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Pattern Recognition in Remote Sensing

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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 (15 April 2023) | Viewed by 33882

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Prof. Dr. Chunlei Huo

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National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Interests: pattern recognition; deep learning; remote sensing; image processing
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Dr. Yi Liu

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Group of Research & Development, Det Norske Veritas (DNV)，Norway
Interests: Image processing and analysis; AI/Data-driven R&D

Dr. Lurui Xia

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School of Space Information, Space Engineering University, Beijing 101416, China
Interests: remote sensing image processing
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Dr. Zhiqiang Zhou

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School of Automation, Beijing Institute of Technology, Beijing 100081, China
Interests: pattern recognition; image processing; multi-modal information fusion
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Special Issue Information

Dear Colleagues,

Pattern recognition is a powerful tool for remote sensing image analysis. With the development of deep learning, cutting-edge performance has been witnessed in several remote sensing applications in the last decade. However, it is evident that remote sensing has been lagging behind other domains. In this context, this Special Issue encourages the submission of papers that offer recent advances and innovative solutions in the wide topic of remote sensing image analysis. In particular, topics that fall within topics including but not limited to the following are welcome:

New pattern recognition principles and their potential in remote sensing image analysis;
Low-level image processing techniques (e.g., denoising, enhancing, deblurring, rectification);
Mid-level image processing techniques (e.g., feature extraction, feature matching, image mosaic, image fusion, super-resolution, salience detection, change detection);
High-level image processing techniques (e.g., object recognition, semantic segmentation, image classification, image captioning, image understanding);
Parallel computing and cloud computing techniques;
Light-weight network and embedding design for remote sensing processing;
Applications to resource management, disaster monitoring, intelligent agriculture, smart city.

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 at any point from now until the deadline. All papers will be 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 the 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.

Prof. Dr. Chunlei Huo
Dr. Yi Liu
Dr. Lurui Xia
Dr. Zhiqiang Zhou
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

pattern recognition
deep learning
remote sensing
image processing
artificial intelligence

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Related Special Issue

Pattern Recognition in Remote Sensing II in Remote Sensing (5 articles)

Published Papers (13 papers)

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Research

30 pages, 47919 KiB

Open AccessArticle

DCAT: Dual Cross-Attention-Based Transformer for Change Detection

by Yuan Zhou, Chunlei Huo, Jiahang Zhu, Leigang Huo and Chunhong Pan

Remote Sens. 2023, 15(9), 2395; https://doi.org/10.3390/rs15092395 - 3 May 2023

Cited by 10 | Viewed by 4343

Abstract

Several transformer-based methods for change detection (CD) in remote sensing images have been proposed, with Siamese-based methods showing promising results due to their two-stream feature extraction structure. However, these methods ignore the potential of the cross-attention mechanism to improve change feature discrimination and thus, may limit the final performance. Additionally, using either high-frequency-like fast change or low-frequency-like slow change alone may not effectively represent complex bi-temporal features. Given these limitations, we have developed a new approach that utilizes the dual cross-attention-transformer (DCAT) method. This method mimics the visual change observation procedure of human beings and interacts with and merges bi-temporal features. Unlike traditional Siamese-based CD frameworks, the proposed method extracts multi-scale features and models patch-wise change relationships by connecting a series of hierarchically structured dual cross-attention blocks (DCAB). DCAB is based on a hybrid dual branch mixer that combines convolution and transformer to extract and fuse local and global features. It calculates two types of cross-attention features to effectively learn comprehensive cues with both low- and high-frequency information input from paired CD images. This helps enhance discrimination between the changed and unchanged regions during feature extraction. The feature pyramid fusion network is more lightweight than the encoder and produces powerful multi-scale change representations by aggregating features from different layers. Experiments on four CD datasets demonstrate the advantages of DCAT architecture over other state-of-the-art methods. Full article

(This article belongs to the Special Issue Pattern Recognition in Remote Sensing)

