Objective evaluation-based efficient learning framework for hyperspectral image classification

doi:10.1080/15481603.2023.2225273

Objective evaluation-based efficient learning framework for hyperspectral image classification

2023-06-17

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DOI：10.1080/15481603.2023.2225273

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Deep learning techniques with remarkable performance have been successfully applied to hyperspectral image (HSI) classification. Due to the limited availability of training data, earlier studies primarily adopted the patch-based classification framework, which divides images into overlapping patches for training and testing. However, this framework results in redundant computations and possible information leakage. This study proposes an objective evaluation-based efficient learning framework for HSI classification. It consists of two main parts: (i) a leakage-free balanced sampling strategy and (ii) an efficient fully convolutional network (EfficientFCN) optimized for the accuracy-efficiency trade-off. The leakage-free balanced sampling strategy first generates balanced and non-overlapping training and test data by partitioning the HSI and its ground truth image into non-overlapping windows. Then, the generated training and test data are used to train and test the proposed EfficientFCN. EfficientFCN exhibits a pixel-to-pixel architecture with modifications for faster inference speed and improved parameter efficiency. Experimental results demonstrate that the proposed sampling strategy can provide objective performance evaluation. EfficientFCN outperforms many state-of-the-art approaches concerning the speed-accuracy trade-off. For instance, compared to the recent efficient models EfficientNetV2 and ConvNeXt, EfficientFCN achieves 0.92% and 3.42% superior accuracy and 0.19s and 0.16s faster inference time, respectively, on the Houston dataset. Code is available at .

Keyword:

Deep learning

fully convolutional network

features extraction

sampling strategy

Objective evaluation-based efficient learning framework for hyperspectral image classification

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