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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 (31 March 2024) | Viewed by 13825

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Special Issue Editors

Dr. Sultan Daud Khan

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Guest Editor

Department of Computer Science, National University of Technology, Islamabad, Pakistan
Interests: computer vision; remote sensing; deep learning; embedded system design

Dr. Habib Ullah

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Guest Editor

Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
Interests: computer vision; remote sensing; signal processing

Dr. Mohib Ullah

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Guest Editor

Department of Computer Science, Faculty of Information Technology and Electrical Engineering, Norges Teknisk-Naturvitenskapelige Universitet, Trondheim, Norway
Interests: image and video analysis; remote sensing; deep learning; pattern recognition; medical imaging
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Special Issue Information

Dear Colleagues,

Remote sensing technologies enabled researchers to understand, analyze and monitor different activities on Earth from a far distance. With the current advances in technologies, such as satellites, drones, etc., a significant amount of data (in the form of high-resolution images) can be easily acquired. This opens new paradigms and research directions for the remote sensing community and offers different applications in diverse fields, such as smart agriculture, traffic monitoring, disaster management, and urban planning. For monitoring Earth, visual pattern recognition is a pre-processing step. The automated recognition of different patterns by employing computer vision and deep learning techniques will provide crucial information for monitoring changes across the Earth’s surface. Although deep learning techniques have achieved tremendous success in object classification, detection, and segmentation tasks in natural images, however, these models face challenges in identifying patterns in remote sensing images due to complex background, arbitrary views, and large variations in objects sizes

This Special Issue invites authors to submit their original articles regarding the design and development of novel deep learning models to identify different visual patterns to support Earth monitoring. In addition, we would like to invite the submission of research relating to remote-sensing-based disaster assessment and management support systems. Lastly, we also welcome comprehensive review articles that focus on analyzing the performance of state-of-the-art deep learning models in remote sensing imagery.

Submissions may cover a wide range of topics, including the following:

Deep learning models for monitoring Earth;
Deep learning models for monitoring corps using remote sensing data;
Flood segmentation and natural hazards prediction;
Roads and building footprints extraction for urban growth and planning;
Remote sensing for smart farming for sustainable agriculture.

Dr. Sultan Daud Khan
Dr. Habib Ullah
Dr. Mohib Ullah
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

satellite image processing
multi-scale feature extraction
deep Learning
context understanding
data fusion

Published Papers (7 papers)

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Research

28 pages, 6569 KiB

Open AccessArticle

A Novel Building Extraction Network via Multi-Scale Foreground Modeling and Gated Boundary Refinement

by Junlin Liu, Ying Xia, Jiangfan Feng and Peng Bai

Remote Sens. 2023, 15(24), 5638; https://doi.org/10.3390/rs15245638 - 05 Dec 2023

Viewed by 925

Abstract

Deep learning-based methods for building extraction from remote sensing images have been widely applied in fields such as land management and urban planning. However, extracting buildings from remote sensing images commonly faces challenges due to specific shooting angles. First, there exists a foreground–background imbalance issue, and the model excessively learns features unrelated to buildings, resulting in performance degradation and propagative interference. Second, buildings have complex boundary information, while conventional network architectures fail to capture fine boundaries. In this paper, we designed a multi-task U-shaped network (BFL-Net) to solve these problems. This network enhances the expression of the foreground and boundary features in the prediction results through foreground learning and boundary refinement, respectively. Specifically, the Foreground Mining Module (FMM) utilizes the relationship between buildings and multi-scale scene spaces to explicitly model, extract, and learn foreground features, which can enhance foreground and related contextual features. The Dense Dilated Convolutional Residual Block (DDCResBlock) and the Dual Gate Boundary Refinement Module (DGBRM) individually process the diverted regular stream and boundary stream. The former can effectively expand the receptive field, and the latter utilizes spatial and channel gates to activate boundary features in low-level feature maps, helping the network refine boundaries. The predictions of the network for the building, foreground, and boundary are respectively supervised by ground truth. The experimental results on the WHU Building Aerial Imagery and Massachusetts Buildings Datasets show that the IoU scores of BFL-Net are 91.37% and 74.50%, respectively, surpassing state-of-the-art models. Full article

