scholarly journals Enhanced Back-Projection as Postprocessing for Pansharpening

2019 ◽  
Vol 11 (6) ◽  
pp. 712 ◽  
Author(s):  
Junmin Liu ◽  
Jing Ma ◽  
Rongrong Fei ◽  
Huirong Li ◽  
Jiangshe Zhang
Keyword(s):  
Spatial Resolution ◽  
Modulation Transfer Function ◽  
High Spatial Resolution ◽  
Multispectral Image ◽  
Back Projection ◽  
Modulation Transfer ◽  
Panchromatic Image ◽  
Consistency Property ◽  
High Pass ◽  
Satellite Sensor

Pansharpening is the process of integrating a high spatial resolution panchromatic image with a low spatial resolution multispectral image to obtain a multispectral image with high spatial and spectral resolution. Over the last decade, several algorithms have been developed for pansharpening. In this paper, a technique, called enhanced back-projection (EBP), is introduced and applied as postprocessing on the pansharpening. The proposed EBP first enhances the spatial details of the pansharpening results by histogram matching and high-pass modulation, followed by a back-projection process, which takes into account the modulation transfer function (MTF) of the satellite sensor such that the pansharpening results obey the consistency property. The EBP is validated on four datasets acquired by different satellites and several commonly used pansharpening methods. The pansharpening results achieve substantial improvements by this postprocessing technique, which is widely applicable and requires no modification of existing pansharpening methods.

scholarly journals A CNN-Based Pan-Sharpening Method for Integrating Panchromatic and Multispectral Images Using Landsat 8

2019 ◽  
Vol 11 (22) ◽  
pp. 2606 ◽  
Author(s):  
Zhiqiang Li ◽  
Chengqi Cheng
Keyword(s):  
Spatial Resolution ◽  
Mean Squared Error ◽  
High Spatial Resolution ◽  
Multispectral Image ◽  
Landsat 8 ◽  
Spectral Angle Mapper ◽  
Learning Capacity ◽  
Panchromatic Image ◽  
Highly Nonlinear ◽  
Construction Tasks

The increasing availability of sensors enables the combination of a high-spatial-resolution panchromatic image and a low-spatial-resolution multispectral image, which has become a hotspot in recent years for many applications. To address the spectral and spatial distortions that adversely affect the conventional methods, a pan-sharpening method based on a convolutional neural network (CNN) architecture is proposed in this paper, where the low-spatial-resolution multispectral image is upgraded and integrated with the high-spatial-resolution panchromatic image to produce a new multispectral image with high spatial resolution. Based on the pyramid structure of the CNN architecture, the proposed method has high learning capacity to generate more representative and robust hierarchical features for construction tasks. Moreover, the highly nonlinear fusion process can be effectively simulated by stacking several linear filtering layers, which is suitable for learning the complex mapping relationship between a high-spatial-resolution panchromatic and low-spatial-resolution multispectral image. Both qualitative and quantitative experimental analyses were carried out on images captured from a Landsat 8 on-board operational land imager (LOI) sensor to demonstrate the method’s performance. The results regarding the sensitivity analysis of the involved parameters indicate the effects of parameters on the performance of our CNN-based pan-sharpening approach. Additionally, our CNN-based pan-sharpening approach outperforms other existing conventional pan-sharpening methods with a more promising fusion result for different landcovers, with differences in Erreur Relative Globale Adimensionnelle de Synthse (ERGAS), root-mean-squared error (RMSE), and spectral angle mapper (SAM) of 0.69, 0.0021, and 0.81 on average, respectively.

scholarly journals Joint Spatial-spectral Resolution Enhancement of Multispectral Images with Spectral Matrix Factorization and Spatial Sparsity Constraints

2020 ◽  
Vol 12 (6) ◽  
pp. 993 ◽  
Author(s):  
Chen Yi ◽  
Yong-qiang Zhao ◽  
Jonathan Cheung-Wai Chan ◽  
Seong G. Kong
Keyword(s):  
Spatial Resolution ◽  
Spectral Resolution ◽  
High Spatial Resolution ◽  
Resolution Enhancement ◽  
Multispectral Image ◽  
Observation Model ◽  
Multispectral Images ◽  
Spectral Matrix ◽  
Spectral Bands ◽  
Landsat 7

