Updating land cover map based on change detection of high-resolution remote sensing images

Guo, Rui; Xiao, Pengfeng; Zhang, Xueliang; Liu, Hao

doi:10.1117/1.jrs.15.044507

Cited by 5 publications

(4 citation statements)

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“…Hu Wenliang et al [4] An improved objectoriented optimal segmentation scale calculation model with good reliability is proposed. The classification results with high accuracy and reliability are obtained for the information of buildings and woodlands [5][6] .…”

Section: Research Status At Home and Abroad 121 Application And Theor...mentioning

confidence: 99%

Classification and Extraction of Rural Green Coverage Based on Object-based High-resolution Remote Sensing Images

2023

jipta

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Rural green space is the foundation of rural environment. In order to solve the practical problems of rural green space, this paper starts from the perspective of rural space, and studies the systematic and normative extraction method of rural green space in Jiangning District of Nanjing city. It provides a scientific basis for the development of rural green space in China. Based on the object-oriented threshold classification method, this paper divides the rural green space into cultivated land, grassland, forest land, residential green space and road green space, and other features into water, road and urban and rural land, and analyzes the land coverage. The main research contents and conclusions are as follows: (1) The multiscale segmentation parameters are determined. The influence of spectral factor, shape and compactness factor, segmentation scale and band weight on the experimental results is analyzed. After many experiments, the optimal segmentation scale parameters are

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Section: Research Status At Home and Abroad 121 Application And Theor...mentioning

confidence: 99%

Classification and Extraction of Rural Green Coverage Based on Object-based High-resolution Remote Sensing Images

2023

jipta

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“…The spatial attention mechanism adjusts and calculates the weights by locating the target region in the image and performing affine transformations. The channel attention mechanism, on the other hand, adjusts weights based on the influence of different bands of the feature map on the output data [27]. The spatial-channel attention mechanism is a combination of the two above-mentioned mechanisms.…”

Section: Channel Attention Mechanismmentioning

confidence: 99%

Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX

et al. 2023

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Anomaly detection of remote sensing images has gained significant attention in remote sensing image processing due to their rich spectral information. The Local RX (LRX) algorithm, derived from the Reed–Xiaoli (RX) algorithm, is a hyperspectral anomaly detection method that focuses on identifying anomalous pixels in hyperspectral images by exploiting local statistics and background modeling. However, it is still susceptible to the noises in the Hyperspectral Images (HSIs), which limits its detection performance. To address this problem, a hyperspectral anomaly detection algorithm based on channel attention mechanism and LRX is proposed in this paper. The HSI is feed into the auto-encoder network that is constrained by the channel attention module to generate a more representative reconstructed image that better captures the characteristics of different land covers and has less noises. The channel attention module in the auto-encoder network aims to explore the effective spectral bands corresponding to different land covers. Subsequently, the LRX algorithm is utilized for anomaly detection on the reconstructed image obtained from the auto-encoder network with the channel attention mechanism, which avoids the influence of noises on the anomaly detection results and improves the anomaly detection performance. The experiments are conducted on three HSIs to verify the performance of the proposed method. The proposed hyperspectral anomaly detection method achieves higher Area Under Curve (AUC) values of 0.9871, 0.9916 and 0.9642 on HYDICE urban dataset, AVIRIS aircraft dataset and Salinas Valley dataset, respectively, compared with other six methods. The experimental results demonstrate that the proposed algorithm has better anomaly detection performance than LRX and other algorithms.

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“…High-resolution remote sensing images encapsulate crucial information, and the segmentation of buildings proves valuable in diverse applications, including urban growth forecasting [1], disaster management [2], traffic management [3], and map updating [4]. Traditional segmentation methods, including edge-based [5], threshold processing [6], and graph theory [7] approaches, often require manual intervention and suffer from limitations in parameter selection and feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Dual Hybrid Attention Mechanism-Based U-Net for Building Segmentation in Remote Sensing Images

Lei,

Liu,

Yang

et al. 2024

Applied Sciences

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High-resolution remote sensing images (HRRSI) have important theoretical and practical value in urban planning. However, current segmentation methods often struggle with issues like blurred edges and loss of detailed information due to the intricate backgrounds and rich semantics in high-resolution remote sensing images. To tackle these challenges, this paper proposes an end-to-end attention-based Convolutional Neural Network (CNN) called Double Hybrid Attention U-Net (DHAU-Net). We designed a new Double Hybrid Attention structure consisting of dual-parallel hybrid attention modules to replace the skip connections in U-Net, which can eliminate redundant information interference and enhances the collection and utilization of important shallow features. Comprehensive experiments on the Massachusetts remote sensing building dataset and the Inria aerial image labeling dataset demonstrate that our proposed method achieves effective pixel-level building segmentation in urban remote sensing images by eliminating redundant information interference and making full use of shallow features, and improves the segmentation performance without significant time costs (approximately 15%). The evaluation metrics reveal significant results, with an accuracy rate of 0.9808, precision reaching 0.9300, an F1 score of 0.9112, a mean intersection over union (mIoU) of 0.9088, and a recall rate of 0.8932.

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Updating land cover map based on change detection of high-resolution remote sensing images

Cited by 5 publications

References 0 publications

Classification and Extraction of Rural Green Coverage Based on Object-based High-resolution Remote Sensing Images

Classification and Extraction of Rural Green Coverage Based on Object-based High-resolution Remote Sensing Images

Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX

Dual Hybrid Attention Mechanism-Based U-Net for Building Segmentation in Remote Sensing Images

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