convolutional network and attention block. The temporal convolutional network can capture long-range dependence by using a hierarchy of temporal convolutional filters. To capture relevant information inside the sequence and enhance the important information, the attention block is used to enhance the important features in the channel dimension since not all bands contain equal information in crop type classification. The proposed CA-TCN can excavate deeper phenological characteristics. Compared to the temporal attention-based temporal convolutional network and other deep learning-based models, the proposed CA-TCN has achieved state-of-the-art performance in the Breizhcrops dataset with fewer parameters.

An effective network structure is essential for the classification of satellite image time series (SITS). Deep learning models have been widely used for SITS classification and achieved impressive performance, especially the architectures based on self-attention. However, the lack of efficient and comprehensive attention to valuable bands and time series structure hinders the performance to some extent. To address this problem, an end-to-end attention-aware dynamic self-aggregation network (ADSN) is proposed for SITS classification in this work, which combines two main parts: spectral focusing and spectral–temporal feature learning. The core components of ADSN are the channel attention module and dynamic self-aggregation block. Specifically, informative bands in the SITS flowing through the channel attention module can adaptively get a high weight to increase their contributions, while the attentions of some low-efficiency bands are weakened. Besides, the dynamic self-aggregation block, which integrates multiscale dynamic convolution and improved multihead attention in parallel, can simultaneously capture long- and short-distance sequence structures and position relationships to better represent temporal information. Compared with random forest (RF) and seven deep learning algorithms, the proposed model effectively learns spectral and temporal features, and the experimental results confirm that ADSN has achieved superior classification accuracy and generalization ability on two SITS datasets with extremely unbalanced samples.