An Efficient Deep Unsupervised Superresolution Model for Remote Sensing Images

Sheikholeslami, Mohammad Moein; Nadi, Saeed; Naeini, Amin Alizadeh; Ghamisi, Pedram

doi:10.1109/jstars.2020.2984589

Cited by 13 publications

(9 citation statements)

References 34 publications

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“…Current super resolution research (Sheikholeslami et al 2020;Yokoya et al 2017;Zhang et al 2020a, b) evaluates algorithms based on data sets which have high spatial resolutions. They down-sample the spatial resolution of original data sets to create the counterpart low resolution data.…”

Section: Super-resolutionmentioning

confidence: 99%

MDAS: A New Multimodal Benchmark Dataset for Remote Sensing

Liu

Hong

et al. 2022

Preprint

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Abstract. In Earth observation, multimodal data fusion is an intuitive strategy to break the limitation of individual data. Complementary physical contents of data sources allow comprehensive and precise information retrieve. With current satellite missions, such as ESA Copernicus programme, various data will be accessible at an affordable cost. Future applications will have many options on data sources. Such privilege can be beneficial only if algorithms are ready to work with various data sources. However, current data fusion studies mostly focus on the fusion of two data sources. There are two reasons, first, different combinations of data sources face different scientific challenges. For example, the fusion of synthetic aperture radar (SAR) data and optical images needs to handle the geometric difference, while the fusion of hyperspectral and multispectral images deals with different resolutions on spatial and spectral domains. Second, nowadays, it is still both financially and labour expensive to acquire multiple data sources for the same region at the same time. In this paper, we provide the community a benchmark multimodal data set, MDAS, for the city of Augsburg, Germany. MDAS includes synthetic aperture radar (SAR) data, multispectral image, hyperspectral image, digital surface model (DSM), and geographic information system (GIS) data. All these data are collected on the same date, 7th May 2018. MDAS is a new benchmark data set that provides researchers rich options on data selections. In this paper, we run experiments for three typical remote sensing applications, namely, resolution enhancement, spectral unmixing, and land cover classification, on MDAS data set. Our experiments demonstrate the performance of representative state-of-the-art algorithms whose outcomes can sever as baselines for further studies.

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Section: Super-resolutionmentioning

confidence: 99%

MDAS: A New Multimodal Benchmark Dataset for Remote Sensing

Liu

Hong

et al. 2022

Preprint

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“…The first feature set contained spectral features, including original spectral bands from optical imageries. The second feature set contained spectral and spatial features, including nDSM, NDVI, or ExG (2). Finally, the third feature set contained deep features extracted by training proposed CAE over raw spectral information of each band.…”

Section: Competing Featuresmentioning

confidence: 99%

“…Satellite and airborne image classification is one of the most demanding remote sensing (RS) applications [1]. In general, image classification can be categorized as supervised and unsupervised approaches [2]. Although supervised algorithms perform better than unsupervised ones, they require labeled or training samples.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Multi-Sensor Remote Sensing Image Clustering in Urban Areas via Boosted Convolutional Autoencoder (BCAE)

et al. 2021

Self Cite

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High-resolution urban image clustering has remained a challenging task. This is mainly because its performance strongly depends on the discrimination power of features. Recently, several studies focused on unsupervised learning methods by autoencoders to learn and extract more efficient features for clustering purposes. This paper proposes a Boosted Convolutional AutoEncoder (BCAE) method based on feature learning for efficient urban image clustering. The proposed method was applied to multi-sensor remote-sensing images through a multistep workflow. The optical data were first preprocessed by applying a Minimum Noise Fraction (MNF) transformation. Then, these MNF features, in addition to the normalized Digital Surface Model (nDSM) and vegetation indexes such as Normalized Difference Vegetation Index (NDVI) and Excess Green (ExG(2)), were used as the inputs of the BCAE model. Next, our proposed convolutional autoencoder was trained to automatically encode upgraded features and boost the hand-crafted features for producing more clustering-friendly ones. Then, we employed the Mini Batch K-Means algorithm to cluster deep features. Finally, the comparative feature sets were manually designed in three modes to prove the efficiency of the proposed method in extracting compelling features. Experiments on three datasets show the efficiency of BCAE for feature learning. According to the experimental results, by applying the proposed method, the ultimate features become more suitable for clustering, and spatial correlation among the pixels in the feature learning process is also considered.

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“…Recently, deep learning (DL) models have gained popularity and have demonstrated impressive results in various applications, such as pattern recognition [23] [24], remote sensing images [25], and built-up land expansion [26]. Advanced versions of recurrent neural networks (RNNs) or combinations of convolutional neural networks (CNNs) are frequently used for PM 2.5 prediction.…”

Section: Introductionmentioning

confidence: 99%

CombineDeepNet: A Deep Network for Multistep Prediction of Near-Surface PM$_{2.5}$ Concentration

Dey,

Dev,

Phelan

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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PM2.5 is a type of air pollutant that can cause respiratory and cardiovascular problems. Precise PM2.5 (µg/m 3 ) concentration prediction may help reduce health concerns and provide early warnings. To better understand the air pollution, a number of approaches have been presented for predicting PM2.5 concentrations. Previous research used deep learning models for hourly predictions of air pollutant due to their success in pattern recognition, however, these models were unsuitable for multi-site, long-term predictions, particularly in regards to the correlation between pollutants and meteorological data. This article proposes the combine deep network (CombineDeepNet), which combines multiple deep network, including a bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and a shallow model represented by fully connected layers (FC), to create a hybrid forecasting system. It can effectively capture the complex relationships between air pollutants and various influencing factors to predict hourly PM2.5 concentrations in multiple monitoring sites based in China. The best root mean square error (RMSE) achieved was 22.0 µg/m 3 (long-term) and 6.2 µg/m 3 (short-term), with mean absolute error (MAE) values of 3.4 µg/m 3 (long-term) and 2.2 µg/m 3 (short-term). Additionally, the correlation coefficient (R 2 ) reached 0.96 (longterm) and 0.83 (short-term) across six monitoring sites. These results demonstrate that CombineDeepNet enhances prediction accuracy compared to popular deep learning methods. Therefore, CombineDeepNet proves to be a important framework for predicting PM2.5 concentration.

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An Efficient Deep Unsupervised Superresolution Model for Remote Sensing Images

Cited by 13 publications

References 34 publications

MDAS: A New Multimodal Benchmark Dataset for Remote Sensing

MDAS: A New Multimodal Benchmark Dataset for Remote Sensing

An Efficient Multi-Sensor Remote Sensing Image Clustering in Urban Areas via Boosted Convolutional Autoencoder (BCAE)

CombineDeepNet: A Deep Network for Multistep Prediction of Near-Surface PM$_{2.5}$ Concentration

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