In:
Photogrammetric Engineering & Remote Sensing, American Society for Photogrammetry and Remote Sensing, Vol. 88, No. 8 ( 2022-08-01), p. 535-546
Abstract:
Hyperspectral image (HSI) classification is the most vibrant research field in the hyperspectral community, aiming to assign each pixel in the image to one certain land cover category based on its spectral or spectral-spatial characteristics. Recently, some spectral-spatial–feature
deep learning–based convolutional neural networks have been proposed and demonstrated remarkable classification performance. However, these networks are time consuming when facing a real HSI in practical application. The trained mode l must be forwarded independently across m ×
m crops of the image in strides of 1 pixel. In this article, an efficient and practical network was proposed for HSI classification that can take an HSI as an input instance and directly output a dense pixel-level classification map. First, a novel mechanism, training based on pixels and prediction based on images (TPPI ), is proposed and formulated. Second, some basic rules that should be obeyed during network design and implementation are given. Finally, following the basic rules, three TPPI -Nets are derived and demonstrated based on state-of- the-art classification networks. Experimental
results on three public data sets show that the proposed TPPI-Net can not only obtain higher classification accuracy than the existing DCNN-based methods but also greatly reduce the computational complexity of HSI classification.
Type of Medium:
Online Resource
ISSN:
0099-1112
DOI:
10.14358/PERS.21-00089R3
Language:
English
Publisher:
American Society for Photogrammetry and Remote Sensing
Publication Date:
2022
detail.hit.zdb_id:
2317128-5
