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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 2023) | Viewed by 7169

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Special Issue Editors

Dr. Jens Nieke

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Guest Editor

ESA European Space Agency (ESA ESTEC), Keplerlaan 1, 2200 AG Noordwijk, The Netherlands
Interests: imaging spectroscopy; hyperspectral data processing

Dr. Nafiseh Ghasemi

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Guest Editor

ESA European Space Agency (ESA ESTEC), Keplerlaan 1, 2200 AG Noordwijk, The Netherlands
Interests: synthetic aperture radar; forestry; vegetation and agriculture monitoring

Special Issue Information

Dear Colleagues,

The recent advances of hyperspectral imaging missions (PRISMA, ENMAP, EMITS, HYPERSCOUT) is enabling access to a large variety and increased quality of hyperspectral data. The increased number of spectroscopic measurements from these missions will allow us to derive algorithms and products to account for the need to observe quantitative surface characteristics supporting the monitoring, implementation, and improvement of a range of policies in the domains of agriculture, food security, raw materials, soils, biodiversity, environmental degradation and hazards, inland and coastal waters, and forestry.

Recently, also, new upcoming operational hyperspectral missions such as CHIME and SBG will pave the way to increase the pool of available and to-be-processed data. The amount of data continuously generated and/or available in an increasing data pool is creating great challenges, such as in-time data dissemination, complex dimensionality of datasets and structures, and the large variety of data quality and user end-product requirements.

There is no other means than applying new computational intelligence tools to address those challenges so that certain elements in the product generation chain can be addressed (e.g., on the level of pre-processing, cloud detection and fast data product retrieval, in general).

A fundamental need is the access to improved hyperspectral data processing technologies to pave the way towards operational retrieval across a large variety of applications. This Special Issue of Remote Sensing will allow invited authors to publish recent advances related to:

Increased onboard satellite processing.
Increased on-ground hyperspectral data processing capability using advanced (e.g., AI) algorithms and technologies, allowing the reduction of data transmissions and/or the acceleration of decision making for rapid-response scenarios.

Dr. Jens Nieke
Dr. Nafiseh Ghasemi
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

algorithms
artificial intelligence
computational intelligence
evolutionary algorithm
expert system
knowledge representation
neural network
programming
data compression
big data processing

Published Papers (5 papers)

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Research

19 pages, 8497 KiB

Open AccessArticle

Reshaping Leaf-Level Reflectance Data for Plant Species Discrimination: Exploring Image Shape’s Impact on Deep Learning Results

by Shaoxiong Yuan, Guangman Song, Qinghua Gong, Quan Wang, Jun Wang and Jun Chen

Remote Sens. 2023, 15(24), 5628; https://doi.org/10.3390/rs15245628 - 5 Dec 2023

Viewed by 756

Abstract

The application of hyperspectral imagery coupled with deep learning shows vast promise in plant species discrimination. Reshaping one-dimensional (1D) leaf-level reflectance data (LLRD) into two-dimensional (2D) grayscale images as convolutional neural network (CNN) model input demonstrated marked effectiveness in plant species distinction. However, the impact of the image shape on CNN model performance remained unexplored. This study addressed this by reshaping data into fifteen distinct rectangular formats and creating nine CNN models to examine the effect of image structure. Results demonstrated that irrespective of CNN model structure, elongated narrow images yielded superior species identification results. The ‘l’-shaped images at 225 × 9 pixels outperformed other configurations based on 93.95% accuracy, 94.55% precision, and 0.94 F1 score. Furthermore, ‘l’-shaped hyperspectral images consistently produced high classification precision across species. The results suggest this image shape boosts robust predictive performance, paving the way for enhancing leaf trait estimation and proposing a practical solution for pixel-level categorization within hyperspectral imagery (HSIs). Full article

(This article belongs to the Special Issue Computational Intelligence in Hyperspectral Remote Sensing)

