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Deep Learning for Remote Sensing Image Classification II

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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 37229

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Special Issue Editors

Dr. Sidike Paheding

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Guest Editor

Department of Applied Computing, College of Computing, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA
Interests: machine learning; computer vision; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Ashraf Saleem

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Guest Editor

Department of Applied Computing, College of Computing, Michigan Technological University, Houghton, MI, USA
Interests: robotics system design; Integration of UAV-based remote sensing; AI applications in remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issue on the topic of Deep Learning for Remote Sensing Image Classification, we herein open its second volume to continuously provide a forum for potential authors to address existing challenges in remote sensing image classification using the state-of-the-art deep learning algorithms.

In recent years, we have been witnessing the tremendous success of deep learning in diverse research areas and applications, ranging from natural language processing, health care, wide-area surveillance, network security, and precision agriculture. The significance of deep learning in remote sensing image analysis has also been observed and is continuously being increased. Thanks to the rapid advancement of sensors, including high-resolution RGB, thermal, Lidar, and multi-/hyper-spectral cameras, and emerging sensing platforms, such as satellites and aerial vehicles, remote sensing image scenes can now be captured by multi-temporal, multi-senor, and provide a wider view of sensing devices. This undoubtfully facilitates remote sensing research fields, and, at the same time, introduces challenges, such as: 1) large quantities and wider areas of remote sensing image data, 2) high dimensional and noisy spectral and spatial information, and 3) the complexity of remote sensing scene itself. These challenges not only bring difficulties for image analytics and interpretation but also demands more advanced computational methods. Although many deep learning algorithms have been proposed to address some challenges, the problems are remaining. The objective of this Special Issue is to provide a forum for cutting-edge research works that address the ongoing challenges in remote sensing image classification. We welcome topics that include, but are not limited to:

Land-use land-cover mapping;
Hyperspectral image classification;
Data fusion technologies;
High-performance computing paradigm for remote sensing image classification;
Dimensionality reduction for remote sensing data;
Spatial and spectral feature extraction methods;
Big data analytics;
Data visualization of classification results;
Data augmentation for image classification;
New image classification architectures;
New datasets for remote sensing image classification with deep learning;
Image enhancement for image classification.

Dr. Sidike Paheding
Dr. Ashraf Saleem
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

deep learning
remote sensing
computer vision
adversarial learning
geospatial data analysis
scene classification
convolutional neural networks
feature extraction
dimensionality reduction
aerial and satellite images

Published Papers (13 papers)

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Research

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26 pages, 2531 KiB

Open AccessArticle

Adversarial Robust Aerial Image Recognition Based on Reactive-Proactive Defense Framework with Deep Ensembles

by Zihao Lu, Hao Sun, Kefeng Ji and Gangyao Kuang

Remote Sens. 2023, 15(19), 4660; https://doi.org/10.3390/rs15194660 - 22 Sep 2023

Viewed by 734

Abstract

As a safety-related application, visual systems based on deep neural networks (DNNs) in modern unmanned aerial vehicles (UAVs) show adversarial vulnerability when performing real-time inference. Recently, deep ensembles with various defensive strategies against adversarial samples have drawn much attention due to the increased diversity and reduced variance for their members. Aimed at the recognition task of remote sensing images (RSIs), this paper proposes to use a reactive-proactive ensemble defense framework to solve the security problem. In reactive defense, we fuse scoring functions of several classical detection algorithms with the hidden features and average output confidences from sub-models as a second fusion. In terms of proactive defense, we attempt two strategies, including enhancing the robustness of each sub-model and limiting the transferability among sub-models. In practical applications, the real-time RSIs are first input to the reactive defense part, which can detect and reject the adversarial RSIs. The accepted ones are then passed to robust recognition with a proactive defense. We conduct extensive experiments on three benchmark RSI datasets (i.e., UCM, AID, and FGSC-23). The experimental results show that the deep ensemble method of reactive and proactive defense performs very well in gradient-based attacks. The analysis of the applicable attack scenarios for each proactive ensemble defense is also helpful for this field. We also perform a case study with the whole framework in the black-box scenario, and the highest detection rate reaches 93.25%. Most of the adversarial RSIs can be rejected in advance or correctly recognized by the enhanced deep ensemble. This article is the first one to combine reactive and proactive defenses with a deep ensemble against adversarial attacks in the context of RSI recognition for DNN-based UAVs. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Classification II)

