scholarly journals MDCwFB: A Multilevel Dense Connection Network with Feedback Connections for Pansharpening

2021 ◽  
Vol 13 (11) ◽  
pp. 2218
Author(s):  
Weisheng Li ◽  
Minghao Xiang ◽  
Xuesong Liang
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Feature Fusion ◽  
Multispectral Images ◽  
Dimensional Synthesis ◽  
Feedback Connection ◽  
Visual Evaluation ◽  
Spectral Angle Mapper ◽  
Practical Applications ◽  
Different Levels

In most practical applications of remote sensing images, high-resolution multispectral images are needed. Pansharpening aims to generate high-resolution multispectral (MS) images from the input of high spatial resolution single-band panchromatic (PAN) images and low spatial resolution multispectral images. Inspired by the remarkable results of other researchers in pansharpening based on deep learning, we propose a multilevel dense connection network with a feedback connection. Our network consists of four parts. The first part consists of two identical subnetworks to extract features from PAN and MS images. The second part is a multilevel feature fusion and recovery network, which is used to fuse images in the feature domain and to encode and decode features at different levels so that the network can fully capture different levels of information. The third part is a continuous feedback operation, which refines shallow features by feedback. The fourth part is an image reconstruction network. High-quality images are recovered by making full use of multistage decoding features through dense connections. Experiments on different satellite datasets show that our proposed method is superior to existing methods, through subjective visual evaluation and objective evaluation indicators. Compared with the results of other models, our results achieve significant gains on the multiple objective index values used to measure the spectral quality and spatial details of the generated image, namely spectral angle mapper (SAM), relative global dimensional synthesis error (ERGAS), and structural similarity (SSIM).

scholarly journals A Dense Encoder–Decoder Network with Feedback Connections for Pan-Sharpening

2021 ◽  
Vol 13 (22) ◽  
pp. 4505
Author(s):  
Weisheng Li ◽  
Minghao Xiang ◽  
Xuesong Liang
Keyword(s):  
Feature Extraction ◽  
Network Performance ◽  
High Spatial Resolution ◽  
Feature Fusion ◽  
Multispectral Images ◽  
Feedback Connection ◽  
Practical Applications ◽  
Long Jump ◽  
Continuous Feedback ◽  
Feedback Connections

To meet the need for multispectral images having high spatial resolution in practical applications, we propose a dense encoder–decoder network with feedback connections for pan-sharpening. Our network consists of four parts. The first part consists of two identical subnetworks, one each to extract features from PAN and MS images, respectively. The second part is an efficient feature-extraction block. We hope that the network can focus on features at different scales, so we propose innovative multiscale feature-extraction blocks that fully extract effective features from networks of various depths and widths by using three multiscale feature-extraction blocks and two long-jump connections. The third part is the feature fusion and recovery network. We are inspired by the work on U-Net network improvements to propose a brand new encoder network structure with dense connections that improves network performance through effective connections to encoders and decoders at different scales. The fourth part is a continuous feedback connection operation with overfeedback to refine shallow features, which enables the network to obtain better reconstruction capabilities earlier. To demonstrate the effectiveness of our method, we performed several experiments. Experiments on various satellite datasets show that the proposed method outperforms existing methods. Our results show significant improvements over those from other models in terms of the multiple-target index values used to measure the spectral quality and spatial details of the generated images.

An empirical cross sensor model for high-resolution multispectral imagery

2020 ◽  
Author(s):  
Manivasagam Vellalapalayam Subramanian ◽  
Gregoriy Kaplan ◽  
Offer Rozenstein
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Transformation Model ◽  
Sensor Data ◽  
Absolute Difference ◽  
Landsat 8 ◽  
Vegetation Monitoring ◽  
Spectral Angle Mapper ◽  
Statistical Measures ◽  
Sentinel 2

