3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image Classification

01/12/2021
by   Haokui Zhang, et al.
11

Hyperspectral images involve abundant spectral and spatial information, playing an irreplaceable role in land-cover classification. Recently, based on deep learning technologies, an increasing number of HSI classification approaches have been proposed, which demonstrate promising performance. However, previous studies suffer from two major drawbacks: 1) the architecture of most deep learning models is manually designed, relies on specialized knowledge, and is relatively tedious. Moreover, in HSI classifications, datasets captured by different sensors have different physical properties. Correspondingly, different models need to be designed for different datasets, which further increases the workload of designing architectures; 2) the mainstream framework is a patch-to-pixel framework. The overlap regions of patches of adjacent pixels are calculated repeatedly, which increases computational cost and time cost. Besides, the classification accuracy is sensitive to the patch size, which is artificially set based on extensive investigation experiments. To overcome the issues mentioned above, we firstly propose a 3D asymmetric neural network search algorithm and leverage it to automatically search for efficient architectures for HSI classifications. By analysing the characteristics of HSIs, we specifically build a 3D asymmetric decomposition search space, where spectral and spatial information are processed with different decomposition convolutions. Furthermore, we propose a new fast classification framework, i,e., pixel-to-pixel classification framework, which has no repetitive operations and reduces the overall cost. Experiments on three public HSI datasets captured by different sensors demonstrate the networks designed by our 3D-ANAS achieve competitive performance compared to several state-of-the-art methods, while having a much faster inference speed.

READ FULL TEXT

page 1

page 6

page 7

page 9

page 10

page 11

page 12

page 13

research
10/21/2021

3D-ANAS v2: Grafting Transformer Module on Automatically Designed ConvNet for Hyperspectral Image Classification

Hyperspectral image (HSI) classification has been a hot topic for decide...
research
04/16/2019

Deep Neural Network Based Hyperspectral Pixel Classification With Factorized Spectral-Spatial Feature Representation

Deep learning has been widely used for hyperspectral pixel classificatio...
research
02/23/2023

A2S-NAS: Asymmetric Spectral-Spatial Neural Architecture Search For Hyperspectral Image Classification

Existing deep learning-based hyperspectral image (HSI) classification wo...
research
11/15/2022

Probabilistic Deep Metric Learning for Hyperspectral Image Classification

This paper proposes a probabilistic deep metric learning (PDML) framewor...
research
04/23/2023

HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel Neural Architecture Search

Recent neural architecture search (NAS) based approaches have made great...
research
04/21/2022

GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification

Hyperspectral image (HSI) classification is the most vibrant area of res...
research
10/28/2021

MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning

Tiny deep learning on microcontroller units (MCUs) is challenging due to...

Please sign up or login with your details

Forgot password? Click here to reset