# A CNN With Multi-scale Convolution for Hyperspectral Image Classification using Target-Pixel-Orientation scheme Source: [A CNN With Multi-scale Convolution for Hyperspectral Image Classification using Target-Pixel-Orientation scheme]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:11:37.721Z Authors: Jayasree Saha, Yuvraj Khanna, Jayanta Mukherjee Published: 2020-01-30T07:45:07.000Z ## Abstract Recently, CNN is a popular choice to handle the hyperspectral image classification challenges. In spite of having such large spectral information in Hyper-Spectral Image(s) (HSI), it creates a curse of dimensionality. Also, large spatial variability of spectral signature adds more difficulty in classification problem. Additionally, training a CNN in the end to end fashion with scarced training examples is another challenging and interesting problem. In this paper, a novel target-patch-orientation method is proposed to train a CNN based network. Also, we have introduced a hybrid of 3D-CNN and 2D-CNN based network architecture to implement band reduction and feature extraction methods, respectively. Experimental results show that our method outperforms the accuracies reported in the existing state of the art methods. ## 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]()