Remote Sensing

Research

19 pages, 2523 KiB

Open AccessArticle

Hyperspectral Image Denoising by Pixel-Wise Noise Modeling and TV-Oriented Deep Image Prior

by Lixuan Yi, Qian Zhao and Zongben Xu

Remote Sens. 2024, 16(15), 2694; https://doi.org/10.3390/rs16152694 - 23 Jul 2024

Cited by 1 | Viewed by 1178

Model-based hyperspectral image (HSI) denoising methods have attracted continuous attention in the past decades, due to their effectiveness and interpretability. In this work, we aim at advancing model-based HSI denoising, through sophisticated investigation for both the fidelity and regularization terms, or correspondingly noise [...] Read more.

Model-based hyperspectral image (HSI) denoising methods have attracted continuous attention in the past decades, due to their effectiveness and interpretability. In this work, we aim at advancing model-based HSI denoising, through sophisticated investigation for both the fidelity and regularization terms, or correspondingly noise and prior, by virtue of several recently developed techniques. Specifically, we formulate a novel unified probabilistic model for the HSI denoising task, within which the noise is assumed as pixel-wise non-independent and identically distributed (non-i.i.d) Gaussian predicted by a pre-trained neural network, and the prior for the HSI image is designed by incorporating the deep image prior (DIP) with total variation (TV) and spatio-spectral TV. To solve the resulted maximum a posteriori (MAP) estimation problem, we design a Monte Carlo Expectation–Maximization (MCEM) algorithm, in which the stochastic gradient Langevin dynamics (SGLD) method is used for computing the E-step, and the alternative direction method of multipliers (ADMM) is adopted for solving the optimization in the M-step. Experiments on both synthetic and real noisy HSI datasets have been conducted to verify the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Knowledge-Driven and/or Data-Driven Methods for Remote Sensing Image Processing)

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18 pages, 10310 KiB

Open AccessArticle

A New Sparse Collaborative Low-Rank Prior Knowledge Representation for Thick Cloud Removal in Remote Sensing Images

by Dong-Lin Sun, Teng-Yu Ji and Meng Ding

Remote Sens. 2024, 16(9), 1518; https://doi.org/10.3390/rs16091518 - 25 Apr 2024

Cited by 1 | Viewed by 856

Abstract

Efficiently removing clouds from remote sensing imagery presents a significant challenge, yet it is crucial for a variety of applications. This paper introduces a novel sparse function, named the tri-fiber-wise sparse function, meticulously engineered for the targeted tasks of cloud detection and removal. [...] Read more.

Efficiently removing clouds from remote sensing imagery presents a significant challenge, yet it is crucial for a variety of applications. This paper introduces a novel sparse function, named the tri-fiber-wise sparse function, meticulously engineered for the targeted tasks of cloud detection and removal. This function is adept at capturing cloud characteristics across three dimensions, leveraging the sparsity of mode-1, -2, and -3 fibers simultaneously to achieve precise cloud detection. By incorporating the concept of tensor multi-rank, which describes the global correlation, we have developed a tri-fiber-wise sparse-based model that excels in both detecting and eliminating clouds from images. Furthermore, to ensure that the cloud-free information accurately matches the corresponding areas in the observed data, we have enhanced our model with an extended box-constraint strategy. The experiments showcase the notable success of the proposed method in cloud removal. This highlights its potential and utility in enhancing the accuracy of remote sensing imagery. Full article

(This article belongs to the Special Issue Knowledge-Driven and/or Data-Driven Methods for Remote Sensing Image Processing)

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22 pages, 5150 KiB

Open AccessArticle

Convolutional Neural Network-Based Method for Agriculture Plot Segmentation in Remote Sensing Images

by Liang Qi, Danfeng Zuo, Yirong Wang, Ye Tao, Runkang Tang, Jiayu Shi, Jiajun Gong and Bangyu Li

Remote Sens. 2024, 16(2), 346; https://doi.org/10.3390/rs16020346 - 15 Jan 2024

Cited by 3 | Viewed by 1958

Abstract

Accurate delineation of individual agricultural plots, the foundational units for agriculture-based activities, is crucial for effective government oversight of agricultural productivity and land utilization. To improve the accuracy of plot segmentation in high-resolution remote sensing images, the paper collects GF-2 satellite remote sensing [...] Read more.