(This article belongs to the Special Issue Deep Learning Meets Remote Sensing for Earth Observation and Monitoring)

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18 pages, 5922 KiB

Open AccessArticle

Weed–Crop Segmentation in Drone Images with a Novel Encoder–Decoder Framework Enhanced via Attention Modules

by Sultan Daud Khan, Saleh Basalamah and Ahmed Lbath

Remote Sens. 2023, 15(23), 5615; https://doi.org/10.3390/rs15235615 - 03 Dec 2023

Cited by 1 | Viewed by 1037

Abstract

The rapid expansion of the world’s population has resulted in an increased demand for agricultural products which necessitates the need to improve crop yields. To enhance crop yields, it is imperative to control weeds. Traditionally, weed control predominantly relied on the use of herbicides; however, the indiscriminate application of herbicides presents potential hazards to both crop health and productivity. Fortunately, the advent of cutting-edge technologies such as unmanned vehicle technology (UAVs) and computer vision has provided automated and efficient solutions for weed control. These approaches leverage drone images to detect and identify weeds with a certain level of accuracy. Nevertheless, the identification of weeds in drone images poses significant challenges attributed to factors like occlusion, variations in color and texture, and disparities in scale. The utilization of traditional image processing techniques and deep learning approaches, which are commonly employed in existing methods, presents difficulties in extracting features and addressing scale variations. In order to address these challenges, an innovative deep learning framework is introduced which is designed to classify every pixel in a drone image into categories such as weed, crop, and others. In general, our proposed network adopts an encoder–decoder structure. The encoder component of the network effectively combines the Dense-inception network with the Atrous spatial pyramid pooling module, enabling the extraction of multi-scale features and capturing local and global contextual information seamlessly. The decoder component of the network incorporates deconvolution layers and attention units, namely, channel and spatial attention units (CnSAUs), which contribute to the restoration of spatial information and enhance the precise localization of weeds and crops in the images. The performance of the proposed framework is assessed using a publicly available benchmark dataset known for its complexity. The effectiveness of the proposed framework is demonstrated via comprehensive experiments, showcasing its superiority by achieving a 0.81 mean Intersection over Union (mIoU) on the challenging dataset. Full article

(This article belongs to the Special Issue Deep Learning Meets Remote Sensing for Earth Observation and Monitoring)

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25 pages, 13476 KiB

Open AccessArticle

Multi-Branch Deep Learning Framework for Land Scene Classification in Satellite Imagery

by Sultan Daud Khan and Saleh Basalamah

Remote Sens. 2023, 15(13), 3408; https://doi.org/10.3390/rs15133408 - 05 Jul 2023

Cited by 9 | Viewed by 1771

Abstract

Land scene classification in satellite imagery has a wide range of applications in remote surveillance, environment monitoring, remote scene analysis, Earth observations and urban planning. Due to immense advantages of the land scene classification task, several methods have been proposed during recent years to automatically classify land scenes in remote sensing images. Most of the work focuses on designing and developing deep networks to identify land scenes from high-resolution satellite images. However, these methods face challenges in identifying different land scenes. Complex texture, cluttered background, extremely small size of objects and large variations in object scale are the common challenges that restrict the models to achieve high performance. To tackle these challenges, we propose a multi-branch deep learning framework that efficiently combines global contextual features with multi-scale features to identify complex land scenes. Generally, the framework consists of two branches. The first branch extracts global contextual information from different regions of the input image, and the second branch exploits a fully convolutional network (FCN) to extract multi-scale local features. The performance of the proposed framework is evaluated on three benchmark datasets, UC-Merced, SIRI-WHU, and EuroSAT. From the experiments, we demonstrate that the framework achieves superior performance compared to other similar models. Full article

(This article belongs to the Special Issue Deep Learning Meets Remote Sensing for Earth Observation and Monitoring)

