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Hyperspectral image (HSI) classification has been long envisioned in the remote sensing community. Many methods have been proposed for HSI classification. Among them, the method of fusing spatial features has been widely used and achieved good performance. Aiming at the problem of spatial feature extraction in spectral-spatial HSI classification, we proposed a guided filter-based method. We attempted two fusion methods for spectral and spatial features. In order to optimize the classification results, we also adopted a guided filter to obtain better results. We apply the support vector machine (SVM) to classify the HSI. Experiments show that our proposed methods can obtain very competitive results than compared methods on all the three popular datasets. More importantly, our methods are fast and easy to implement.

In view of the relatively high noise interference and halo phenomenon of the traditional adaptive image enhancement algorithm based on the unsharp masking method, a kind of adaptive image enhancement algorithm based on the integration of the model of surface roughness detection system (hereinafter referred to as MSRDS for short) is put forward in this paper. Through the design of the model of the surface roughness detection system, non-linear segmentation, denoising, and adaptive amplification are carried out on the details of the image under this system model. The dynamic range compressed image base layer and the adaptively enhanced image detail layer are non-linearly superimposed to obtain the final enhanced image. Finally, through the comparative experiment analysis, it demonstrates that the method put forward in this paper can suppress the interference noise and the halo phenomenon of the image very well while carrying out dynamic range compression and detail amplification of the adaptive image effectively. And the result thus obtained is very suitable for the back-end image processing of the actual thermal infrared imager.

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