Accurate delineation of individual agricultural plots, the foundational units for agriculture-based activities, is crucial for effective government oversight of agricultural productivity and land utilization. To improve the accuracy of plot segmentation in high-resolution remote sensing images, the paper collects GF-2 satellite remote sensing images, uses ArcGIS10.3.1 software to establish datasets, and builds UNet, SegNet, DeeplabV3+, and TransUNet neural network frameworks, respectively, for experimental analysis. Then, the TransUNet network with the best segmentation effects is optimized in both the residual module and the skip connection to further improve its performance for plot segmentation in high-resolution remote sensing images. This article introduces Deformable ConvNets in the residual module to improve the original ResNet50 feature extraction network and combines the convolutional block attention module (CBAM) at the skip connection to calculate and improve the skip connection steps. Experimental results indicate that the optimized remote sensing plot segmentation algorithm based on the TransUNet network achieves an Accuracy of 86.02%, a Recall of 83.32%, an F1-score of 84.67%, and an Intersection over Union (IOU) of 86.90%. Compared to the original TransUNet network for remote sensing land parcel segmentation, whose F1-S is 81.94% and whose IoU is 69.41%, the optimized TransUNet network has significantly improved the performance of remote sensing land parcel segmentation, which verifies the effectiveness and reliability of the plot segmentation algorithm. Full article

(This article belongs to the Special Issue Knowledge-Driven and/or Data-Driven Methods for Remote Sensing Image Processing)

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22 pages, 6234 KiB

Open AccessArticle

Radiation-Variation Insensitive Coarse-to-Fine Image Registration for Infrared and Visible Remote Sensing Based on Zero-Shot Learning

by Jiaqi Li, Guoling Bi, Xiaozhen Wang, Ting Nie and Liang Huang

Remote Sens. 2024, 16(2), 214; https://doi.org/10.3390/rs16020214 - 5 Jan 2024

Cited by 3 | Viewed by 1563

Abstract

Infrared and visible remote sensing image registration is significant for utilizing remote sensing images to obtain scene information. However, it is difficult to establish a large number of correct matches due to the difficulty in obtaining similarity metrics due to the presence of [...] Read more.

Infrared and visible remote sensing image registration is significant for utilizing remote sensing images to obtain scene information. However, it is difficult to establish a large number of correct matches due to the difficulty in obtaining similarity metrics due to the presence of radiation variation between heterogeneous sensors, which is caused by different imaging principles. In addition, the existence of sparse textures in infrared images as well as in some scenes and the small number of relevant trainable datasets also hinder the development of this field. Therefore, we combined data-driven and knowledge-driven methods to propose a Radiation-variation Insensitive, Zero-shot learning-based Registration (RIZER). First, RIZER, as a whole, adopts a detector-free coarse-to-fine registration framework, and the data-driven methods use a Transformer based on zero-shot learning. Next, the knowledge-driven methods are embodied in the coarse-level matches, where we adopt the strategy of seeking reliability by introducing the HNSW algorithm and employing a priori knowledge of local geometric soft constraints. Then, we simulate the matching strategy of the human eye to transform the matching problem into a model-fitting problem and employ a multi-constrained incremental matching approach. Finally, after fine-level coordinate fine tuning, we propose an outlier culling algorithm that only requires very few iterations. Meanwhile, we propose a multi-scene infrared and visible remote sensing image registration dataset. After testing, RIZER achieved a correct matching rate of 99.55% with an RMSE of 1.36 and had an advantage in the number of correct matches, as well as a good generalization ability for other multimodal images, achieving the best results when compared to some traditional and state-of-the-art multimodal registration algorithms. Full article

(This article belongs to the Special Issue Knowledge-Driven and/or Data-Driven Methods for Remote Sensing Image Processing)

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18 pages, 4159 KiB

Open AccessArticle

Fast Thick Cloud Removal for Multi-Temporal Remote Sensing Imagery via Representation Coefficient Total Variation

by Shuang Xu, Jilong Wang and Jialin Wang

Remote Sens. 2024, 16(1), 152; https://doi.org/10.3390/rs16010152 - 29 Dec 2023

Cited by 2 | Viewed by 1475

Abstract

Although thick cloud removal is a complex task, the past decades have witnessed the remarkable development of tensor-completion-based techniques. Nonetheless, they require substantial computational resources and may suffer from checkboard artifacts. This study presents a novel technique to address this challenging task using [...] Read more.

Although thick cloud removal is a complex task, the past decades have witnessed the remarkable development of tensor-completion-based techniques. Nonetheless, they require substantial computational resources and may suffer from checkboard artifacts. This study presents a novel technique to address this challenging task using representation coefficient total variation (RCTV), which imposes a total variation regularizer on decomposed data. The proposed approach enhances cloud removal performance while effectively preserving the textures with high speed. The experimental results confirm the efficiency of our method in restoring image textures, demonstrating its superior performance compared to state-of-the-art techniques. Full article

(This article belongs to the Special Issue Knowledge-Driven and/or Data-Driven Methods for Remote Sensing Image Processing)

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31 pages, 19832 KiB

Open AccessArticle

Classification of Urban Surface Elements by Combining Multisource Data and Ontology

by Ling Zhu, Yuzhen Lu and Yewen Fan

Remote Sens. 2024, 16(1), 4; https://doi.org/10.3390/rs16010004 - 19 Dec 2023

Viewed by 1214

Abstract

The rapid pace of urbanization and increasing demands for urban functionalities have led to diversification and complexity in the types of urban surface elements. The conventional approach of relying solely on remote sensing imagery for urban surface element extraction faces emerging challenges. Data-driven [...] Read more.