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24 pages, 61957 KiB

Open AccessArticle

Benchmark for Building Segmentation on Up-Scaled Sentinel-2 Imagery

by Svetlana Illarionova, Dmitrii Shadrin, Islomjon Shukhratov, Ksenia Evteeva, Georgii Popandopulo, Nazar Sotiriadi, Ivan Oseledets and Evgeny Burnaev

Remote Sens. 2023, 15(9), 2347; https://doi.org/10.3390/rs15092347 - 29 Apr 2023

Cited by 7 | Viewed by 2749

Abstract

Currently, we can solve a wide range of tasks using computer vision algorithms, which reduce manual labor and enable rapid analysis of the environment. The remote sensing domain provides vast amounts of satellite data, but it also poses challenges associated with processing this data. Baseline solutions with intermediate results are available for various tasks, such as forest species classification, infrastructure recognition, and emergency situation analysis using satellite data. Despite these advances, two major issues with high-performing artificial intelligence algorithms remain in the current decade. The first issue relates to the availability of data. To train a robust algorithm, a reasonable amount of well-annotated training data is required. The second issue is the availability of satellite data, which is another concern. Even though there are a number of data providers, high-resolution and up-to-date imagery is extremely expensive. This paper aims to address these challenges by proposing an effective pipeline for building segmentation that utilizes freely available Sentinel-2 data with 10 m spatial resolution. The approach we use combines a super-resolution (SR) component with a semantic segmentation component. As a result, we simultaneously consider and analyze SR and building segmentation tasks to improve the quality of the infrastructure analysis through medium-resolution satellite data. Additionally, we collected and made available a unique dataset for the Russian Federation covering area of 1091.2 square kilometers. The dataset provides Sentinel-2 imagery adjusted to the spatial resolution of 2.5 m and is accompanied by semantic segmentation masks. The building footprints were created using OpenStreetMap data that was manually checked and verified. Several experiments were conducted for the SR task, using advanced image SR methods such as the diffusion-based SR3 model, RCAN, SRGAN, and MCGR. The MCGR network produced the best result, with a PSNR of 27.54 and SSIM of 0.79. The obtained SR images were then used to tackle the building segmentation task with different neural network models, including DeepLabV3 with different encoders, SWIN, and Twins transformers. The SWIN transformer achieved the best results, with an F1-score of 79.60. Full article

(This article belongs to the Special Issue Deep Learning Meets Remote Sensing for Earth Observation and Monitoring)

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19 pages, 9341 KiB

Open AccessArticle

TESR: Two-Stage Approach for Enhancement and Super-Resolution of Remote Sensing Images

by Anas M. Ali, Bilel Benjdira, Anis Koubaa, Wadii Boulila and Walid El-Shafai

Remote Sens. 2023, 15(9), 2346; https://doi.org/10.3390/rs15092346 - 29 Apr 2023

Cited by 8 | Viewed by 2495

Abstract

Remote Sensing (RS) images are usually captured at resolutions lower than those required. Deep Learning (DL)-based super-resolution (SR) architectures are typically used to increase the resolution artificially. In this study, we designed a new architecture called TESR (Two-stage approach for Enhancement and super-resolution), leveraging the power of Vision Transformers (ViT) and the Diffusion Model (DM) to increase the resolution of RS images artificially. The first stage is the ViT-based model, which serves to increase resolution. The second stage is an iterative DM pre-trained on a larger dataset, which serves to increase image quality. Every stage is trained separately on the given task using a separate dataset. The self-attention mechanism of the ViT helps the first stage generate global and contextual details. The iterative Diffusion Model helps the second stage enhance the image’s quality and generate consistent and harmonic fine details. We found that TESR outperforms state-of-the-art architectures on super-resolution of remote sensing images on the UCMerced benchmark dataset. Considering the PSNR/SSIM metrics, TESR improves SR image quality as compared to state-of-the-art techniques from 34.03/0.9301 to 35.367/0.9449 in the scale ×2. On a scale of ×3, it improves from 29.92/0.8408 to 32.311/0.91143. On a scale of ×4, it improves from 27.77/0.7630 to 31.951/0.90456. We also found that the Charbonnier loss outperformed other loss functions in the training of both stages of TESR. The improvement was by a margin of 21.5%/14.3%, in the PSNR/SSIM, respectively. The source code of TESR is open to the community. Full article