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24 pages, 43169 KiB

Open AccessArticle

Annual Field-Scale Maps of Tall and Short Crops at the Global Scale Using GEDI and Sentinel-2

by Stefania Di Tommaso, Sherrie Wang, Vivek Vajipey, Noel Gorelick, Rob Strey and David B. Lobell

Remote Sens. 2023, 15(17), 4123; https://doi.org/10.3390/rs15174123 - 22 Aug 2023

Cited by 3 | Viewed by 1826

Abstract

Crop type maps are critical for tracking agricultural land use and estimating crop production. Remote sensing has proven an efficient and reliable tool for creating these maps in regions with abundant ground labels for model training, yet these labels remain difficult to obtain for many regions and years. NASA’s Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR instrument, originally designed for forest monitoring, has shown promise for distinguishing tall and short crops. In the current study, we leverage GEDI to develop wall-to-wall maps of short vs. tall crops on a global scale at 10 m resolution for 2019–2021. Specifically, we show that (i) GEDI returns can reliably be classified into tall and short crops after removing shots with extreme view angles or topographic slope, (ii) the frequency of tall crops over time can be used to identify months when tall crops are at their peak height, and (iii) GEDI shots in these months can then be used to train random forest models that use Sentinel-2 time series to accurately predict short vs. tall crops. Independent reference data from around the world are then used to evaluate these GEDI-S2 maps. We find that GEDI-S2 performed nearly as well as models trained on thousands of local reference training points, with accuracies of at least 87% and often above 90% throughout the Americas, Europe, and East Asia. A systematic underestimation of tall crop area was observed in regions where crops frequently exhibit low biomass, namely Africa and South Asia, and further work is needed in these systems. Although the GEDI-S2 approach only differentiates tall from short crops, in many landscapes this distinction is sufficient to map individual crop types (e.g., maize vs. soy, sugarcane vs. rice). The combination of GEDI and Sentinel-2 thus presents a very promising path towards global crop mapping with minimal reliance on ground data. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Classification II)

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21 pages, 11398 KiB

Open AccessArticle

Spatial-Aware Transformer (SAT): Enhancing Global Modeling in Transformer Segmentation for Remote Sensing Images

by Duolin Wang, Yadang Chen, Bushra Naz, Le Sun and Baozhu Li

Remote Sens. 2023, 15(14), 3607; https://doi.org/10.3390/rs15143607 - 19 Jul 2023

Cited by 1 | Viewed by 1093

Abstract

In this research, we present the Spatial-Aware Transformer (SAT), an enhanced implementation of the Swin Transformer module, purposed to augment the global modeling capabilities of existing transformer segmentation mechanisms within remote sensing. The current landscape of transformer segmentation techniques is encumbered by an inability to effectively model global dependencies, a deficiency that is especially pronounced in the context of occluded objects. Our innovative solution embeds spatial information into the Swin Transformer block, facilitating the creation of pixel-level correlations, and thereby significantly elevating the feature representation potency for occluded subjects. We have incorporated a boundary-aware module into our decoder to mitigate the commonly encountered shortcoming of inaccurate boundary segmentation. This component serves as an innovative refinement instrument, fortifying the precision of boundary demarcation. After these strategic enhancements, the Spatial-Aware Transformer achieved state-of-the-art performance benchmarks on the Potsdam, Vaihingen, and Aerial datasets, demonstrating its superior capabilities in recognizing occluded objects and distinguishing unique features, even under challenging conditions. This investigation constitutes a significant advancement toward optimizing transformer segmentation algorithms in remote sensing, opening a wealth of opportunities for future research and development. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Classification II)