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28 pages, 22162 KiB

Open AccessArticle

MF-DCMANet: A Multi-Feature Dual-Stage Cross Manifold Attention Network for PolSAR Target Recognition

by Feng Li, Chaoqi Zhang, Xin Zhang and Yang Li

Remote Sens. 2023, 15(9), 2292; https://doi.org/10.3390/rs15092292 - 26 Apr 2023

Cited by 4 | Viewed by 2129

Abstract

The distinctive polarization information of polarimetric SAR (PolSAR) has been widely applied to terrain classification but is rarely used for PolSAR target recognition. The target recognition strategies built upon multi-feature have gained favor among researchers due to their ability to provide diverse classification information. The paper introduces a robust multi-feature cross-fusion approach, i.e., a multi-feature dual-stage cross manifold attention network, namely, MF-DCMANet, which essentially relies on the complementary information between different features to enhance the representation ability of targets. In the first-stage process, a Cross-Feature-Network (CFN) module is proposed to mine the middle-level semantic information of monogenic features and polarization features extracted from the PolSAR target. In the second-stage process, a Cross-Manifold-Attention (CMA) transformer is proposed, which takes the input features represented on the Grassmann manifold to mine the nonlinear relationship between features so that rich and fine-grained features can be captured to compute attention weight. Furthermore, a local window is used instead of the global window in the attention mechanism to improve the local feature representation capabilities and reduce the computation. The proposed MF-DCMANet achieves competitive performance on the GOTCHA dataset, with a recognition accuracy of 99.75%. Furthermore, it maintains a high accuracy rate in the few-shot recognition and open-set recognition scenarios, outperforming the current state-of-the-art method by about 2%. Full article

(This article belongs to the Special Issue Pattern Recognition in Remote Sensing)

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19 pages, 14936 KiB

Open AccessArticle

Combining Discrete and Continuous Representation: Scale-Arbitrary Super-Resolution for Satellite Images

by Tai An, Chunlei Huo, Shiming Xiang and Chunhong Pan

Remote Sens. 2023, 15(7), 1827; https://doi.org/10.3390/rs15071827 - 29 Mar 2023

Cited by 1 | Viewed by 2565

Abstract

The advancements in image super-resolution technology have led to its widespread use in remote sensing applications. However, there is currently a lack of a general solution for the reconstruction of satellite images at arbitrary resolutions. The existing scale-arbitrary super-resolution methods are primarily predicated on learning either a discrete representation (DR) or a continuous representation (CR) of the image, with DR retaining the sensitivity to resolution and CR guaranteeing the generalization of the model. In this paper, we propose a novel image representation that combines the discrete and continuous representation, known as CDCR, which enables the extension of images to any desired resolution in a plug-and-play manner. CDCR consists of two components: a CR-based dense prediction that gathers more available information and a DR-based resolution-specific refinement that adjusts the predicted values of local pixels. Furthermore, we introduce a scale cumulative ascent (SCA) method, which enhances the performance of the dense prediction and improves the accuracy of the generated images at ultra-high magnifications. The efficacy and dependability of CDCR are substantiated by extensive experiments conducted on multiple remote sensing datasets, providing strong support for scenarios that require accurate images. Full article

(This article belongs to the Special Issue Pattern Recognition in Remote Sensing)

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19 pages, 3314 KiB

Open AccessArticle

Multi-Modal Multi-Stage Underwater Side-Scan Sonar Target Recognition Based on Synthetic Images

by Jian Wang, Haisen Li, Guanying Huo, Chao Li and Yuhang Wei

Remote Sens. 2023, 15(5), 1303; https://doi.org/10.3390/rs15051303 - 26 Feb 2023

Cited by 8 | Viewed by 2561

Abstract

Due to the small sample size of underwater acoustic data and the strong noise interference caused by seabed reverberation, recognizing underwater targets in Side-Scan Sonar (SSS) images is challenging. Using a transfer-learning-based recognition method to train the backbone network on a large optical dataset (ImageNet) and fine-tuning the head network with a small SSS image dataset can improve the classification of sonar images. However, optical and sonar images have different statistical characteristics, directly affecting transfer-learning-based target recognition. In order to improve the accuracy of underwater sonar image classification, a style transformation method between optical and SSS images is proposed in this study. In the proposed method, objects with the SSS style were synthesized through content image feature extraction and image style transfer to reduce the variability of different data sources. A staged optimization strategy using multi-modal data effectively captures the anti-noise features of sonar images, providing a new learning method for transfer learning. The results of the classification experiment showed that the approach is more stable when using synthetic data and other multi-modal datasets, with an overall accuracy of 100%. Full article

