Improved folded-PCA for efficient remote sensing hyperspectral image classification

Hyperspectral images (HSIs) contain notable information of land objects by acquiring an immense set of narrow and contiguous spectral bands. Feature extraction (FE) and feature selection (FS) as dimensionality (band) reduction strategies are performed to enhance the classification result of HSI. Principal component analysis (PCA) is frequently exploited for the FE of HSI. However, it often possesses the inability to extract local and subtle HSI structures. As such, segmented-PCA (SPCA), spectrally segmented-PCA (SSPCA) and folded-PCA (FPCA) are presented for local and useful FE from the HSI. In this paper, we propose two FE methods called segmented-FPCA (SFPCA) and spectrally segmented-FPCA (SSFPCA). SFPCA exploits SPCA and FPCA while SSFPCA exploits SSPCA and FPCA together. In particular, SFPCA and SSFPCA apply FPCA on highly correlated and spectrally grouped HSI bands, respectively. We consider nonlinear methods Kernel-PCA (KPCA) and Kernel entropy component analysis (KECA) for extended comparison. For the experimented agricultural Indian Pine and urban Washington DC Mall HSIs, the results manifest that SFPCA (95.6262% for the agricultural HSI and 97.4782% for the urban HSI) and SSFPCA (96.3221% for the agricultural HSI and 98.0116% for the urban HSI) outperform the conventional methods.

Automated summary

Hyperspectral images contain important information of land objects acquired through numerous narrow and contiguous spectral bands. Different strategies of feature extraction and selection are used to enhance the classification results. Despite the common use of PCA, it may not effectively capture local and subtle structures in HSIs. New methods like SFPCA and SSFPCA outperform traditional approaches by applying FPCA on highly correlated and spectrally grouped HSI bands.