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17 pages, 20410 KiB

Open AccessArticle

DMAU-Net: An Attention-Based Multiscale Max-Pooling Dense Network for the Semantic Segmentation in VHR Remote-Sensing Images

by Yang Yang, Junwu Dong, Yanhui Wang, Bibo Yu and Zhigang Yang

Remote Sens. 2023, 15(5), 1328; https://doi.org/10.3390/rs15051328 - 27 Feb 2023

Cited by 3 | Viewed by 1502

Abstract

High-resolution remote-sensing images cover more feature information, including texture, structure, shape, and other geometric details, while the relationships among target features are more complex. These factors make it more complicated for classical convolutional neural networks to obtain ideal results when performing a feature classification on remote-sensing images. To address this issue, we proposed an attention-based multiscale max-pooling dense network (DMAU-Net), which is based on U-Net for ground object classification. The network is designed with an integrated max-pooling module that incorporates dense connections in the encoder part to enhance the quality of the feature map, and thus improve the feature-extraction capability of the network. Equally, in the decoding, we introduce the Efficient Channel Attention (ECA) module, which can strengthen the effective features and suppress the irrelevant information. To validate the ground object classification performance of the multi-pooling integration network proposed in this paper, we conducted experiments on the Vaihingen and Potsdam datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). We compared DMAU-Net with other mainstream semantic segmentation models. The experimental results show that the DMAU-Net proposed in this paper effectively improves the accuracy of the feature classification of high-resolution remote-sensing images. The feature boundaries obtained by DMAU-Net are clear and regionally complete, enhancing the ability to optimize the edges of features. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Classification II)

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20 pages, 2390 KiB

Open AccessArticle

SS-TMNet: Spatial–Spectral Transformer Network with Multi-Scale Convolution for Hyperspectral Image Classification

by Xiaohui Huang, Yunfei Zhou, Xiaofei Yang, Xianhong Zhu and Ke Wang

Remote Sens. 2023, 15(5), 1206; https://doi.org/10.3390/rs15051206 - 22 Feb 2023

Cited by 5 | Viewed by 1885

Abstract

Hyperspectral image (HSI) classification is a significant foundation for remote sensing image analysis, widely used in biology, aerospace, and other applications. Convolution neural networks (CNNs) and attention mechanisms have shown outstanding ability in HSI classification and have been widely studied in recent years. However, the existing CNN-based and attention mechanism-based methods cannot fully use spatial–spectral information, which is not conducive to further improving HSI classification accuracy. This paper proposes a new spatial–spectral Transformer network with multi-scale convolution (SS-TMNet), which can effectively extract local and global spatial–spectral information. SS-TMNet includes two key modules, i.e., multi-scale 3D convolution projection module (MSCP) and spatial–spectral attention module (SSAM). The MSCP uses multi-scale 3D convolutions with different depths to extract the fused spatial–spectral features. The spatial–spectral attention module includes three branches: height spatial attention, width spatial attention, and spectral attention, which can extract the fusion information of spatial and spectral features. The proposed SS-TMNet was tested on three widely used HSI datasets: Pavia University, IndianPines, and Houston2013. The experimental results show that the proposed SS-TMNet is superior to the existing methods. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Classification II)

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26 pages, 8198 KiB

Open AccessArticle

SAR Image Classification Using Gated Channel Attention Based Convolutional Neural Network