(This article belongs to the Special Issue Pattern Recognition in Remote Sensing)

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25 pages, 11338 KiB

Open AccessArticle

Automatic Calibration between Multi-Lines LiDAR and Visible Light Camera Based on Edge Refinement and Virtual Mask Matching

by Chengkai Chen, Jinhui Lan, Haoting Liu, Shuai Chen and Xiaohan Wang

Remote Sens. 2022, 14(24), 6385; https://doi.org/10.3390/rs14246385 - 17 Dec 2022

Cited by 2 | Viewed by 2286

Abstract

To assist in the implementation of a fine 3D terrain reconstruction of the scene in remote sensing applications, an automatic joint calibration method between light detection and ranging (LiDAR) and visible light camera based on edge points refinement and virtual mask matching is proposed in this paper. The proposed method is used to solve the problem of inaccurate edge estimation of LiDAR with different horizontal angle resolutions and low calibration efficiency. First, we design a novel calibration target, adding four hollow rectangles for fully automatic locating of the calibration target and increasing the number of corner points. Second, an edge refinement strategy based on background point clouds is proposed to estimate the target edge more accurately. Third, a two-step method of automatically matching between the calibration target in 3D point clouds and the 2D image is proposed. Through this method, i.e., locating firstly and then fine processing, corner points can be automatically obtained, which can greatly reduce the manual operation. Finally, a joint optimization equation is established to optimize the camera’s intrinsic and extrinsic parameters of LiDAR and camera. According to our experiments, we prove the accuracy and robustness of the proposed method through projection and data consistency verifications. The accuracy can be improved by at least 15.0% when testing on the comparable traditional methods. The final results verify that our method is applicable to LiDAR with large horizontal angle resolutions. Full article

(This article belongs to the Special Issue Pattern Recognition in Remote Sensing)

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19 pages, 3495 KiB

Open AccessArticle

Filtered Convolution for Synthetic Aperture Radar Images Ship Detection

by Luyang Zhang, Haitao Wang, Lingfeng Wang, Chunhong Pan, Chunlei Huo, Qiang Liu and Xinyao Wang

Remote Sens. 2022, 14(20), 5257; https://doi.org/10.3390/rs14205257 - 20 Oct 2022

Cited by 6 | Viewed by 2120

Abstract

Synthetic aperture radar (SAR) image ship detection is currently a research hotspot in the field of national defense science and technology. However, SAR images contain a large amount of coherent speckle noise, which poses significant challenges in the task of ship detection. To address this issue, we propose filter convolution, a novel design that replaces the traditional convolution layer and suppresses coherent speckle noise while extracting features. Specifically, the convolution kernel of the filter convolution comes from the input and is generated by two modules: the kernel-generation module and local weight generation module. The kernel-generation module is a dynamic structure that generates dynamic convolution kernels using input image or feature information. The local weight generation module is based on the statistical characteristics of the input images or features and is used to generate local weights. The introduction of local weights allows the extracted features to contain more local characteristic information, which is conducive to ship detection in SAR images. In addition, we proved that the fusion of the proposed kernel-generation module and the local weight module can suppress coherent speckle noise in the SAR image. The experimental results show the excellent performance of our method on a large-scale SAR ship detection dataset-v1.0 (LS-SSDD-v1.0). It also achieved state-of-the-art performance on a high-resolution SAR image dataset (HRSID), which confirmed its applicability. Full article

(This article belongs to the Special Issue Pattern Recognition in Remote Sensing)

