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Special Issue "Interactive Deep Learning for Hyperspectral Images"

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 16322

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Special Issue Editors

Dr. Muhammad Ahmad

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Guest Editor

Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan
Interests: Machine Learning; Image and Signal Processing; Computer Vision; Hyperspectral Imaging; Wearable Computing
Special Issues, Collections and Topics in MDPI journals

Dr. Adil Mehmood Khan

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Guest Editor

Machine Learning & Knowledge Representation Lab, Innopolis University, Innopolis, Russia
Interests: machine learning; data mining; pattern recognition; context-aware computing; intelligent systems; data modeling and analysis
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Dr. Diego Oliva

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Guest Editor

Depto. de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jalisco, Mexico
Interests: metaheuristic algorithms; bioinspired computation; image processing; machine learning; optimization
Special Issues, Collections and Topics in MDPI journals

Dr. Omar Nibouche

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Guest Editor

School of Computing, Ulster University, Jordanstown, UK
Interests: chemometrics; sensing; machine learning
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Special Issue Information

Deep models have achieved remarkable results in many real-world applications based on quality-labeled training samples. However, accruing reliable training samples is expensive in many real-world applications, particularly in the remote sensing domain. Therefore, there is a need to develop interactive deep schemes that can help to attain reliable, informative, and heterogeneous samples for learning. For this Special Issue of Remote Sensing, we aim to present a collection of articles related to “Interactive Deep Learning for Hyperspectral Images” including, but not limited to, the following topics:

1. Investigation of the behavior and performance of deep models in terms of the computational cost and generalized performance for both on-shelf and novel classification methods under different experimental conditions.

2. Development of several novel strategies to limit the Hughes phenomenon for hyperspectral image classification by exploiting several on-shelf and novel sample selection methods. This includes the generation of a good spectral library, the development of suitable models to upscale the ground spectra to air and space-borne measurements, evolving application methodologies, and device algorithms for pre and post-processing hyperspectral cubes.

Dr. Muhammad Ahmad
Dr. Adil Mehmood Khan
Dr. Diego Oliva
Dr. Omar Nibouche
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

multi-level hyperspectral
multi-sensor hyperspectral
image processing
data fusion for deep models
learning frameworks for deep models
iot for hyperspectral imaging
tropical environment
haze removal for remotely sensed images

Published Papers (3 papers)

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Research

16 pages, 4938 KiB

Open AccessCommunication

TWC-Net: A SAR Ship Detection Using Two-Way Convolution and Multiscale Feature Mapping

and

Remote Sens. 2021, 13(13), 2558; https://doi.org/10.3390/rs13132558 - 30 Jun 2021

Cited by 25 | Viewed by 2167

Abstract

Synthetic aperture radar (SAR) is an active earth observation system with a certain surface penetration capability and can be employed to observations all-day and all-weather. Ship detection using SAR is of great significance to maritime safety and port management. With the wide application of in-depth learning in ordinary images and good results, an increasing number of detection algorithms began entering the field of remote sensing images. SAR image has the characteristics of small targets, high noise, and sparse targets. Two-stage detection methods, such as faster regions with convolution neural network (Faster RCNN), have good results when applied to ship target detection based on the SAR graph, but their efficiency is low and their structure requires many computing resources, so they are not suitable for real-time detection. One-stage target detection methods, such as single shot multibox detector (SSD), make up for the shortage of the two-stage algorithm in speed but lack effective use of information from different layers, so it is not as good as the two-stage algorithm in small target detection. We propose the two-way convolution network (TWC-Net) based on a two-way convolution structure and use multiscale feature mapping to process SAR images. The two-way convolution module can effectively extract the feature from SAR images, and the multiscale mapping module can effectively process shallow and deep feature information. TWC-Net can avoid the loss of small target information during the feature extraction, while guaranteeing good perception of a large target by the deep feature map. We tested the performance of our proposed method using a common SAR ship dataset SSDD. The experimental results show that our proposed method has a higher recall rate and precision, and the F-Measure is 93.32%. It has smaller parameters and memory consumption than other methods and is superior to other methods. Full article

(This article belongs to the Special Issue Interactive Deep Learning for Hyperspectral Images)

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23 pages, 4309 KiB

Open AccessArticle

Changes in Snow Cover Dynamics over the Indus Basin: Evidences from 2008 to 2018 MODIS NDSI Trends Analysis

Sikandar Ali

Muhammad Jehanzeb Masud Cheema

Muhammad Mohsin Waqas

Muhammad Waseem

Usman Khalid Awan

and

Tasneem Khaliq

Remote Sens. 2020, 12(17), 2782; https://doi.org/10.3390/rs12172782 - 27 Aug 2020

Cited by 19 | Viewed by 4457

Abstract

The frozen water reserves on the Earth are not only very dynamic in their nature, but also have significant effects on hydrological response of complex and dynamic river basins. The Indus basin is one of the most complex river basins in the world and receives most of its share from the Asian Water Tower (Himalayas). In such a huge river basin with high-altitude mountains, the regular quantification of snow cover is a great challenge to researchers for the management of downstream ecosystems. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) daily (MOD09GA) and 8-day (MOD09A1) products were used for the spatiotemporal quantification of snow cover over the Indus basin and the western rivers’ catchments from 2008 to 2018. The high-resolution Landsat Enhanced Thematic Mapper Plus (ETM+) was used as a standard product with a minimum Normalized Difference Snow Index (NDSI) threshold (0.4) to delineate the snow cover for 120 scenes over the Indus basin on different days. All types of errors of commission/omission were masked out using water, sand, cloud, and forest masks at different spatiotemporal resolutions. The snow cover comparison of MODIS products with Landsat ETM+, in situ snow data and Google Earth imagery indicated that the minimum NDSI threshold of 0.34 fits well compared to the globally accepted threshold of 0.4 due to the coarser resolution of MODIS products. The intercomparison of the time series snow cover area of MODIS products indicated R² values of 0.96, 0.95, 0.97, 0.96 and 0.98, for the Chenab, Jhelum, Indus and eastern rivers’ catchments and Indus basin, respectively. A linear least squares regression analysis of the snow cover area of the Indus basin indicated a declining trend of about 3358 and 2459 km² per year for MOD09A1 and MOD09GA products, respectively. The results also revealed a decrease in snow cover area over all the parts of the Indus basin and its sub-catchments. Our results suggest that MODIS time series NDSI analysis is a useful technique to estimate snow cover over the mountainous areas of complex river basins. Full article

(This article belongs to the Special Issue Interactive Deep Learning for Hyperspectral Images)

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14 pages, 2831 KiB

Open AccessArticle

Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

and

Remote Sens. 2020, 12(10), 1685; https://doi.org/10.3390/rs12101685 - 25 May 2020

Cited by 133 | Viewed by 8297

Abstract

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method. Full article

(This article belongs to the Special Issue Interactive Deep Learning for Hyperspectral Images)

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