Lightweight Spectral-Spatial Squeeze-and- Excitation Residual Bag-of-Features Learning for Hyperspectral Classification
- Title
- Lightweight Spectral-Spatial Squeeze-and- Excitation Residual Bag-of-Features Learning for Hyperspectral Classification
- Creator
- Roy S.K.; Chatterjee S.; Bhattacharyya S.; Chaudhuri B.B.; Platos J.
- Description
- Of late, convolutional neural networks (CNNs) find great attention in hyperspectral image (HSI) classification since deep CNNs exhibit commendable performance for computer vision-related areas. CNNs have already proved to be very effective feature extractors, especially for the classification of large data sets composed of 2-D images. However, due to the existence of noisy or correlated spectral bands in the spectral domain and nonuniform pixels in the spatial neighborhood, HSI classification results are often degraded and unacceptable. However, the elementary CNN models often find intrinsic representation of pattern directly when employed to explore the HSI in the spectral-spatial domain. In this article, we design an end-to-end spectral-spatial squeeze-and-excitation (SE) residual bag-of-feature (S3EResBoF) learning framework for HSI classification that takes as input raw 3-D image cubes without engineering and builds a codebook representation of transform feature by motivating the feature maps facilitating classification by suppressing useless feature maps based on patterns present in the feature maps. To boost the classification performance and learn the joint spatial-spectral features, every residual block is connected to every other 3-D convolutional layer through an identity mapping followed by an SE block, thereby facilitating the rich gradients through backpropagation. Additionally, we introduce batch normalization on every convolutional layer (ConvBN) to regularize the convergence of the network and scale invariant BoF quantization for the measure of classification. The experiments conducted using three well-known HSI data sets and compared with the state-of-the-art classification methods reveal that S3EResBoF provides competitive performance in terms of both classification and computation time. 1980-2012 IEEE.
- Source
- IEEE Transactions on Geoscience and Remote Sensing, Vol-58, No. 8, pp. 5277-5290.
- Date
- 2020-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Bag-of-feature (BoF); convolutional neural networks (CNNs); hyperspectral image (HSI); residual network (ResNet)
- Coverage
- Roy S.K., Department of Computer Science and Engineering, Jalpaiguri Government Engineering College, Jalpaiguri, India; Chatterjee S., Department of Computer Science and Engineering, Iit Kharagpur, Kharagpur, India; Bhattacharyya S., Department of Computer Science and Engineering, Christ University, Bangalore, Bengaluru, India; Chaudhuri B.B., Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India; Platos J., Department of Electrical Engineering and Computer Science, VB-Techincal University of Ostrava, Ostrava, Czech Republic
- Rights
- Restricted Access
- Relation
- ISSN: 1962892; CODEN: IGRSD
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Roy S.K.; Chatterjee S.; Bhattacharyya S.; Chaudhuri B.B.; Platos J., “Lightweight Spectral-Spatial Squeeze-and- Excitation Residual Bag-of-Features Learning for Hyperspectral Classification,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 28, 2025, https://archives.christuniversity.in/items/show/16279.