# 3D CNN with Localized Residual Connections for Hyperspectral Image Classification Source: [3D CNN with Localized Residual Connections for Hyperspectral Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:41.217Z Authors: Shivangi Dwivedi, Murari Mandal, Shekhar Yadav, Santosh Kumar Vipparthi Published: 2019-12-06T06:46:01.000Z ## Abstract In this paper we propose a novel 3D CNN network with localized residual connections for hyperspectral image classification. Our work chalks a comparative study with the existing methods employed for abstracting deeper features and propose a model which incorporates residual features from multiple stages in the network. The proposed architecture processes individual spatiospectral feature rich cubes from hyperspectral images through 3D convolutional layers. The residual connections result in improved performance due to assimilation of both low-level and high-level features. We conduct experiments over Pavia University and Pavia Center dataset for performance analysis. We compare our method with two recent state-of-the-art methods for hyperspectral image classification method. The proposed network outperforms the existing approaches by a good margin. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()