<p>The availability of public-domain high-resolution satellite imagery such as Sentinel-2 and Landsat-8 has increased earth observation (EO) studies across the globe. Empirically combining different EO sensor data into a single dataset increases the temporal coverage, which is useful for land-cover monitoring. In this study, a transformation model was developed for Sentinel-2 and Vegetation and Environmental New micro Spacecraft (VENμS) imagery over Israel. Both sensors offer high spatio-temporal resolution imagery, i.e., VENμS has a 10m spatial resolution with a two-day revisit period, and Sentinel-2 has a 10-20 m spatial resolution with a five-day revisit period. Near-simultaneously acquired imagery was employed for the transformation model development. The model coefficients were derived for the overlapping spectral regions of both sensors. Further, the transformation model performance was tested using various statistical measures, namely, orthogonal distance regression (ODR), spectral angle mapper (SAM), and mean absolute difference (MAD). The validation results highlighted that MAD values were reduced between Sentinel-2 and transformed VENμS reflectance. Similarly, the ODR slope values became closer to one, and the overall spectral similarity increased as demonstrated by a decrease in SAM values. This transformation function creates a unified reflectance dataset in the form of a dense time-series of observation, especially useful for vegetation monitoring.</p>

scholarly journals Comparison of Conventional Change Detection Methodologies Using High-Resolution Imagery to Find Forest Damage Caused by Typhoons

2020 ◽  
Vol 12 (19) ◽  
pp. 3242 ◽  
Author(s):  
Flavio Furukawa ◽  
Junko Morimoto ◽  
Nobuhiko Yoshimura ◽  
Masami Kaneko
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Forest Damage ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Spectral Angle Mapper ◽  
Filtering Method

The number of intense tropical cyclones is expected to increase in the future, causing severe damage to forest ecosystems. Remote sensing plays an important role in detecting changes in land cover caused by these tropical storms. Remote sensing techniques have been widely used in different phases of disaster risk management because they can deliver information rapidly to the concerned parties. Although remote sensing technology is already available, an examination of appropriate methods based on the type of disaster is still missing. Our goal is to compare the suitability of three different conventional classification methods for fast and easy change detection analysis using high-spatial-resolution and high-temporal-resolution remote sensing imagery to identify areas with windthrow and landslides caused by typhoons. In August 2016, four typhoons hit Hokkaido, the northern island of Japan, creating large areas of windthrow and landslides. We compared the normalized difference vegetation index (NDVI) filtering method, the spectral angle mapper (SAM) method, and the support vector machine (SVM) method to identify windthrow and landslides in two different study areas in southwestern Hokkaido. These methodologies were evaluated using PlanetScope data with a resolution of 3 m/px and validated with reference data based on Worldview2 data with a very high resolution of 0.46 m/px. The results showed that all three methods, when applied to high-spatial-resolution imagery, can deliver sufficient results for windthrow and landslide detection. In particular, the SAM method performed better at windthrow detection, and the NDVI filtering method performed better at landslide detection.

scholarly journals Pansharpening by Complementing Compressed Sensing with Spectral Correction

2020 ◽  
Vol 10 (17) ◽  
pp. 5789
Author(s):  
Naoko Tsukamoto ◽  
Yoshihiro Sugaya ◽  
Shinichiro Omachi
Keyword(s):  
High Resolution ◽  
Compressed Sensing ◽  
Spatial Resolution ◽  
High Spectral Resolution ◽  
Multispectral Images ◽  
Spectral Distortion ◽  
Ringing Artifacts ◽  
Corrected Intensity ◽  
Component Substitution ◽  
Better Than

Pansharpening (PS) is a process used to generate high-resolution multispectral (MS) images from high-spatial-resolution panchromatic (PAN) and high-spectral-resolution multispectral images. In this paper, we propose a method for pansharpening by focusing on a compressed sensing (CS) technique. The spectral reproducibility of the CS technique is high due to its image reproducibility, but the reproduced image is blurry. Although methods of complementing this incomplete reproduction have been proposed, it is known that the existing method may cause ringing artifacts. On the other hand, component substitution is another technique used for pansharpening. It is expected that the spatial resolution of the images generated by this technique will be as high as that of the high-resolution PAN image, because the technique uses the corrected intensity calculated from the PAN image. Based on these facts, the proposed method fuses the intensity obtained by the component substitution method and the intensity obtained by the CS technique to move the spatial resolution of the reproduced image close to that of the PAN image while reducing the spectral distortion. Experimental results showed that the proposed method can reduce spectral distortion and maintain spatial resolution better than the existing methods.