The rapid pace of urbanization and increasing demands for urban functionalities have led to diversification and complexity in the types of urban surface elements. The conventional approach of relying solely on remote sensing imagery for urban surface element extraction faces emerging challenges. Data-driven techniques, including deep learning and machine learning, necessitate a substantial number of annotated samples as prerequisites. In response, our study proposes a knowledge-driven approach that integrates multisource data with ontology to achieve precise urban surface element extraction. Within this framework, components from the EIONET Action Group on Land Monitoring in Europe matrix serve as ontology primitives, forming a shared vocabulary. The semantics of surface elements are deconstructed using these primitives, enabling the creation of specific descriptions for various types of urban surface elements by combining these primitives. Our approach integrates multitemporal high-resolution remote sensing data, network big data, and other heterogeneous data sources. It segments high-resolution images into individual patches, and for each unit, urban surface element classification is accomplished through semantic rule-based inference. We conducted experiments in two regions with varying levels of urban scene complexity, achieving overall accuracies of 93.03% and 97.35%, respectively. Through this knowledge-driven approach, our proposed method significantly enhances the classification performance of urban surface elements in complex scenes, even in the absence of sample data, thereby presenting a novel approach to urban surface element extraction. Full article

(This article belongs to the Special Issue Knowledge-Driven and/or Data-Driven Methods for Remote Sensing Image Processing)

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25 pages, 5631 KiB

Open AccessArticle

Learn by Yourself: A Feature-Augmented Self-Distillation Convolutional Neural Network for Remote Sensing Scene Image Classification

by Cuiping Shi, Mengxiang Ding, Liguo Wang and Haizhu Pan

Remote Sens. 2023, 15(23), 5620; https://doi.org/10.3390/rs15235620 - 4 Dec 2023

Cited by 3 | Viewed by 1553

Abstract

In recent years, with the rapid development of deep learning technology, great progress has been made in remote sensing scene image classification. Compared with natural images, remote sensing scene images are usually more complex, with high inter-class similarity and large intra-class differences, which [...] Read more.

In recent years, with the rapid development of deep learning technology, great progress has been made in remote sensing scene image classification. Compared with natural images, remote sensing scene images are usually more complex, with high inter-class similarity and large intra-class differences, which makes it difficult for commonly used networks to effectively learn the features of remote sensing scene images. In addition, most existing methods adopt hard labels to supervise the network model, which makes the model prone to losing fine-grained information of ground objects. In order to solve these problems, a feature-augmented self-distilled convolutional neural network (FASDNet) is proposed. First, ResNet34 is adopted as the backbone network to extract multi-level features of images. Next, a feature augmentation pyramid module (FAPM) is designed to extract and fuse multi-level feature information. Then, auxiliary branches are constructed to provide additional supervision information. The self-distillation method is utilized between the feature augmentation pyramid module and the backbone network, as well as between the backbone network and auxiliary branches. Finally, the proposed model is jointly supervised using feature distillation loss, logits distillation loss, and cross-entropy loss. A lot of experiments are conducted on four widely used remote sensing scene image datasets, and the experimental results show that the proposed method is superior to some state-ot-the-art classification methods. Full article

(This article belongs to the Special Issue Knowledge-Driven and/or Data-Driven Methods for Remote Sensing Image Processing)

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17 pages, 65010 KiB

Open AccessArticle

Remote Sensing Image Super-Resolution via Multi-Scale Texture Transfer Network

by Yu Wang, Zhenfeng Shao, Tao Lu, Xiao Huang, Jiaming Wang, Xitong Chen, Haiyan Huang and Xiaolong Zuo

Remote Sens. 2023, 15(23), 5503; https://doi.org/10.3390/rs15235503 - 25 Nov 2023

Cited by 3 | Viewed by 1968

Abstract

As the degradation factors of remote sensing images become increasingly complex, it becomes challenging to infer the high-frequency details of remote sensing images compared to ordinary digital photographs. For super-resolution (SR) tasks, existing deep learning-based single remote sensing image SR methods tend to [...] Read more.

As the degradation factors of remote sensing images become increasingly complex, it becomes challenging to infer the high-frequency details of remote sensing images compared to ordinary digital photographs. For super-resolution (SR) tasks, existing deep learning-based single remote sensing image SR methods tend to rely on texture information, leading to various limitations. To fill this gap, we propose a remote sensing image SR algorithm based on a multi-scale texture transfer network (MTTN). The proposed MTTN enhances the texture feature information of reconstructed images by adaptively transferring texture information according to the texture similarity of the reference image. The proposed method adopts a multi-scale texture-matching strategy, which promotes the transmission of multi-scale texture information of remote sensing images and obtains finer-texture information from more relevant semantic modules. Experimental results show that the proposed method outperforms state-of-the-art SR techniques on the Kaggle open-source remote sensing dataset from both quantitative and qualitative perspectives. Full article

(This article belongs to the Special Issue Knowledge-Driven and/or Data-Driven Methods for Remote Sensing Image Processing)

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