Category-Specific Prototype Self-Refinement Contrastive Learning for Few-Shot Hyperspectral Image Classification

Liu, Quanyong; Peng, Jiangtao; Chen, Na; Sun, Weiwei; Ning, Yujie; Du, Qian

doi:10.1109/tgrs.2023.3317077

Cited by 15 publications

(1 citation statement)

References 64 publications

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“…This typically requires individuals with professional knowledge to conduct on-site surveys and interpret multiple types of remote sensing images. Therefore, employing ML and DL for crop classification holds practical value as it significantly reduces manual annotation costs [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification on Large-Scale Agricultural Crops: The Heilongjiang Benchmark Dataset, Validation Procedure, and Baseline Results

Zhang,

Feng,

et al. 2024

Remote Sensing

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Over the past few decades, researchers have shown sustained and robust investment in exploring methods for hyperspectral image classification (HSIC). The utilization of hyperspectral imagery (HSI) for crop classification in agricultural areas has been widely demonstrated for its feasibility, flexibility, and cost-effectiveness. However, numerous coexisting issues in agricultural scenarios, such as limited annotated samples, uneven distribution of crops, and mixed cropping, could not be explored insightfully in the mainstream datasets. The limitations within these impractical datasets have severely restricted the widespread application of HSIC methods in agricultural scenarios. A benchmark dataset named Heilongjiang (HLJ) for HSIC is introduced in this paper, which is designed for large-scale crop classification. For practical applications, the HLJ dataset covers a wide range of genuine agricultural regions in Heilongjiang Province; it provides rich spectral diversity enriched through two images from diverse time periods and vast geographical areas with intercropped multiple crops. Simultaneously, considering the urgent demand of deep learning models, the two images in the HLJ dataset have 319,685 and 318,942 annotated samples, along with 151 and 149 spectral bands, respectively. To validate the suitability of the HLJ dataset as a baseline dataset for HSIC, we employed eight classical classification models in fundamental experiments on the HLJ dataset. Most of the methods achieved an overall accuracy of more than 80% with 10% of the labeled samples used for training. Furthermore, the advantages of the HLJ dataset and the impact of real-world factors on experimental results are comprehensively elucidated. The comprehensive baseline experimental evaluation and analysis affirm the research potential of the HLJ dataset as a large-scale crop classification dataset.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification on Large-Scale Agricultural Crops: The Heilongjiang Benchmark Dataset, Validation Procedure, and Baseline Results

Zhang,

Feng,

et al. 2024

Remote Sensing

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Intelligent classification and identification method for Conger myriaster freshness based on DWG‐YOLOv8 network model

Gao,

Wang,

et al. 2024

Food Bioengineering

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The freshness of aquatic products is directly related to the safety and health of the people. Traditional methods of detecting the freshness of Conger myriaster rely on manual operations, which are labor‐intensive, inefficient, and highly subjective. This paper combines computer vision and the DWG‐YOLOv8 network model to establish an intelligent classification method for C. myriaster freshness. Through image augmentation, 484 C. myriaster samples were expanded to 2904 samples. The YOLOv8n model was improved by simplifying the network backbone, introducing Ghost convolution and the new DW‐GhostConv, thereby reducing the number of parameters and computational load. Test results show that the recognition accuracy of the DWG‐YOLOv8 model reached 98.958%, outperforming models such as ResNet18, Mobilenetv3 small, and Swin transformer v2 tiny. The model's parameter count is 16.609 K, the inference time is 57.80 ms, and the model size is only 102 KB. The research provides a reliable method for online intelligent and nondestructive detection of C. myriaster freshness.

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A novel graph-based multiple kernel learning framework for hyperspectral image classification

Hassanzadeh,

Danyali,

Karami

et al. 2024

International Journal of Remote Sensing

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Category-Specific Prototype Self-Refinement Contrastive Learning for Few-Shot Hyperspectral Image Classification

Cited by 15 publications

References 64 publications

Hyperspectral Image Classification on Large-Scale Agricultural Crops: The Heilongjiang Benchmark Dataset, Validation Procedure, and Baseline Results

Hyperspectral Image Classification on Large-Scale Agricultural Crops: The Heilongjiang Benchmark Dataset, Validation Procedure, and Baseline Results

Intelligent classification and identification method for Conger myriaster freshness based on DWG‐YOLOv8 network model

A novel graph-based multiple kernel learning framework for hyperspectral image classification

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