New texture-based pansharpening method using wavelet packet transform and PCA

2020 ◽  
Vol 18 (04) ◽  
pp. 2050025
Author(s):  
Asma Abdolahpoor ◽  
Peyman Kabiri
Keyword(s):  
High Resolution ◽  
Image Fusion ◽  
Spatial Resolution ◽  
Wavelet Packet ◽  
Principal Component ◽  
Wavelet Packet Transform ◽  
Multispectral Image ◽  
Multispectral Images ◽  
Low Resolution ◽  
Panchromatic Image

Image fusion is an important concept in remote sensing. Earth observation satellites provide both high-resolution panchromatic and low-resolution multispectral images. Pansharpening is aimed on fusion of a low-resolution multispectral image with a high-resolution panchromatic image. Because of this fusion, a multispectral image with high spatial and spectral resolution is generated. This paper reports a new method to improve spatial resolution of the final multispectral image. The reported work proposes an image fusion method using wavelet packet transform (WPT) and principal component analysis (PCA) methods based on the textures of the panchromatic image. Initially, adaptive PCA (APCA) is applied to both multispectral and panchromatic images. Consequently, WPT is used to decompose the first principal component of multispectral and panchromatic images. Using WPT, high frequency details of both panchromatic and multispectral images are extracted. In areas with similar texture, extracted spatial details from the panchromatic image are injected into the multispectral image. Experimental results show that the proposed method can provide promising results in fusing multispectral images with high-spatial resolution panchromatic image. Moreover, results show that the proposed method can successfully improve spectral features of the multispectral image.

Practical applications of scanning tunneling microscopy

1989 ◽  
Vol 47 ◽  
pp. 18-19
Author(s):  
D. R. Denley
Keyword(s):  
High Resolution ◽  
Scanning Tunneling Microscopy ◽  
Quantitative Information ◽  
Digital Data ◽  
Scanning Tunneling ◽  
Ambient Atmosphere ◽  
Tunneling Microscopy ◽  
Clear Trend ◽  
Practical Applications ◽  
Promising Tool

Scanning tunneling microscopy (STM) has recently been introduced as a promising tool for analyzing surface atomic structure. We have used STM for its extremely high resolution (especially the direction normal to surfaces) and its ability for imaging in ambient atmosphere. We have examined surfaces of metals, semiconductors, and molecules deposited on these materials to achieve atomic resolution in favorable cases.When the high resolution capability is coupled with digital data acquisition, it is simple to get quantitative information on surface texture. This is illustrated for the measurement of surface roughness of evaporated gold films as a function of deposition temperature and annealing time in Figure 1. These results show a clear trend for which the roughness, as well as the experimental deviance of the roughness is found to be minimal for evaporation at 300°C. It is also possible to contrast different measures of roughness.

Mapping and Monitoring of the Land Use/Cover Changes in the Wider Area of ltanos, Crete, Using Very High Resolution EO Imagery With Specific Interest in Archaeological Sites

2019 ◽  
Author(s):  
Sawyer Reid stippa ◽  
George Petropoulos ◽  
Leonidas Toulios ◽  
Prashant K. Srivastava
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Archaeological Site ◽  
Digital Photography ◽  
Archaeological Sites ◽  
Large Area ◽  
High Resolution Imagery ◽  
Site Mapping ◽  
Rule Sets

Archaeological site mapping is important for both understanding the history as well as protecting them from excavation during the developmental activities. As archaeological sites generally spread over a large area, use of high spatial resolution remote sensing imagery is becoming increasingly applicable in the world. The main objective of this study was to map the land cover of the Itanos area of Crete and of its changes, with specific focus on the detection of the landscape’s archaeological features. Six satellite images were acquired from the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digital photography of two known archaeological sites was used for validation. An Object Based Image Analysis (OBIA) classification was subsequently developed using the five acquired satellite images. Two rule-sets were created, one using the standard four bands which both satellites have and another for the two WorldView-2 images their four extra bands included. Validation of the thematic maps produced from the classification scenarios confirmed a difference in accuracy amongst the five images. Comparing the results of a 4-band rule-set versus the 8-band showed a slight increase in classification accuracy using extra bands. The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separating the archaeological sites from the open spaces with little or no vegetation proved challenging. This was mainly due to the high spectral similarity between rocks and the archaeological ruins. The satellite data spatial resolution allowed for the accuracy in defining larger archaeological sites, but still was a difficulty in distinguishing smaller areas of interest. The digital photography data provided a very good 3D representation for the archaeological sites, assisting as well in validating the satellite-derived classification maps. All in all, our study provided further evidence that use of high resolution imagery may allow for archaeological sites to be located, but only where they are of a suitable size archaeological features.