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21 pages, 8672 KiB

Open AccessArticle

Contrastive Self-Supervised Two-Domain Residual Attention Network with Random Augmentation Pool for Hyperspectral Change Detection

by Yixiang Huang, Lifu Zhang, Wenchao Qi, Changping Huang and Ruoxi Song

Remote Sens. 2023, 15(15), 3739; https://doi.org/10.3390/rs15153739 - 27 Jul 2023

Cited by 3 | Viewed by 911

Abstract

Hyperspectral images can assist change-detection methods in precisely identifying differences in land cover in the same region at different observation times. However, the difficulty of labeling hyperspectral images restricts the number of training samples for supervised change-detection methods, and there are also complex real influences on hyperspectral images, such as noise and observation directions. Furthermore, current deep-learning-based change-detection methods ignore the feature reusage from receptive fields with different scales and cannot effectively suppress unrelated spatial–spectral dependencies globally. To better handle these issues, a contrastive self-supervised two-domain residual attention network (TRAMNet) with a random augmentation pool is proposed for hyperspectral change detection. The contributions of this article are summarized as follows. (1) To improve the feature extraction from hyperspectral images with random Gaussian noise and directional information, a contrastive learning framework with a random data augmentation pool and a soft contrastive loss function (SCLF) is proposed. (2) The multi-scale feature fusion module (MFF) is provided to achieve feature reusage from different receptive fields. (3) A two-domain residual attention (TRA) block is designed to suppress irrelated change information and extract long-range dependencies from both spectral and spatial domains globally. Extensive experiments were carried out on three real datasets. The results show that the proposed TRAMNet can better initialize the model weights for hyperspectral change-detection task and effectively decrease the need for training samples. The proposed method outperforms most existing hyperspectral change-detection methods. Full article

(This article belongs to the Special Issue Computational Intelligence in Hyperspectral Remote Sensing)

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25 pages, 37756 KiB

Open AccessArticle

Hyperspectral Anomaly Detection Using Spatial–Spectral-Based Union Dictionary and Improved Saliency Weight

by Sheng Lin, Min Zhang, Xi Cheng, Shaobo Zhao, Lei Shi and Hai Wang

Remote Sens. 2023, 15(14), 3609; https://doi.org/10.3390/rs15143609 - 19 Jul 2023

Cited by 3 | Viewed by 1057

Abstract

Hyperspectral anomaly detection (HAD), which is widely used in military and civilian fields, aims to detect the pixels with large spectral deviation from the background. Recently, collaborative representation using union dictionary (CRUD) was proved to be effective for achieving HAD. However, the existing CRUD detectors generally only use the spatial or spectral information to construct the union dictionary (UD), which possibly causes a suboptimal performance and may be hard to use in actual scenarios. Additionally, the anomalies are treated as salient relative to the background in a hyperspectral image (HSI). In this article, a HAD method using spatial–spectral-based UD and improved saliency weight (SSUD-ISW) is proposed. To construct robust UD for each testing pixel, a spatial-based detector, a spectral-based detector and superpixel segmentation are jointly considered to yield the background set and anomaly set, which provides pure and representative pixels to form a robust UD. Differently from the conventional operation that uses the dual windows to construct the background dictionary in the local region and employs the RX detector to construct the anomaly dictionary in a global scope, we developed a robust UD construction strategy in a nonglobal range by sifting the pixels closest to the testing pixel from the background set and anomaly set to form the UD. With a preconstructed UD, a CRUD is performed, and the product of the anomaly dictionary and corresponding representation coefficient is explored to yield the response map. Moreover, an improved saliency weight is proposed to fully mine the saliency characteristic of the anomalies. To further improve the performance, the response map and saliency weight are combined with a nonlinear fusion strategy. Extensive experiments performed on five datasets (i.e., Salinas, Texas Coast, Gainesville, San Diego and SpecTIR datasets) demonstrate that the proposed SSUD-ISW detector achieves the satisfactory AUC_df values (i.e., 0.9988, 0.9986, 0.9939, 0.9945 and 0.9997), as compared to the comparative detectors whose best AUC_df values are 0.9938, 0.9956, 0.9833, 0.9919 and 0.9991. Full article

