# Objective Evaluation-based High-efficiency Learning Framework for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2302.05297 · HTML: https://mlsanity.com/paper/2302.05297/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 57 references that are in the map.
- First posted on arXiv 10 Jan 2023.

## Builds on (most cited first, 17 of 17)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Deeply supervised salient object detection with short connections](https://mlsanity.com/paper/1611.04849/) (2016)
- [SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers](https://mlsanity.com/paper/2107.02988/) (2021)
- [Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks](https://mlsanity.com/paper/2003.09514/) (2020)
- [Going Deeper with Contextual CNN for Hyperspectral Image Classification](https://mlsanity.com/paper/1604.03519/) (2016)
- [FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification](https://mlsanity.com/paper/2011.05670/) (2020)
- [Validating Hyperspectral Image Segmentation](https://mlsanity.com/paper/1811.03707/) (2018)
- [A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification](https://mlsanity.com/paper/2105.14327/) (2021)
- [Efficient Deep Learning of Non-local Features for Hyperspectral Image Classification](https://mlsanity.com/paper/2008.00542/) (2020)
- [On the Sampling Strategy for Evaluation of Spectral-spatial Methods in Hyperspectral Image Classification](https://mlsanity.com/paper/1605.05829/) (2016)
- [Effectiveness of Self Normalizing Neural Networks for Text Classification](https://mlsanity.com/paper/1905.01338/) (2019)

## Abstract (arXiv)

Deep learning methods have been successfully applied to hyperspectral image (HSI) classification with remarkable performance. Because of limited labelled HSI data, earlier studies primarily adopted a patch-based classification framework, which divides images into overlapping patches for training and testing. However, this approach results in redundant computations and possible information leakage. In this study, we propose an objective evaluation-based high-efficiency learning framework for tiny HSI classification. This framework comprises two main parts: (i) a leakage-free balanced sampling strategy, and (ii) a modified end-to-end fully convolutional network (FCN) architecture that optimizes the trade-off between accuracy and efficiency. The leakage-free balanced sampling strategy generates balanced and non-overlapping training and testing data by partitioning an HSI and the ground truth image into small windows, each of which corresponds to one training or testing sample. The proposed high-efficiency FCN exhibits a pixel-to-pixel architecture with modifications aimed at faster inference speed and improved parameter efficiency. Experiments conducted on four representative datasets demonstrated that the proposed sampling strategy can provide objective performance evaluation and that the proposed network outperformed many state-of-the-art approaches with respect to the speed/accuracy tradeoff. Our source code is available at https://github.com/xmzhang2018.