With the development of satellite technology, the importance of multi-temporal remote sensing (RS) image change detection (CD) in urban planning, environmental monitoring, and other fields is increasingly prominent. Deep learning techniques enable a...
In complex agroforestry mosaics, similarities in canopy structure and spectral response among tree species constrain medium-resolution peach orchard identification. Using 2023 multi-temporal Sentinel-2 imagery from Ganyu District, Lianyungang, Jiangs...
Wetlands, known as “the kidney of the Earth”, serve as critical ecological carriers for global sustainable development. The fine classification of wetlands is crucial to their utilization and protection. Wetland fine-scale classification...
The application of remote sensing technology in water body detection has become increasingly widespread, offering significant value for environmental monitoring, hydrological research, and disaster early warning. However, the existing methods face ch...
To address the challenge of image matching posed by significant modal differences in remote sensing images influenced by snow cover, this paper proposes an innovative image transformation-based matching method. Initially, the Pix2Pix-GAN conversion n...
Although efforts and progress have been made in crop classification using optical remote sensing images, it is still necessary to make full use of the high spatial, temporal, and spectral resolutions of remote sensing images. However, with the increa...
Accurate and timely crop mapping supports agricultural resource management, yield prediction, and food security decision-making. In this study, we propose the Temporal Multi-scale Spatio-Temporal Network (TMST), a crop mapping method that integrates...
For the remaining useful life (RUL) prediction of rolling bearing under strong background noise, it is hard to get accurate results based on the non-stationary vibration signals because of complex degradation characteristics and difficult extraction...
Reliable estimation of winter wheat yield is critical to food system stability and farmland management. Integrating multi-spectral remote sensing data with agronomic parameters represents a primary strategy for improving yield estimation accuracy. Ho...
Accurate crop distribution mapping using multi-temporal remote sensing has become increasingly important for agricultural monitoring and management. However, existing methods often rely on the direct stacking of multi-temporal features, which leads t...
Impervious surfaces have important effects on the natural environment, including promoting hydrological run-off and impeding evapotranspiration, as well as increasing the urban heat island effect. Obtaining accurate and timely information on the spat...
Building change detection (BuCD) can offer fundamental data for applications such as urban planning and identifying illegally-built new buildings. With the development of deep neural network-based approaches, BuCD using high-spatial-resolution remote...
Estuarine wetlands are highly dynamic ecosystems, and the vegetation serves as a critical indicator of ecological health. Accurate mapping of different vegetation types remains challenging due to spectral similarities and the high dimensionality of t...
Detailed knowledge about tree species composition is of great importance for forest management. The two identical European Space Agency (ESA) Sentinel-2 (S2) satellites provide data with unprecedented spectral, spatial and temporal resolution. Here,...
Accurate capacity prediction is paramount for ensuring the operational safety and reliability of lithium-ion battery management systems (BMS). Nevertheless, contemporary data-driven approaches often grapple with limited feature representation—f...
Using plastic mulching for farmland is booming around the world. Despite its benefit of protecting crops from unfavorable conditions and increasing crop yield, the massive use of the plastic-mulching technique causes many environmental problems. Ther...
Fine-grained identification of crop planting structures provides key data for precision agriculture, thereby supporting scientific production and evidence-based policy making. This study selected a representative experimental farmland in Qingyang, Ga...
Accurate, high-resolution information on economic forest resources, here referring to fruit-tree plantations and economic tree crops, is essential for land-use planning and resource management in arid regions. Xinjiang, China—one of the country...
Remote sensing technologies have seen extraordinary improvements in both spatial resolution and accuracy recently. In particular, airborne laser scanning systems can now provide data for surface modeling with unprecedented resolution and accuracy, wh...
Timely and accurate information on crop mapping and monitoring is necessary for agricultural resources management. Accordingly, the applicability of the proposed classification-feature selection ensemble procedure with different feature sets for crop...
Soil organic matter (SOM) is a key indicator of arable land quality and the global carbon cycle; accurate regional-scale SOM estimation is vitally significant for sustainable agricultural development and climate change research. This study evaluates...
Accurate soybean mapping is critical for food–oil security and cropping assessment, yet spatiotemporal heterogeneity arising from fragmented parcels and phenological variability reduces class separability and robustness. This study aims to deli...
Mapping soil heavy metal concentration using machine learning models based on readily available satellite remote sensing images is highly desirable. Accurate mapping relies on appropriate data, feature extraction, and model selection. To this end, a...
Spatial features retrieved from satellite data play an important role for improving crop classification. In this study, we proposed a deep-learning-based time-series analysis method to extract and organize spatial features to improve parcel-based cro...
Currently, remote sensing crop identification is mostly based on all available images acquired throughout crop growth. However, the available image and data resources in the early growth stage are limited, which makes early crop identification challe...