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19 pages, 2484 KiB

Open AccessArticle

Wetlands Classification Using Quad-Polarimetric Synthetic Aperture Radar through Convolutional Neural Networks Based on Polarimetric Features

by Shuaiying Zhang, Wentao An, Yue Zhang, Lizhen Cui and Chunhua Xie

Remote Sens. 2022, 14(20), 5133; https://doi.org/10.3390/rs14205133 - 14 Oct 2022

Cited by 2 | Viewed by 1879

Abstract

Wetlands are the “kidneys” of the earth and are crucial to the ecological environment. In this study, we utilized GF-3 quad-polarimetric synthetic aperture radar (QP) images to classify the ground objects (nearshore water, seawater, spartina alterniflora, tamarix, reed, tidal flat, and suaeda salsa) in the Yellow River Delta through convolutional neural networks (CNNs) based on polarimetric features. In this case, four schemes were proposed based on the extracted polarimetric features from the polarization coherency matrix and reflection symmetry decomposition (RSD). Through the well-known CNNs: AlexNet and VGG16 as backbone networks to classify GF-3 QP images. After testing and analysis, 21 total polarimetric features from RSD and the polarization coherency matrix for QP image classification contributed to the highest overall accuracy (OA) of 96.54% and 94.93% on AlexNet and VGG16, respectively. The performance of the polarization coherency matrix and polarimetric power features was similar but better than just using three main diagonals of the polarization coherency matrix. We also conducted noise test experiments. The results indicated that OAs and kappa coefficients decreased in varying degrees after we added 1 to 3 channels of Gaussian random noise, which proved that the polarimetric features are helpful for classification. Thus, higher OAs and kappa coefficients can be acquired when more informative polarimetric features are input CNNs. In addition, the performance of RSD was slightly better than obtained using the polarimetric coherence matrix. Therefore, RSD can help improve the accuracy of polarimetric SAR image classification of wetland objects using CNNs. Full article

(This article belongs to the Special Issue Pattern Recognition in Remote Sensing)

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29 pages, 5783 KiB

Open AccessArticle

Dim and Small Target Detection Based on Improved Hessian Matrix and F-Norm Collaborative Filtering

by Xiangsuo Fan, Juliu Li, Huajin Chen, Lei Min and Feng Li

Remote Sens. 2022, 14(18), 4490; https://doi.org/10.3390/rs14184490 - 8 Sep 2022

Cited by 3 | Viewed by 2095

Abstract

In order to effectively improve the dim and small target detection ability of photoelectric detection system to solve the high false rate issue under complex clouds scene in background modeling, a novelty Hessian matrix and F-norm collaborative filtering is proposed in this paper. Considering the influence of edge noise, we propose an improved Hessian matrix background modeling (IHMM) algorithm, where a local saliency function for adaptive representation of the local gradient difference between the target and background region is constructed to suppress the background and preserve the target. Because the target energy is still weak after the background modeling, a new local multi-scale gradient maximum (LMGM) energy-enhancement model is constructed to enhance the target signal, and with the help of LMGM, the target’s energy significant growth and the target’s recognition are clearer. Thus, based on the above preprocessing, using the motion correlation of the target between frames, this paper proposes an innovative collaborative filtering model combining F-norm and Pasteur coefficient (FNPC) to obtain the real target in sequence images. In this paper, we selected six scenes of the target size of 2 × 2 to 3 × 3 and with complex clouds and ground edge contour to finish experimental verification. By comparing with 10 algorithms, the background modeling indicators SSIM, SNR, and IC of the IHMM model are greater than 0.9999, 47.4750 dB, and 12.1008 dB, respectively. In addition, the target energy-enhancement effect of LMGM model reaches 17.9850 dB in six scenes, and when the false alarm rate is 0.01%, the detection rate of the FNPC model reaches 100% in all scenes. It shows that the algorithm proposed in this paper has excellent performance in dim and small target detection. Full article

(This article belongs to the Special Issue Pattern Recognition in Remote Sensing)

