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25 pages, 4585 KiB

Open AccessArticle

Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images

by Sam L. Polk, Kangning Cui, Aland H. Y. Chan, David A. Coomes, Robert J. Plemmons and James M. Murphy

Remote Sens. 2023, 15(4), 1053; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15041053 - 15 Feb 2023

Cited by 7 | Viewed by 2269

Abstract

Hyperspectral images taken from aircraft or satellites contain information from hundreds of spectral bands, within which lie latent lower-dimensional structures that can be exploited for classifying vegetation and other materials. A disadvantage of working with hyperspectral images is that, due to an inherent [...] Read more.

Hyperspectral images taken from aircraft or satellites contain information from hundreds of spectral bands, within which lie latent lower-dimensional structures that can be exploited for classifying vegetation and other materials. A disadvantage of working with hyperspectral images is that, due to an inherent trade-off between spectral and spatial resolution, they have a relatively coarse spatial scale, meaning that single pixels may correspond to spatial regions containing multiple materials. This article introduces the Diffusion and Volume maximization-based Image Clustering (D-VIC) algorithm for unsupervised material clustering to address this problem. By directly incorporating pixel purity into its labeling procedure, D-VIC gives greater weight to pixels corresponding to a spatial region containing just a single material. D-VIC is shown to outperform comparable state-of-the-art methods in extensive experiments on a range of hyperspectral images, including land-use maps and highly mixed forest health surveys (in the context of ash dieback disease), implying that it is well-equipped for unsupervised material clustering of spectrally-mixed hyperspectral datasets. Full article

(This article belongs to the Special Issue Theory and Application of Machine Learning in Remote Sensing)

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20 pages, 6044 KiB

Open AccessArticle

Multiscale Superpixel Guided Discriminative Forest for Hyperspectral Anomaly Detection

by Xi Cheng, Min Zhang, Sheng Lin, Kexue Zhou, Liang Wang and Hai Wang

Remote Sens. 2022, 14(19), 4828; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194828 - 27 Sep 2022

Cited by 8 | Viewed by 1672

Abstract

Recently, the isolation forest (IF) methods have received increasing attention for their promising performance in hyperspectral anomaly detection (HAD). However, limited by the ability of exploiting spatial-spectral information, existing IF-based methods suffer from a lot of false alarms and disappointing performance of detecting [...] Read more.

Recently, the isolation forest (IF) methods have received increasing attention for their promising performance in hyperspectral anomaly detection (HAD). However, limited by the ability of exploiting spatial-spectral information, existing IF-based methods suffer from a lot of false alarms and disappointing performance of detecting local anomalies. To overcome the two problems, a multiscale superpixel guided discriminative forest method is proposed for HAD. First, the multiscale superpixel segmentation is employed to generate some homogeneous regions, and it can effectively extract spatial information to guide anomaly detection for the discriminative forest in local areas. Then, a novel discriminative forest (DF) model with the gain split criterion is designed, which enhances the sensitivity of the DF to local anomalies by the utilization of multi-dimension spectral bands for node division; meanwhile, the acceptable range of hyperplane attribute values is introduced to capture any unseen anomaly pixels that are out-of-range in the evaluation stage. Finally, for the high false alarm rate situation in the existing IF-based algorithms, the multiscale fusion with guided filtering is put forward to refine the initial detection results from the DF. In addition, the extensive experimental results on four real hyperspectral datasets demonstrate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Theory and Application of Machine Learning in Remote Sensing)

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30 pages, 2011 KiB

Open AccessArticle

Long-Tailed Graph Representation Learning via Dual Cost-Sensitive Graph Convolutional Network

by Yijun Duan, Xin Liu, Adam Jatowt, Hai-tao Yu, Steven Lynden, Kyoung-Sook Kim and Akiyoshi Matono

Remote Sens. 2022, 14(14), 3295; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143295 - 08 Jul 2022

Cited by 3 | Viewed by 1515

Abstract

Deep learning algorithms have seen a massive rise in popularity for remote sensing over the past few years. Recently, studies on applying deep learning techniques to graph data in remote sensing (e.g., public transport networks) have been conducted. In graph node classification tasks, [...] Read more.