High Resolution Current Imaging by Direct Magnetic Field Sensing

2003 ◽  
Author(s):  
S.I. Woods ◽  
Nesco M. Lettsome ◽  
A.B. Cawthorne ◽  
L.A. Knauss ◽  
R.H. Koch
Keyword(s):  
Magnetic Field ◽  
High Resolution ◽  
Spatial Resolution ◽  
Great Promise ◽  
Magnetoresistive Sensor ◽  
Magnetic Sensitivity ◽  
Current Lines ◽  
Scanning Squid ◽  
Scanning Fiber ◽  
Ferromagnetic Probe

Abstract Two types of magnetic microscopes have been investigated for use in high resolution current mapping. The scanning fiber/SQUID microscope uses a SQUID sensor coupled to a nanoscale ferromagnetic probe, and the GMR microscope employs a nanoscale giant magnetoresistive sensor. Initial scans demonstrate that these microscopes can resolve current lines less than 10 µm apart with edge resolution of 1 µm. These types of microscopes are compared with the performance of a standard scanning SQUID microscope and with each other with respect to spatial resolution and magnetic sensitivity. Both microscopes show great promise for identifying current defects in die level devices.

scholarly journals Mineral Detection Using Sharpened VNIR and SWIR Bands of Worldview-3 Satellite Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 5518
Author(s):  
Honglyun Park ◽  
Jaewan Choi
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Near Infrared ◽  
False Negative ◽  
Similarity Score ◽  
Majority Voting ◽  
Similarity Analysis ◽  
Spectral Angle Mapper ◽  
Mineral Detection ◽  
Shortwave Infrared

Worldview-3 satellite imagery provides panchromatic images with a high spatial resolution and visible near infrared (VNIR) and shortwave infrared (SWIR) bands with a low spatial resolution. These images can be used for various applications such as environmental analysis, urban monitoring and surveying for sustainability. In this study, mineral detection was performed using Worldview-3 satellite imagery. A pansharpening technique was applied to the spatial resolution of the panchromatic image to effectively utilize the VNIR and SWIR bands of Worldview-3 satellite imagery. The following representative similarity analysis techniques were implemented for the mineral detection: the spectral angle mapper (SAM), spectral information divergence (SID) and the normalized spectral similarity score (NS3). In addition, pixels that could be estimated to indicate minerals were calculated by applying an empirical threshold to each similarity analysis result. A majority voting technique was applied to the results of each similarity analysis and pixels estimated to indicate minerals were finally selected. The results of each similarity analysis were compared to evaluate the accuracy of the proposed methods. From that comparison, it could be confirmed that false negative and false positive rates decreased when the methods proposed in the present study were applied.

scholarly journals Deriving VIIRS High-Spatial Resolution Water Property Data over Coastal and Inland Waters Using Deep Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1944
Author(s):  
Xiaoming Liu ◽  
Menghua Wang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Ocean Color ◽  
Super Resolution ◽  
Inland Waters ◽  
Coastal Oceans ◽  
Chl A ◽  
Color Data

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been a reliable source of ocean color data products, including five moderate (M) bands and one imagery (I) band normalized water-leaving radiance spectra nLw(λ). The spatial resolutions of the M-band and I-band nLw(λ) are 750 m and 375 m, respectively. With the technique of convolutional neural network (CNN), the M-band nLw(λ) imagery can be super-resolved from 750 m to 375 m spatial resolution by leveraging the high spatial resolution features of I1-band nLw(λ) data. However, it is also important to enhance the spatial resolution of VIIRS-derived chlorophyll-a (Chl-a) concentration and the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), as well as other biological and biogeochemical products. In this study, we describe our effort to derive high-resolution Kd(490) and Chl-a data based on super-resolved nLw(λ) images at the VIIRS five M-bands. To improve the network performance over extremely turbid coastal oceans and inland waters, the networks are retrained with a training dataset including ocean color data from the Bohai Sea, Baltic Sea, and La Plata River Estuary, covering water types from clear open oceans to moderately turbid and highly turbid waters. The evaluation results show that the super-resolved Kd(490) image is much sharper than the original one, and has more detailed fine spatial structures. A similar enhancement of finer structures is also found in the super-resolved Chl-a images. Chl-a filaments are much sharper and thinner in the super-resolved image, and some of the very fine spatial features that are not shown in the original images appear in the super-resolved Chl-a imageries. The networks are also applied to four other coastal and inland water regions. The results show that super-resolution occurs mainly on pixels of Chl-a and Kd(490) features, especially on the feature edges and locations with a large spatial gradient. The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd(490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images.

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