Accurate estimation of winter wheat canopy nitrogen concentration (CNC) supports crop diagnosis and precision nitrogen management, yet the relative value and redundancy of multispectral, structural, and chlorophyll-related variables remain unclear in...
Industrial Control Systems (ICSs) are critical infrastructure for maintaining social and economic stability, but they face increasing security threats that require robust anomaly detection mechanisms. Anomaly detection in ICS, based on sensor data, i...
As tea is an important economic crop in many regions, efficient and accurate methods for remotely identifying tea plantations are essential for the implementation of sustainable tea practices and for periodic monitoring. In this study, we developed a...
Space infrared (IR) target recognition has always been a key issue in the field of space technology. The imaging distance is long, the target is weak, and the feature discrimination is low, making it difficult to distinguish between high-threat targe...
Localizing anomalous activities in surveillance video requires spatial appearance to be interpreted within its temporal context. This study presents a supervised dual-branch architecture that combines YOLOv8n features from a key frame with R3D-18 fea...
Forecasting the water supply of a water treatment plant is an important management and decision-making task in the water treatment system. Currently, most studies in this field do not consider the problems brought about by the time scale when selecti...
Given video streams, we aim to correctly detect unsegmented signs related to continuous sign language recognition (CSLR). Despite the increase in proposed deep learning methods in this area, most of them mainly focus on using only an RGB feature, eit...
Network intrusion detection is a core component of modern cybersecurity, but it remains challenging due to highly imbalanced traffic, heterogeneous feature types, and a presence of short-term temporal dependencies in network flows. Traditional machin...
The quick and precise assessment of rice distribution by remote sensing technology is important for agricultural development. However, mountain rice is limited by the complex terrain, and its distribution is fragmented. Therefore, it is necessary to...
This research presents DuSAFNet, a lightweight deep neural network for fine-grained bird audio classification. DuSAFNet combines dual-path feature fusion, spectral–temporal attention, and a multi-band ArcMarginProduct classifier to enhance inte...
An accurate and efficient estimation of eucalyptus plantation areas is of paramount significance for forestry resource management and ecological environment monitoring. Currently, combining multidimensional optical and SAR images with machine learnin...
Multitemporal very-high-resolution (VHR) satellite images are used as core data in the field of remote sensing because they express the topography and features of the region of interest in detail. However, geometric misalignment and radiometric dissi...
Automotive dashboard cameras are widely used to record driving events and often serve as critical evidence in accident investigations and insurance claims. However, the availability of free and low-cost editing tools has increased the risk of video t...
Soil sand content is an important characterization index of soil texture, which directly affects soil water regulation, nutrient cycling, and crop growth potential. Therefore, its high-precision spatial distribution information is of great importance...
The dynamic nature of acoustic environments—particularly the fluctuation of underwater channels and time-varying target observation angles—poses significant challenges for active sonar target recognition, a problem further aggravated by t...
Relative radiometric normalization (RRN) is important for pre-processing and analyzing multitemporal remote sensing (RS) images. Multitemporal RS images usually include different land use/land cover (LULC) types; therefore, considering an identical l...
Since 2014, Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data have become an important source in the field of displacement detection thanks to regular acquisitions and 7.5 years of temporal coverage at global level. Despite the increasing number of...
Object-Based Image Analysis (OBIA) generates high-dimensional features that frequently induce the curse of dimensionality, impairing classification efficiency and generalizability in high-resolution remote sensing images. To address these challenges...
This paper presents a comprehensive time-series analysis framework leveraging the Temporal Fusion Transformer (TFT) architecture to address the challenge of multi-horizon forecasting in complex ecological systems, specifically focusing on global fish...
Foreground detection, which extracts moving objects from videos, is an important and fundamental problem of video analysis. Classic methods often build background models based on some hand-craft features. Recent deep neural network (DNN) based method...
Crop classification in agriculture is one of important applications for polarimetric synthetic aperture radar (PolSAR) data. For agricultural crop discrimination, compared with single-temporal data, multi-temporal data can dramatically increase crop...
Semantic change detection has become a key technology for monitoring the evolution of land cover and land use categories at the semantic level. However, existing methods often lack effective information interaction and fail to capture changes at mult...
Crop classification in mountainous agricultural regions is challenged by fragmented fields, limited reference samples, class imbalance, and strong spatial heterogeneity. We evaluated the Tabular Prior-data Fitted Network (TabPFN) for reliability-awar...
Spatial registration of multidate or multisensorial images is required for many applications in remote sensing. Automatic image registration, which has been extensively studied in other areas of image processing, is still a complex problem in the fra...
Multi-temporal remote sensing image matching plays a crucial role in tasks such as detecting changes in urban buildings, monitoring agriculture, and assessing ecological dynamics. Due to temporal variations in images, significant changes in land feat...
of 70