Deep learning algorithms have seen a massive rise in popularity for remote sensing over the past few years. Recently, studies on applying deep learning techniques to graph data in remote sensing (e.g., public transport networks) have been conducted. In graph node classification tasks, traditional graph neural network (GNN) models assume that different types of misclassifications have an equal loss and thus seek to maximize the posterior probability of the sample nodes under labeled classes. The graph data used in realistic scenarios tend to follow unbalanced long-tailed class distributions, where a few majority classes contain most of the vertices and the minority classes contain only a small number of nodes, making it difficult for the GNN to accurately predict the minority class samples owing to the classification tendency of the majority classes. In this paper, we propose a dual cost-sensitive graph convolutional network (DCSGCN) model. The DCSGCN is a two-tower model containing two subnetworks that compute the posterior probability and the misclassification cost. The model uses the cost as ”complementary information” in a prediction to correct the posterior probability under the perspective of minimal risk. Furthermore, we propose a new method for computing the node cost labels based on topological graph information and the node class distribution. The results of extensive experiments demonstrate that DCSGCN outperformed other competitive baselines on different real-world imbalanced long-tailed graphs. Full article

(This article belongs to the Special Issue Theory and Application of Machine Learning in Remote Sensing)

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21 pages, 49147 KiB

Open AccessArticle

Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel

by Keren Goldberg, Ittai Herrmann, Uri Hochberg and Offer Rozenstein

Remote Sens. 2021, 13(17), 3488; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173488 - 02 Sep 2021

Cited by 4 | Viewed by 4714

Abstract

The overarching aim of this research was to develop a method for deriving crop maps from a time series of Sentinel-2 images between 2017 and 2018 to address global challenges in agriculture and food security. This study is the first step towards improving [...] Read more.

The overarching aim of this research was to develop a method for deriving crop maps from a time series of Sentinel-2 images between 2017 and 2018 to address global challenges in agriculture and food security. This study is the first step towards improving crop mapping based on phenological features retrieved from an object-based time series on a national scale. Five main crops in Israel were classified: wheat, barley, cotton, carrot, and chickpea. To optimize the object-based classification process, different characteristics and inputs of the mean shift segmentation algorithm were tested, including vegetation indices, three-band combinations, and high/low emphasis on the spatial and spectral characteristics. Four known vegetation indices (VIs)-based time series were tested. Additionally, we compared two widely used machine learning methods for crop classification, support vector machine (SVM) and random forest (RF), in addition to a newer classifier, extreme gradient boosting (XGBoost). Lastly, we examined two accuracy measures—overall accuracy (OA) and area under the curve (AUC)—in order to optimally estimate the accuracy in the case of imbalanced class representation. Mean shift best performed when emphasizing both the spectral and spatial characteristics while using the green, red, and near-infrared (NIR) bands as input. Both accuracy measures showed that RF and XGBoost classified different types of crops with significantly greater success than achieved by SVM. Nevertheless, AUC was better able to represent the significant differences between the classification algorithms than OA was. None of the VIs showed a significantly higher contribution to the classification. However, normalized difference infrared index (NDII) with XGBoost classifier showed the highest AUC results (88%). This study demonstrates that the short-wave infrared (SWIR) band with XGBoost improves crop type classification results. Furthermore, the study emphasizes the importance of addressing imbalanced classification datasets by using a proper accuracy measure. Since object-based classification and phenological features derived from a VI-based time series are widely used to produce crop maps, the current study is also relevant for operational agricultural management and informatics at large scales. Full article

(This article belongs to the Special Issue Theory and Application of Machine Learning in Remote Sensing)

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18 pages, 5242 KiB

Open AccessArticle

Hyperspectral Image Clustering with Spatially-Regularized Ultrametrics

by Shukun Zhang and James M. Murphy

Remote Sens. 2021, 13(5), 955; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050955 - 04 Mar 2021

Cited by 3 | Viewed by 1891

Abstract

We propose a method for the unsupervised clustering of hyperspectral images based on spatially regularized spectral clustering with ultrametric path distances. The proposed method efficiently combines data density and spectral-spatial geometry to distinguish between material classes in the data, without the need for [...] Read more.

We propose a method for the unsupervised clustering of hyperspectral images based on spatially regularized spectral clustering with ultrametric path distances. The proposed method efficiently combines data density and spectral-spatial geometry to distinguish between material classes in the data, without the need for training labels. The proposed method is efficient, with quasilinear scaling in the number of data points, and enjoys robust theoretical performance guarantees. Extensive experiments on synthetic and real HSI data demonstrate its strong performance compared to benchmark and state-of-the-art methods. Indeed, the proposed method not only achieves excellent labeling accuracy, but also efficiently estimates the number of clusters. Thus, unlike almost all existing hyperspectral clustering methods, the proposed algorithm is essentially parameter-free. Full article

(This article belongs to the Special Issue Theory and Application of Machine Learning in Remote Sensing)

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16 pages, 6432 KiB

Open AccessArticle

Deep Learning for RFI Artifact Recognition in Sentinel-1 Data

by Piotr Artiemjew, Agnieszka Chojka and Jacek Rapiński

Remote Sens. 2021, 13(1), 7; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010007 - 22 Dec 2020

Cited by 14 | Viewed by 2838

Abstract

Beyond the variety of unwanted disruptions that appear quite frequently in synthetic aperture radar (SAR) measurements, radio-frequency interference (RFI) is one of the most challenging issues due to its various forms and sources. Unfortunately, over the years, this problem has grown worse. RFI [...] Read more.