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17 pages, 1692 KiB

Open AccessArticle

Local Matrix Feature-Based Kernel Joint Sparse Representation for Hyperspectral Image Classification

by Xiang Chen, Na Chen, Jiangtao Peng and Weiwei Sun

Remote Sens. 2022, 14(17), 4363; https://doi.org/10.3390/rs14174363 - 2 Sep 2022

Cited by 2 | Viewed by 1856

Abstract

Hyperspectral image (HSI) classification is one of the hot research topics in the field of remote sensing. The performance of HSI classification greatly depends on the effectiveness of feature learning or feature design. Traditional vector-based spectral–spatial features have shown good performance in HSI classification. However, when the number of labeled samples is limited, the performance of these vector-based features is degraded. To fully mine the discriminative features in small-sample case, a novel local matrix feature (LMF) was designed to reflect both the correlation between spectral pixels and the spectral bands in a local spatial neighborhood. In particular, the LMF is a linear combination of a local covariance matrix feature and a local correntropy matrix feature, where the former describes the correlation between spectral pixels and the latter measures the similarity between spectral bands. Based on the constructed LMFs, a simple Log-Euclidean distance-based linear kernel is introduced to measure the similarity between them, and an LMF-based kernel joint sparse representation (LMFKJSR) model is proposed for HSI classification. Due to the superior performance of region covariance and correntropy descriptors, the proposed LMFKJSR shows better results than existing vector-feature-based and matrix-feature-based support vector machine (SVM) and JSR methods on three well-known HSI data sets in the case of limited labeled samples. Full article

(This article belongs to the Special Issue Pattern Recognition in Remote Sensing)

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19 pages, 4789 KiB

Open AccessArticle

MsIFT: Multi-Source Image Fusion Transformer

by Xin Zhang, Hangzhi Jiang, Nuo Xu, Lei Ni, Chunlei Huo and Chunhong Pan

Remote Sens. 2022, 14(16), 4062; https://doi.org/10.3390/rs14164062 - 19 Aug 2022

Cited by 9 | Viewed by 3431

Abstract

Multi-source image fusion is very important for improving image representation ability since its essence relies on the complementarity between multi-source information. However, feature-level image fusion methods based on the convolution neural network are impacted by the spatial misalignment between image pairs, which leads to the semantic bias in merging features and destroys the representation ability of the region-of-interests. In this paper, a novel multi-source image fusion transformer (MsIFT) is proposed. Due to the inherent global attention mechanism of the transformer, the MsIFT has non-local fusion receptive fields, and it is more robust to spatial misalignment. Furthermore, multiple classification-based downstream tasks (e.g., pixel-wise classification, image-wise classification and semantic segmentation) are unified in the proposed MsIFT framework, and the fusion module architecture is shared by different tasks. The MsIFT achieved state-of-the-art performances on the image-wise classification dataset VAIS, semantic segmentation dataset SpaceNet 6 and pixel-wise classification dataset GRSS-DFC-2013. The code and trained model are being released upon the publication of the work. Full article

(This article belongs to the Special Issue Pattern Recognition in Remote Sensing)

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23 pages, 4684 KiB

Open AccessArticle

Clustering Optimization for Triple-Frequency Combined Observations of BDS-3 Based on Improved PSO-FCM Algorithm

by Zhaoyong Qian, Yuhua Cao, Xiaoshuang Sun, Lei Ni, Zhiyu Wang and Xiaowei Chen

Remote Sens. 2022, 14(15), 3713; https://doi.org/10.3390/rs14153713 - 3 Aug 2022

Cited by 2 | Viewed by 1498

Abstract

The triple-frequency linear combination method can provide combinations with different characteristics and is one of the important methods to improve the performance of navigation services. Due to the large number of combinations and different combination performances, combinatorial clustering optimization is very important, and the efficiency of manual screening is very low. Firstly, based on the basic model, the objective equations are derived. Secondly, based on the fuzzy c-means (FCM) algorithm, three improved PSO-FCM algorithms are proposed, namely the S-PSO-FCM algorithm, L-PSO-FCM algorithm, and LOG-PSO-FCM algorithm. Thirdly, according to the different combination characteristics, the two datasets whose combined coefficients sum to 0 and 1 are emphatically discussed. Finally, the effectiveness of the improved PSO-FCM algorithms is studied based on the public dataset and the measured BeiDou-3 navigation satellite system (BDS-3) data of short baseline, long baseline, and ultra-long baseline. The results show that the performance of the proposed algorithm is better than that of the FCM algorithm, especially in short baseline and long baseline cases. Full article

