Affiliation:
1. School of Software Engineering, Tongji University, Shanghai 201804, China
2. School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
Abstract
Hyperspectral imaging (HSI) offers rich spectral and spatial data, beneficial for a variety of applications. However, challenges persist in HSI classification due to spectral variability, non-linearity, limited samples, and a dearth of spatial information in conventional spectral classifiers. While various spectral–spatial classifiers and dimension reduction techniques have been developed to mitigate these issues, they are often constrained by the utilization of handcrafted features. Deep learning has been introduced to HSI classification, with pixel- and patch-level deep learning (DL) classifiers gaining substantial attention. Yet, existing patch-level DL classifiers encounter difficulties in concentrating on long-distance dependencies and managing category areas of diverse sizes. The proposed Self-Adaptive 3D atrous spatial pyramid pooling (ASPP) Multi-Scale Feature Fusion Network (SAAFN) addresses these challenges by simultaneously preserving high-resolution spatial detail data and high-level semantic information. This method integrates a modified hyperspectral superpixel segmentation technique, a multi-scale 3D ASPP convolution block, and an end-to-end framework to extract and fuse multi-scale features at a self-adaptive rate for HSI classification. This method significantly enhances the classification accuracy of HSI with limited samples.
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference47 articles.
1. Classification of hyperspectral remote sensing images with support vector machines;Melgani;IEEE Trans. Geosci. Remote Sens.,2004
2. Spectral-spatial classification of hyperspectral images with a super-pixel-based discriminative sparse model;Fang;IEEE Trans. Geosci. Remote Sens.,2015
3. A New Spatial–Spectral Feature Extraction Method for Hyperspectral Images Using Local Covariance Matrix Representation;Fang;IEEE Trans. Geosci. Remote Sens.,2018
4. Bio-Inspired Adaptive Hyperspectral Imaging for Real-Time Target Tracking;Wang;IEEE Sensors J.,2010
5. Uzkent, B., Hoffman, M.J., and Vodacek, A. (2016, January 27–30). Real–time vehicle tracking in aerial video using hyperspectral features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Las Vegas, NV, USA.
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