by Anjun Zhang, Lu Jia, Jun Wang and Chuanjian Wang

Remote Sens. 2023, 15(2), 362; https://doi.org/10.3390/rs15020362 - 06 Jan 2023

Cited by 1 | Viewed by 1286

Abstract

Algorithms combining CNN (Convolutional Neural Network) and super-pixel based smoothing have been proposed in recent years for Synthetic Aperture Radar (SAR) image classification. However, the smoothing may lead to the damage of details. To solve this problem the feature fusion strategy is utilized, and a novel adaptive fusion module named Gated Channel Attention (GCA) is designed in this paper. In this module, the relevance between channels is embedded into the conventional gated attention module to emphasize the variation in contribution on classification results between channels of feature-maps, which is not well considered by the conventional gated attention module. A GCA-CNN network is then constructed for SAR image classification. In this network, feature-maps corresponding to the original image and the smoothed image are extracted, respectively, by feature-extraction layers and adaptively fused. The fused features are used to obtain the results. Classification can be performed by the GCA-CNN in an end-to-end way. By the adaptive feature fusion in GCA-CNN, the smoothing of misclassification and the detail keeping can be realized at the same time. Experiments have been performed on one elaborately designed synthetic image and three real world SAR images. The superiority of the GCA-CNN is demonstrated by comparing with the conventional algorithms and the relative state-of-the-art algorithms. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Classification II)

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19 pages, 1337 KiB

Open AccessArticle

Tri-CNN: A Three Branch Model for Hyperspectral Image Classification

by Mohammed Q. Alkhatib, Mina Al-Saad, Nour Aburaed, Saeed Almansoori, Jaime Zabalza, Stephen Marshall and Hussain Al-Ahmad

Remote Sens. 2023, 15(2), 316; https://doi.org/10.3390/rs15020316 - 05 Jan 2023

Cited by 28 | Viewed by 4998

Abstract

Hyperspectral Image (HSI) classification methods that use Deep Learning (DL) have proven to be effective in recent years. In particular, Convolutional Neural Networks (CNNs) have demonstrated extremely powerful performance in such tasks. However, the lack of training samples is one of the main contributors to low classification performance. Traditional CNN-based techniques under-utilize the inter-band correlations of HSI because they primarily use 2D-CNNs for feature extraction. Contrariwise, 3D-CNNs extract both spectral and spatial information using the same operation. While this overcomes the limitation of 2D-CNNs, it may lead to insufficient extraction of features. In order to overcome this issue, we propose an HSI classification approach named Tri-CNN which is based on a multi-scale 3D-CNN and three-branch feature fusion. We first extract HSI features using 3D-CNN at various scales. The three different features are then flattened and concatenated. To obtain the classification results, the fused features then traverse a number of fully connected layers and eventually a softmax layer. Experimental results are conducted on three datasets, Pavia University (PU), Salinas scene (SA) and GulfPort (GP) datasets, respectively. Classification results indicate that our proposed methodology shows remarkable performance in terms of the Overall Accuracy (OA), Average Accuracy (AA), and Kappa metrics when compared against existing methods. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Classification II)

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25 pages, 63832 KiB

Open AccessArticle

XANet: An Efficient Remote Sensing Image Segmentation Model Using Element-Wise Attention Enhancement and Multi-Scale Attention Fusion

by Chenbin Liang, Baihua Xiao, Bo Cheng and Yunyun Dong

Remote Sens. 2023, 15(1), 236; https://doi.org/10.3390/rs15010236 - 31 Dec 2022

Cited by 2 | Viewed by 1776

Abstract

Massive and diverse remote sensing data provide opportunities for data-driven tasks in the real world, but also present challenges in terms of data processing and analysis, especially pixel-level image interpretation. However, the existing shallow-learning and deep-learning segmentation methods, bounded by their technical bottlenecks, cannot properly balance accuracy and efficiency, and are thus hardly scalable to the practice scenarios of remote sensing in a successful way. Instead of following the time-consuming deep stacks of local operations as most state-of-the-art segmentation networks, we propose a novel segmentation model with the encoder–decoder structure, dubbed XANet, which leverages the more computationally economical attention mechanism to boost performance. Two novel attention modules in XANet are proposed to strengthen the encoder and decoder, respectively, namely the Attention Recalibration Module (ARM) and Attention Fusion Module (AFM). Unlike current attention modules, which only focus on elevating the feature representation power, and regard the spatial and channel enhancement of a feature map as two independent steps, ARM gathers element-wise semantic descriptors coupling spatial and channel information to directly generate a 3D attention map for feature enhancement, and AFM innovatively utilizes the cross-attention mechanism for the sufficient spatial and channel fusion of multi-scale features. Extensive experiments were conducted on ISPRS and GID datasets to comprehensively analyze XANet and explore the effects of ARM and AFM. Furthermore, the results demonstrate that XANet surpasses other state-of-the-art segmentation methods in both model performance and efficiency, as ARM yields a superior improvement versus existing attention modules with a competitive computational overhead, and AFM achieves the complementary advantages of multi-level features under the sufficient consideration of efficiency. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Classification II)

