Biao Wang

Anhui University · school of resources and environmental engineering

High-resolution remote sensing image-based vegetation monitoring is a hot topic in remote sensing technology and applications. However, when facing large-scale monitoring across different sensors in broad areas, the current methods suffer from fragmentation and weak generalization capabilities. To address this issue, this paper proposes a multisour...

Remote sensing semantic change detection (SCD) involves extracting information about changes in land cover/land use (LCLU) within the same area at different times. This issue is of crucial significance in many Earth observation tasks, such as precise urban planning and natural resource management. However, the current methods primarily focus on spa...

Multi-spectral 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...

Change detection (CD), a crucial technique for observing ground-level changes over time, is a challenging research area in the remote sensing field. Deep learning methods for CD have made significant progress in remote sensing intelligent interpretation. However, with very high-resolution (VHR) satellite imagery, technical challenges such as insuff...

The complex remote sensing image acquisition conditions and the differences in crop growth create many crop classification challenges. Frequency decomposition enables the capture of the feature information in an image that is difficult to discern. Frequency domain filters can strengthen or weaken specific frequency components to enhance the intercl...

Ozone concentration Monitoring is essential to atmospheric pollution prevention and control. Against the background of severe ozone pollution over China in recent years, a spatiotemporal contiguous ozone concentration mapping method was developed. We imputed the significant data gaps of the Ozone Monitoring Instrument's tropospheric NO2 content by...

Winter wheat is one of the most important food crops in the world. Remote sensing technology can be used to obtain the spatial distribution and planting area of winter wheat in a timely and accurate manner, which is of great significance for agricultural management. Influenced by the growth conditions of winter wheat, the planting structures of the...

Individuals with abnormalities are key drivers of subtle stress changes in forest ecosystems. Although remote sensing monitoring and deep learning have been developed for forest ecosystems, they are faced with the complexity of forest landscapes, multiple sources of remote sensing data, high monitoring costs, and complex terrain, which pose signifi...

Urban rivers are complex ecosystems that directly determine the living environment of human beings. Monitoring the urban river water quality indexes is a challenge in water quality evaluation. The purpose of this study was to propose a multi-source remote sensing water quality inversion method based on a small number of samples to solve the problem...

Obtaining accurate and timely crop area information is crucial for crop yield estimates and food security. Because most existing crop mapping models based on remote sensing data have poor generalizability, they cannot be rapidly deployed for crop identification tasks in different regions. Based on a priori knowledge of phenology, we designed an off...

Obtaining accurate and timely crop mapping is essential for refined agricultural refinement and food security. Due to the spectral similarity between different crops, the influence of image resolution, the boundary blur and spatial inconsistency that often occur in remotely sensed crop mapping, remotely sensed crop mapping still faces great challen...

High-resolution remote sensing (HRRS) images have few spectra, low interclass separability and large intraclass differences, and there are some problems in land cover classification (LCC) of HRRS images that only rely on spectral information, such as misclassification of small objects and unclear boundaries. Here, we propose a deep learning fusion...

Building change detection has always been an important research focus in production and urbanization. In recent years, deep learning methods have demonstrated a powerful ability in the field of detecting remote sensing changes. However, due to the heterogeneity of remote sensing and the characteristics of buildings, the current methods do not prese...

Water body extraction from remote sensing images is an important task. Deep learning has become a more popular method for extracting water bodies from remote sensing images. However, these methods are usually aimed at a specific sensor and are not applicable. Thus, we proposed a new network, called the dense-local-feature-compression network (DLFC)...

Road extraction is an important task in remote sensing image information extraction. Recently, deep learning semantic segmentation has become an important method of road extraction. Due to the impact of the loss of multiscale spatial features, the results of road extraction still contain incomplete or fractured results. In this paper, we proposed a...

Land use classification is a fundamental task of information extraction from remote sensing imagery. Semantic segmentation based on deep convolutional neural networks (DCNNs) has shown outstanding performance in this task. However, these methods are still affected by the loss of spatial features. In this study, we proposed a new network, called the...

Building extraction from very high resolution (VHR) imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Compared with the traditional building extraction approaches, deep learning networks have recently shown outstanding performance in this tas...

Wetlands are one of the most important ecosystems on the Earth and play a critical role in regulating regional climate, preventing floods, and reducing flood severity. However, it is difficult to detect wetland changes in multitemporal Landsat 8 OLI satellite images due to the mixed composition of vegetation, soil, and water. The main objective of...

Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study...

Lakes are sensitive to both climate change and human activities, and therefore serve as an excellent indicator of environmental change. Based on a time series of Landsat images over the last 16 years, this article attempts to provide a first picture of the annual variations in area of nine plateau lakes in Yunnan province, China. The modified norma...

The main objective of this letter is to improve the accuracy of unsupervised change detection method and minimize registration errors among multi-temporal images in the change detection process. To this end, iteratively regularized multivariate alteration detection (IR-MAD) is applied to synthetically fused images. First, four synthetically fused h...

Change detection technique is essential to various applications of Very High-Resolution(VHR) satellite imagery and land monitoring. However, change detection accuracy of VHR satellite imagery can be decreased due to various geometrical dissimilarity. In this paper, the existing fusion evaluation indexes were revised and applied to improve VHR image...

The construction of hyperspectral test-bed dataset is essential for the effective performance of hyperspectral image for various applications. In this study, we analyzed the technical points for generating of optimal hyperspectral test-bed site for hyperspectral sensors and the efficiency of hyperspectral test-bed site. In this regard regions we an...