Beyond the variety of unwanted disruptions that appear quite frequently in synthetic aperture radar (SAR) measurements, radio-frequency interference (RFI) is one of the most challenging issues due to its various forms and sources. Unfortunately, over the years, this problem has grown worse. RFI artifacts not only hinder processing of SAR data, but also play a significant role when it comes to the quality, reliability, and accuracy of the final outcomes. To address this issue, a robust, effective, and—importantly—easy-to-implement method for identifying RFI-affected images was developed. The main aim of the proposed solution is the support of the automatic permanent scatters in SAR (PSInSAR) processing workflow through the exclusion of contaminated SAR data that could lead to misinterpretation of the calculation results. The approach presented in this paper for the purpose of recognition of these specific artifacts is based on deep learning. Considering different levels of image damage, we used three variants of a LeNet-type convolutional neural network. The results show the high efficiency of our model used directly on sample data. Full article

(This article belongs to the Special Issue Theory and Application of Machine Learning in Remote Sensing)

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Other

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16 pages, 3664 KiB

Open AccessTechnical Note

Deep Hashing Using Proxy Loss on Remote Sensing Image Retrieval

by Xue Shan, Pingping Liu, Yifan Wang, Qiuzhan Zhou and Zhen Wang

Remote Sens. 2021, 13(15), 2924; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152924 - 25 Jul 2021

Cited by 15 | Viewed by 2537

Abstract

With the improvement of various space-satellite shooting methods, the sources, scenes, and quantities of remote sensing data are also increasing. An effective and fast remote sensing image retrieval method is necessary, and many researchers have conducted a lot of work in this direction. [...] Read more.

With the improvement of various space-satellite shooting methods, the sources, scenes, and quantities of remote sensing data are also increasing. An effective and fast remote sensing image retrieval method is necessary, and many researchers have conducted a lot of work in this direction. Nevertheless, a fast retrieval method called hashing retrieval is proposed to improve retrieval speed, while maintaining retrieval accuracy and greatly reducing memory space consumption. At the same time, proxy-based metric learning losses can reduce convergence time. Naturally, we present a proxy-based hash retrieval method, called DHPL (Deep Hashing using Proxy Loss), which combines hash code learning with proxy-based metric learning in a convolutional neural network. Specifically, we designed a novel proxy metric learning network, and we used one hash loss function to reduce the quantified losses. For the University of California Merced (UCMD) dataset, DHPL resulted in a mean average precision (mAP) of up to 98.53% on 16 hash bits, 98.83% on 32 hash bits, 99.01% on 48 hash bits, and 99.21% on 64 hash bits. For the aerial image dataset (AID), DHPL achieved an mAP of up to 93.53% on 16 hash bits, 97.36% on 32 hash bits, 98.28% on 48 hash bits, and 98.54% on 64 bits. Our experimental results on UCMD and AID datasets illustrate that DHPL could generate great results compared with other state-of-the-art hash approaches. Full article

(This article belongs to the Special Issue Theory and Application of Machine Learning in Remote Sensing)

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16 pages, 5127 KiB

Open AccessTechnical Note

Intra-Pulse Modulation Classification of Radar Emitter Signals Based on a 1-D Selective Kernel Convolutional Neural Network

by Shibo Yuan, Bin Wu and Peng Li

Remote Sens. 2021, 13(14), 2799; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142799 - 16 Jul 2021

Cited by 15 | Viewed by 2985

Abstract

The intra-pulse modulation of radar emitter signals is a key feature for analyzing radar systems. Traditional methods which require a tremendous amount of prior knowledge are insufficient to accurately classify the intra-pulse modulations. Recently, deep learning-based methods, especially convolutional neural networks (CNN), have [...] Read more.

The intra-pulse modulation of radar emitter signals is a key feature for analyzing radar systems. Traditional methods which require a tremendous amount of prior knowledge are insufficient to accurately classify the intra-pulse modulations. Recently, deep learning-based methods, especially convolutional neural networks (CNN), have been used in classification of intra-pulse modulation of radar emitter signals. However, those two-dimensional CNN-based methods, which require dimensional transformation of the original sampled signals in the stage of data preprocessing, are resource-consuming and poorly feasible. In order to solve these problems, we proposed a one-dimensional selective kernel convolutional neural network (1-D SKCNN) to accurately classify the intra-pulse modulation of radar emitter signals. Compared with other previous methods described in the literature, the data preprocessing of the proposed method merely includes zero-padding, fast Fourier transformation (FFT) and amplitude normalization, which is much faster and easier to achieve. The experimental results indicate that the proposed method has the advantages of faster speed in data preprocessing and higher accuracy in intra-pulse modulation classification of radar emitter signals. Full article

(This article belongs to the Special Issue Theory and Application of Machine Learning in Remote Sensing)

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