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17 pages, 3044 KiB

Open AccessArticle

A Full Tensor Decomposition Network for Crop Classification with Polarization Extension

by Wei-Tao Zhang, Sheng-Di Zheng, Yi-Bang Li, Jiao Guo and Hui Wang

Remote Sens. 2023, 15(1), 56; https://doi.org/10.3390/rs15010056 - 22 Dec 2022

Cited by 2 | Viewed by 1325

Abstract

The multisource data fusion technique has been proven to perform better in crop classification. However, traditional fusion methods simply stack the original source data and their corresponding features, which can be only regarded as a superficial fusion method rather than deep fusion. This paper proposes a pixel-level fusion method for multispectral data and dual polarimetric synthetic aperture radar (PolSAR) data based on the polarization extension, which yields synthetic quad PolSAR data. Then we can generate high-dimensional features by means of various polarization decomposition schemes. High-dimensional features usually cause the curse of the dimensionality problem. To overcome this drawback in crop classification using the end-to-end network, we propose a simple network, namely the full tensor decomposition network (FTDN), where the feature extraction in the hidden layer is accomplished by tensor transformation. The number of parameters of the FTDN is considerably fewer than that of traditional neural networks. Moreover, the FTDN admits higher classification accuracy by making full use of structural information of PolSAR data. The experimental results demonstrate the effectiveness of the fusion method and the FTDN model. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Classification II)

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20 pages, 62620 KiB

Open AccessArticle

Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images

by Elif Sertel, Burak Ekim, Paria Ettehadi Osgouei and M. Erdem Kabadayi

Remote Sens. 2022, 14(18), 4558; https://doi.org/10.3390/rs14184558 - 12 Sep 2022

Cited by 23 | Viewed by 6879

Abstract

Deep learning-based segmentation of very high-resolution (VHR) satellite images is a significant task providing valuable information for various geospatial applications, specifically for land use/land cover (LULC) mapping. The segmentation task becomes more challenging with the increasing number and complexity of LULC classes. In this research, we generated a new benchmark dataset from VHR Worldview-3 images for twelve distinct LULC classes of two different geographical locations. We evaluated the performance of different segmentation architectures and encoders to find the best design to create highly accurate LULC maps. Our results showed that the DeepLabv3+ architecture with an ResNeXt50 encoder achieved the best performance for different metric values with an IoU of 89.46%, an F-1 score of 94.35%, a precision of 94.25%, and a recall of 94.49%. This design could be used by other researchers for LULC mapping of similar classes from different satellite images or for different geographical regions. Moreover, our benchmark dataset can be used as a reference for implementing new segmentation models via supervised, semi- or weakly-supervised deep learning models. In addition, our model results can be used for transfer learning and generalizability of different methodologies. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Classification II)

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24 pages, 7806 KiB

Open AccessArticle

MKANet: An Efficient Network with Sobel Boundary Loss for Land-Cover Classification of Satellite Remote Sensing Imagery

by Zhiqi Zhang, Wen Lu, Jinshan Cao and Guangqi Xie

Remote Sens. 2022, 14(18), 4514; https://doi.org/10.3390/rs14184514 - 09 Sep 2022