(This article belongs to the Special Issue Computational Intelligence in Hyperspectral Remote Sensing)

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24 pages, 1971 KiB

Open AccessArticle

Multi-Scale Spectral-Spatial Attention Network for Hyperspectral Image Classification Combining 2D Octave and 3D Convolutional Neural Networks

by Lianhui Liang, Shaoquan Zhang, Jun Li, Antonio Plaza and Zhi Cui

Remote Sens. 2023, 15(7), 1758; https://doi.org/10.3390/rs15071758 - 24 Mar 2023

Cited by 7 | Viewed by 2057

Abstract

Traditional convolutional neural networks (CNNs) can be applied to obtain the spectral-spatial feature information from hyperspectral images (HSIs). However, they often introduce significant redundant spatial feature information. The octave convolution network is frequently utilized instead of traditional CNN to decrease spatial redundant information of the network and extend its receptive field. However, the 3D octave convolution-based approaches may introduce extensive parameters and complicate the network. To solve these issues, we propose a new HSI classification approach with a multi-scale spectral-spatial network-based framework that combines 2D octave and 3D CNNs. Our method, called MOCNN, first utilizes 2D octave convolution and 3D DenseNet branch networks with various convolutional kernel sizes to obtain complex spatial contextual feature information and spectral characteristics, separately. Moreover, the channel and the spectral attention mechanisms are, respectively, applied to these two branch networks to emphasize significant feature regions and certain important spectral bands that comprise discriminative information for the categorization. Furthermore, a sample balancing strategy is applied to address the sample imbalance problem. Expansive experiments are undertaken on four HSI datasets, demonstrating that our MOCNN approach outperforms several other methods for HSI classification, especially in scenarios dominated by limited and imbalanced sample data. Full article

(This article belongs to the Special Issue Computational Intelligence in Hyperspectral Remote Sensing)

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22 pages, 16209 KiB

Open AccessArticle

From Video to Hyperspectral: Hyperspectral Image-Level Feature Extraction with Transfer Learning

by Yifan Sun, Bing Liu, Xuchu Yu, Anzhu Yu, Kuiliang Gao and Lei Ding

Remote Sens. 2022, 14(20), 5118; https://doi.org/10.3390/rs14205118 - 13 Oct 2022

Cited by 8 | Viewed by 1697

Abstract

Hyperspectral image classification methods based on deep learning have led to remarkable achievements in recent years. However, these methods with outstanding performance are also accompanied by problems such as excessive dependence on the number of samples, poor model generalization, and time-consuming training. Additionally, the previous patch-level feature extraction methods have some limitations, for instance, non-local information is difficult to model, etc. To solve these problems, this paper proposes an image-level feature extraction method with transfer learning. Firstly, we look at a hyperspectral image with hundreds of contiguous spectral bands from a sequential image perspective. We attempt to extract the global spectral variation information between adjacent spectral bands by using the optical flow estimation method. Secondly, we propose an innovative data adaptation strategy to bridge the gap between hyperspectral and video data, and transfer the optical flow estimation network pre-trained with video data to the hyperspectral feature extraction task for the first time. Thirdly, we utilize the traditional classifier to achieve classification. Simultaneously, a vote strategy combined with features at different scales is proposed to improve the classification accuracy further. Extensive, well-designed experiments on four scenes of public hyperspectral images demonstrate that the proposed method (Spe-TL) can obtain results that are competitive with advanced deep learning methods under various sample conditions, with better time effectiveness to adapt to new target tasks. Moreover, it can produce more detailed classification maps that subtly reflect the authentic distribution of ground objects in the original image. Full article

(This article belongs to the Special Issue Computational Intelligence in Hyperspectral Remote Sensing)

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