(This article belongs to the Special Issue Deep Learning Meets Remote Sensing for Earth Observation and Monitoring)

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16 pages, 20205 KiB

Open AccessArticle

Multi-Scale and Context-Aware Framework for Flood Segmentation in Post-Disaster High Resolution Aerial Images

by Sultan Daud Khan and Saleh Basalamah

Remote Sens. 2023, 15(8), 2208; https://doi.org/10.3390/rs15082208 - 21 Apr 2023

Cited by 2 | Viewed by 1675

Abstract

Floods are the most frequent natural disasters, occurring almost every year around the globe. To mitigate the damage caused by a flood, it is important to timely assess the magnitude of the damage and efficiently conduct rescue operations, deploy security personnel and allocate resources to the affected areas. To efficiently respond to the natural disaster, it is very crucial to swiftly obtain accurate information, which is hard to obtain during a post-flood crisis. Generally, high resolution satellite images are predominantly used to obtain post-disaster information. Recently, deep learning models have achieved superior performance in extracting high-level semantic information from satellite images. However, due to the loss of multi-scale and global contextual features, existing deep learning models still face challenges in extracting complete and uninterrupted results. In this work, we proposed a novel deep learning semantic segmentation model that reduces the loss of multi-scale features and enhances global context awareness. Generally, the proposed framework consists of three modules, encoder, decoder and bridge, combined in a popular U-shaped scheme. The encoder and decoder modules of the framework introduce Res-inception units to obtain reliable multi-scale features and employ a bridge module (between the encoder and decoder) to capture global context. To demonstrate the effectiveness of the proposed framework, we perform an evaluation using a publicly available challenging dataset, FloodNet. Furthermore, we compare the performance of the proposed framework with other reference methods. We compare the proposed framework with recent reference models. Quantitative and qualitative results show that the proposed framework outperforms other reference models by an obvious margin. Full article

(This article belongs to the Special Issue Deep Learning Meets Remote Sensing for Earth Observation and Monitoring)

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15 pages, 3371 KiB

Open AccessArticle

Few-Shot Remote Sensing Image Scene Classification Based on Metric Learning and Local Descriptors

by Zhengwu Yuan, Chan Tang, Aixia Yang, Wendong Huang and Wang Chen

Remote Sens. 2023, 15(3), 831; https://doi.org/10.3390/rs15030831 - 01 Feb 2023

Cited by 5 | Viewed by 1929

Abstract

Scene classification is a critical technology to solve the challenges of image search and image recognition. It has become an indispensable and challenging research topic in the field of remote sensing. At present, most scene classifications are solved by deep neural networks. However, existing methods require large-scale training samples and are not suitable for actual scenarios with only a few samples. For this reason, a framework based on metric learning and local descriptors (MLLD) is proposed to enhance the classification effect of remote sensing scenes on the basis of few-shot. Specifically, MLLD adopts task-level training that is carried out through meta-learning, and meta-knowledge is learned to improve the model’s ability to recognize different categories. Moreover, Manifold Mixup is introduced by MLLD as a feature processor for the hidden layer of deep neural networks to increase the low confidence space for smoother decision boundaries and simpler hidden layer representations. In the end, a learnable metric is introduced; the nearest category of the image is matched by measuring the similarity of local descriptors. Experiments are conducted on three public datasets: UC Merced, WHU-RS19, and NWPU-RESISC45. Experimental results show that the proposed scene classification method can achieve the most advanced results on limited datasets. Full article

(This article belongs to the Special Issue Deep Learning Meets Remote Sensing for Earth Observation and Monitoring)

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