Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Lightweight Spectral-Spatial Squeeze-and- Excitation Residual Bag-of-Features Learning for Hyperspectral Classification Subject The topic of the resource Bag-of-feature (BoF); convolutional neural networks (CNNs); hyperspectral image (HSI); residual network (ResNet) Description An account of the resource 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. Creator An entity primarily responsible for making the resource Roy S.K.; Chatterjee S.; Bhattacharyya S.; Chaudhuri B.B.; Platos J. Source A related resource from which the described resource is derived IEEE Transactions on Geoscience and Remote Sensing, Vol-58, No. 8, pp. 5277-5290. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2020-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/TGRS.2019.2961681" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TGRS.2019.2961681</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086138500&amp;doi=10.1109%2FTGRS.2019.2961681&amp;partnerID=40&amp;md5=f3e3751361d91ae4b53fc1098d9aacf8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086138500&amp;doi=10.1109%2fTGRS.2019.2961681&amp;partnerID=40&amp;md5=f3e3751361d91ae4b53fc1098d9aacf8</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 1962892; CODEN: IGRSD Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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