Cited by 17 | Viewed by 1878

Abstract

Land cover classification is a multiclass segmentation task to classify each pixel into a certain natural or human-made category of the earth’s surface, such as water, soil, natural vegetation, crops, and human infrastructure. Limited by hardware computational resources and memory capacity, most existing studies preprocessed original remote sensing images by downsampling or cropping them into small patches less than 512 × 512 pixels before sending them to a deep neural network. However, downsampling incurs a spatial detail loss, renders small segments hard to discriminate, and reverses the spatial resolution progress obtained by decades of efforts. Cropping images into small patches causes a loss of long-range context information, and restoring the predicted results to their original size brings extra latency. In response to the above weaknesses, we present an efficient lightweight semantic segmentation network termed MKANet. Aimed at the characteristics of top view high-resolution remote sensing imagery, MKANet utilizes sharing kernels to simultaneously and equally handle ground segments of inconsistent scales, and also employs a parallel and shallow architecture to boost inference speed and friendly support image patches more than 10× larger. To enhance boundary and small segment discrimination, we also propose a method that captures category impurity areas, exploits boundary information, and exerts an extra penalty on boundaries and small segment misjudgments. Both visual interpretations and quantitative metrics of extensive experiments demonstrate that MKANet obtains a state-of-the-art accuracy on two land-cover classification datasets and infers 2× faster than other competitive lightweight networks. All these merits highlight the potential of MKANet in practical applications. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Classification II)

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15 pages, 28915 KiB

Open AccessArticle

A Deep Learning Application to Map Weed Spatial Extent from Unmanned Aerial Vehicles Imagery

by Paolo Fraccaro, Junaid Butt, Blair Edwards, Robert P. Freckleton, Dylan Z. Childs, Katharina Reusch and David Comont

Remote Sens. 2022, 14(17), 4197; https://doi.org/10.3390/rs14174197 - 26 Aug 2022

Cited by 3 | Viewed by 1723

Abstract

Weed infestation is a global threat to agricultural productivity, leading to low yields and financial losses. Weed detection, based on applying machine learning to imagery collected by Unmanned Aerial Vehicles (UAV) has shown potential in the past; however, validation on large data-sets (e.g., across a wide number of different fields) remains lacking, with few solutions actually made operational. Here, we demonstrate the feasibility of automatically detecting weeds in winter wheat fields based on deep learning methods applied to UAV data at scale. Focusing on black-grass (the most pernicious weed across northwest Europe), we show high performance (i.e., accuracy above 0.9) and highly statistically significant correlation (i.e., ro > 0.75 and p < 0.00001) between imagery-derived local and global weed maps and out-of-bag field survey data, collected by experts over 31 fields (205 hectares) in the UK. We demonstrate how the developed deep learning model can be made available via an easy-to-use docker container, with results accessible through an interactive dashboard. Using this approach, clickable weed maps can be created and deployed rapidly, allowing the user to explore actual model predictions for each field. This shows the potential for this approach to be used operationally and influence agronomic decision-making in the real world. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Classification II)

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Review

Jump to: Research

31 pages, 5152 KiB

Open AccessReview

Transformers in Remote Sensing: A Survey

by Abdulaziz Amer Aleissaee, Amandeep Kumar, Rao Muhammad Anwer, Salman Khan, Hisham Cholakkal, Gui-Song Xia and Fahad Shahbaz Khan

Remote Sens. 2023, 15(7), 1860; https://doi.org/10.3390/rs15071860 - 30 Mar 2023

Cited by 44 | Viewed by 8723

Abstract

Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformer-based architectures, originally introduced in natural language processing, have pervaded computer vision field where the self-attention mechanism has been utilized as a replacement to the popular convolution operator for capturing long-range dependencies. Inspired by recent advances in computer vision, the remote sensing community has also witnessed an increased exploration of vision transformers for a diverse set of tasks. Although a number of surveys have focused on transformers in computer vision in general, to the best of our knowledge we are the first to present a systematic review of recent advances based on transformers in remote sensing. Our survey covers more than 60 recent transformer-based methods for different remote sensing problems in sub-areas of remote sensing: very high-resolution (VHR), hyperspectral (HSI) and synthetic aperture radar (SAR) imagery. We conclude the survey by discussing different challenges and open issues of transformers in remote sensing. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Classification II)

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