(This article belongs to the Special Issue Pattern Recognition in Remote Sensing)

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20 pages, 4741 KiB

Open AccessArticle

Small Ship Detection Based on Hybrid Anchor Structure and Feature Super-Resolution

by Xiaozhu Xie, Linhao Li, Zhe An, Gang Lu and Zhiqiang Zhou

Remote Sens. 2022, 14(15), 3530; https://doi.org/10.3390/rs14153530 - 23 Jul 2022

Cited by 8 | Viewed by 1749

Abstract

Small ships in remote sensing images have blurred details and are difficult to detect. Existing algorithms usually detect small ships based on predefined anchors with different sizes. However, limited by the number of different sizes, it is difficult for anchor-based methods to match small ships of different sizes and structures during training, as they can easily cause misdetections. In this paper, we propose a hybrid anchor structure to generate region proposals for small ships, so as to take full advantage of both anchor-based methods with high localization accuracy and anchor-free methods with fewer misdetections. To unify the output evaluation and obtain the best output, a label reassignment strategy is proposed, which reassigns the sample labels according to the harmonic intersection-over-union (IoU) before and after regression. In addition, an adaptive feature pyramid structure is proposed to enhance the features of important locations on the feature map, so that the features of small ship targets are more prominent and easier to identify. Moreover, feature super-resolution technology is introduced for the region of interest (RoI) features of small ships to generate super-resolution feature representations with a small computational cost, as well as generative adversarial training to improve the realism of super-resolution features. Based on the super-resolution feature, ship proposals are further classified and regressed by using super-resolution features to obtain more accurate detection results. Detailed ablation and comparison experiments demonstrate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Pattern Recognition in Remote Sensing)

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22 pages, 5770 KiB

Open AccessArticle

A Superpixel Spatial Intuitionistic Fuzzy C-Means Clustering Algorithm for Unsupervised Classification of High Spatial Resolution Remote Sensing Images

by Xinran Ji, Liang Huang, Bo-Hui Tang, Guokun Chen and Feifei Cheng

Remote Sens. 2022, 14(14), 3490; https://doi.org/10.3390/rs14143490 - 21 Jul 2022

Cited by 4 | Viewed by 2473

Abstract

This paper proposes a superpixel spatial intuitionistic fuzzy C-means (SSIFCM) clustering algorithm to address the problems of misclassification, salt and pepper noise, and classification uncertainty arising in the pixel-level unsupervised classification of high spatial resolution remote sensing (HSRRS) images. To reduce information redundancy and ensure noise immunity and image detail preservation, we first use a superpixel segmentation to obtain the local spatial information of the HSRRS image. Secondly, based on the bias-corrected fuzzy C-means (BCFCM) clustering algorithm, the superpixel spatial intuitionistic fuzzy membership matrix is constructed by counting an intuitionistic fuzzy set and spatial function. Finally, to minimize the classification uncertainty, the local relation between adjacent superpixels is used to obtain the classification results according to the spectral features of superpixels. Four HSRRS images of different scenes in the aerial image dataset (AID) are selected to analyze the classification performance, and fifteen main existing unsupervised classification algorithms are used to make inter-comparisons with the proposed SSIFCM algorithm. The results show that the overall accuracy and Kappa coefficients obtained by the proposed SSIFCM algorithm are the best within the inter-comparison of fifteen algorithms, which indicates that the SSIFCM algorithm can effectively improve the classification accuracy of HSRRS image. Full article

(This article belongs to the Special Issue Pattern Recognition in Remote Sensing)

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