Hyperspectral Image Classification Based on Dense Convolution and Domain Adaptation

Chunhui ZHAO; Tong LI; Shou FENG

doi:10.3788/gzxb20215003.0310001

[1] Q J JIAO, B ZHANG, J G LIU, L Y LIU. A novel two-step method for winter wheat-leaf chlorophyll content estimation using a hyperspectral vegetation index. International Journal of Remote Sensing, 35, 7363-7375(2014).

[2] Bing ZHANG. Advancement of hyperspectral image processing and information extraction. Journal of Remote Sensing, 20, 1062-1090.

[3] Dongdong XU, Deqiang CHENG, Liangliang CHEN. Hyperspectral image classification based on hierarchical guidance filtering and nearest regularized subspace. Acta Photonica Sinica, 49, 197-209(2020).

[4] F MELGANI, L BRUZZONE. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42, 1778-1790(2004).

[5] I KHOSRAVI, Y JOUYBARI-MOGHADDAM. Hyperspectral imbalanced datasets classification using filter-based forest methods. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 4766-4772(2019).

[6] Extreme-learning-machine-based land cover classification. International Journal of Remote Sensing, 30, 3835-3841(2009).

[7] Y S CHEN, H L JIANG. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 54, 6232-6251(2016).

[8] S RAJAN, J GHOSH, M M CRAWFORD. An active learning approach to hyperspectral data classification. IEEE Transactions on Geoscience and Remote Sensing, 46, 1231-1242(2008).

[9] M M CRAWFORD, D TUIA, H L YANG. Active learning： Any value for classification of remotely sensed data？, 101, 593-608(2013).

[10] M CHI, L BRUZZONE. Semisupervised classification of hyperspectral images by SVMs optimized in the primal. IEEE Transactions on Geoscience and Remote Sensing, 45, 1870-1880(2007).

[11] G CAMPS-VALLS, T V B MARSHEVA, D ZHOU. Semi-supervised graph-based hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 45, 3044-3054(2007).

[12] W KIM, M M CRAWFORD. Adaptive classification for hyperspectral image data using manifold regularization kernel machines. IEEE Transactions on Geoscience and Remote Sensing, 48, 4110-4121(2010).

[13] M YE, Y QIAN, J ZHOU, Y Y TANG. Dictionary learning-based feature-level domain adaptation for cross-scene hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 55, 1544-1562(2017).

[14] X ZHOU, S PRASAD. Domain adaptation for robust classification of disparate hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 3, 822-836(2017).

[15] D TUIA, C PERSELLO, L BRUZZONE. Domain adaptation for the classification of remote sensing data： An overview of recent advances. IEEE Transactions on Geoscience and Remote Sensing Magazine, 4, 41-57(2016).

[16] L BRUZZONE, M CHI, M MARCONCINI. A novel transductive SVM for semisupervised classification of remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing, 44, 3363-3373(2006).

[17] Ying LI, Lifu ZHANG, Wei YAN. Weed identification using imaging spectrometer data. Journal of Remote Sensing, 8, 855-871(2013).

[18] Bin DENG, Sen JIA, Daming SHI. Deep metric learning-based feature embedding for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 58, 1422-1435(2020).

[19] G HUANG, Z LIU. Densely connected convolutional networks. HI, 2261-2269(2017).

[20] W WANG, S DOU, Z JIANG. A fast dense spectral-spatial convolution network framework for hyperspectral images classification. Remote sensing, 10, 1068(2018).

[21] C SZEGEDY, W LIU, Y Q JIA. Going deeper with convolutions. MA, 1-9(2015).

[22] K HE, X ZHANG, S REN, J SUN. Deep residual learning for image recognition. WA, 770-778(2016).