Zhonghao Chen

TL;DR: The proposed method, SFE-SCNN, introduces the spectral feature enhancement operation, which makes the data reflect more discriminative spectral feature details to suppress the interference of mixed pixels and achieves better classification performance than other state-of-the-art methods.

TL;DR: Wang et al. as discussed by the authors proposed a multi-scale feature extraction (MSFE) module to extract spatial-spectral features at a granular level and expand the range of receptive fields, thereby enhancing the MSFE ability.

TL;DR: Zhang et al. as discussed by the authors investigated the limitations of current CNN-based methods for hyperspectral image (HSI) feature extraction and utilization, and proposed a novel spectral band non-localization (SBNL) operation to enable the nonlocal spectral interband correlations to be excavated by convolutional kernels with limited receptive fields.

TL;DR: Zhang et al. as discussed by the authors proposed a global to local hierarchical detection algorithm for hyperspectral image (G2LHTD), where extended morphological attribute profile (EMAP) is first used to model global spatial texture information from HSI, and a diverse-direction constrained energy minimization (CEM) detector is developed to consider the spatial information within eight neighborhoods around each pixel in HSI.

TL;DR: Wang et al. as mentioned in this paper proposed a hierarchical shrinkage multiscale feature extraction network by pruning MDMSRB to reduce the redundancy of network structure, and the proposed network hierarchically integrates low-level edge features and high-level semantic features effectively.