Fuzzy neighbourhood neural network for high-resolution remote sensing image segmentation

Qu, Tingting; Xu, Jindong; Chong, Qianpeng; Liu, Zhaowei; Yan, Weiqing; Wang, Xuan; Song, Yongchao; Ni, Mengying

doi:10.1080/22797254.2023.2174706

Cited by 10 publications

(3 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case, the Gaussian membership function (GMF) has the characteristics of symmetry and flexibility to represent the data more effectively. Research has shown that combining fuzzy logic and deep learning is effective in data representation [56][57][58][59]. Therefore, we combined fuzzy logic and deep learning to propose fuzzy blocks using trainable GMF.…”

Section: Fuzzy Blockmentioning

confidence: 99%

Detection of Black and Odorous Water in Gaofen-2 Remote Sensing Images Using the Modified DeepLabv3+ Model

Huang,

Xu,

Yan

et al. 2023

Sustainability

Self Cite

View full text Add to dashboard Cite

The detection of black and odorous water using remote sensing technology has become an effective method. The high-resolution remote sensing images can extract target features better than low-resolution images. However, the high-resolution images often introduce complex background details and intricate textures, which often have problems with accurate feature extraction. In this paper, based on remote sensing images acquired by the Gaofen-2 satellite, we proposed a Modified DeepLabv3+ model to detect black and odorous water. To reduce the complexity of the encoder part of the model, Modified Deeplabv3+ incorporates a lightweight MobileNetV2 network. A convolutional attention module was introduced to improve the focus on the features of black and odorous water. Then, a fuzzy block was crafted to reduce the uncertainty of the raw data. Additionally, a new loss function was formulated to solve the problem of category imbalance. A series of experiments were conducted on both remote sensing images for the black and odorous water detection (RSBD) dataset and the water pollution dataset, demonstrating that the Modified DeepLabv3+ model outperforms other commonly used semantic segmentation networks. It effectively captures detailed information and reduces image segmentation errors. In addition, in order to better identify black and odorous water and enrich the spectral information of the image, we have generated derived bands using the black and odorous water index. These derived bands were fused together with the original image to construct the RSBD-II dataset. The experimental results show that adding a black and odorous water feature index can achieve a better detection effect.

show abstract

Section: Fuzzy Blockmentioning

confidence: 99%

Detection of Black and Odorous Water in Gaofen-2 Remote Sensing Images Using the Modified DeepLabv3+ Model

Huang,

Xu,

Yan

et al. 2023

Sustainability

Self Cite

View full text Add to dashboard Cite

show abstract

“…Facial detection of animals in the livestock sector is the basis for their subsequent identification and is a key technology for improving management methods and increasing productivity. Due to the superior performance of deep neural networks for feature extraction on large-scale datasets, great progress has been made in target detection research [1] .…”

Section: Introductionmentioning

confidence: 99%

Comparison of cow face target detection algorithms based on deep learning

Guan,

Yang,

Jing

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

View full text Add to dashboard Cite

To address issues in bull face detection, such as unsatisfactory inspection results and fragile inspection devices, we conducted a comparative study on existing representative deep network models (e.g. Mask R-CNN, YOLO, SSD, etc.) based on big data analysis discrepancy theory. The experimental results show that Mask R-CNN has the most efficient comprehensive analysis, although its detection rate is relatively slow. YOLO has a higher detection rate but less efficient comprehensive analysis compared to Mask R-CNN. SSD has the lowest comprehensive efficiency among the models.

show abstract

“…The works in the literature on neuro-fuzzy hybrids are performed either in an ensemble mode fusion pattern [13,14] or by applying the typical ANFIS model. Among the ensemble model fusion, some integrate fuzzy inference techniques as activation functions, and some adopt fuzzy inference for data-level fusion, such as pre-processing or post-processing [15,16] data. Still, the processing results of these approaches are not integral to feature-level fusion learning procedure.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Data Classification Based on the Integrated Fusion of Fuzzy Learning and Graph Neural Network

Snåšel

Štěpnička

Ojha

et al. 2023

Preprint

View full text Add to dashboard Cite

Fuzzy neighbourhood neural network for high-resolution remote sensing image segmentation

Cited by 10 publications

References 70 publications

Detection of Black and Odorous Water in Gaofen-2 Remote Sensing Images Using the Modified DeepLabv3+ Model

Detection of Black and Odorous Water in Gaofen-2 Remote Sensing Images Using the Modified DeepLabv3+ Model

Comparison of cow face target detection algorithms based on deep learning

Large-Scale Data Classification Based on the Integrated Fusion of Fuzzy Learning and Graph Neural Network

Contact Info

Product

Resources

About