This paper presents a joint spatial-spectral resolution enhancement technique to improve the resolution of multispectral images in the spatial and spectral domain simultaneously. Reconstructed hyperspectral images (HSIs) from an input multispectral image represent the same scene in higher spatial resolution, with more spectral bands of narrower wavelength width than the input multispectral image. Many existing improvement techniques focus on spatial- or spectral-resolution enhancement, which may cause spectral distortions and spatial inconsistency. The proposed scheme introduces virtual intermediate variables to formulate a spectral observation model and a spatial observation model. The models alternately solve spectral dictionary and abundances to reconstruct desired high-resolution HSIs. An initial spectral dictionary is trained from prior HSIs captured in different landscapes. A spatial dictionary trained from a panchromatic image and its sparse coefficients provide high spatial-resolution information. The sparse coefficients are used as constraints to obtain high spatial-resolution abundances. Experiments performed on simulated datasets from AVIRIS/Landsat 7 and a real Hyperion/ALI dataset demonstrate that the proposed method outperforms the state-of-the-art spatial- and spectral-resolution enhancement methods. The proposed method also worked well for combination of exiting spatial- and spectral-resolution enhancement methods.

scholarly journals Lunar Image Fusion using Wavelet Transform

2013 ◽  
pp. 211-215
Author(s):  
Dr.Vani. K ◽  
Anto. A. Micheal
Keyword(s):  
High Resolution ◽  
Image Fusion ◽  
Spatial Resolution ◽  
Spectral Resolution ◽  
Lunar Surface ◽  
High Spatial Resolution ◽  
Multispectral Image ◽  
High Spectral Resolution ◽  
Low Resolution ◽  
Mineral Mapping

This paper is an attempt to combine high resolution panchromatic lunar image with low resolution multispectral lunar image to produce a composite image using wavelet approach. There are many sensors that provide us image data about the lunar surface. The spatial resolution and spectral resolution is unique for each sensor, thereby resulting in limitation in extraction of information about the lunar surface. The high resolution panchromatic lunar image has high spatial resolution but low spectral resolution; the low resolution multispectral image has low spatial resolution but high spectral resolution. Extracting features such as craters, crater morphology, rilles and regolith surfaces with a low spatial resolution in multispectral image may not yield satisfactory results. A sensor which has high spatial resolution can provide better information when fused with the high spectral resolution. These fused image results pertain to enhanced crater mapping and mineral mapping in lunar surface. Since fusion using wavelet preserve spectral content needed for mineral mapping, image fusion has been done using wavelet approach.

scholarly journals Pattern recognition of forest canopy using the airborne hyperspectral data and multi-bands high spatial resolution satellite sensor worldview-2 data. A results comparison and accuracy estimation

2019 ◽  
pp. 89-102
Author(s):  
V. V. Kozoderov ◽  
V. D. Egorov
Keyword(s):  
Remote Sensing ◽  
Pattern Recognition ◽  
Spatial Resolution ◽  
Forest Canopy ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Hyperspectral Data ◽  
Automatic Data ◽  
Sensing Data ◽  
Satellite Sensor

Pattern recognition of forest surface from remote sensing data: using the airborne hyperspectral data and using multi-bands high spatial resolution satellite sensor WorldView‑2 data are investigated. The early proposed method and standard QDA method for calculations were used. A comparison of calculations results were conducted. A recognition calculation accuracy range for airborne and satellite remote sensing data for three forest surface fragments for different created data bases for recognition system has been assessed. Some opportunities of automatic data preparing of created system were displayed. Some special features of pattern recognition of forest surfaces from hyperspectral airborne data and from multi-bands high spatial resolution satellite data were discussed.

scholarly journals EFFECT OF PANSHARPENED IMAGE ON SOME OF PIXEL BASED AND OBJECT BASED CLASSIFICATION ACCURACY

2016 ◽  
Vol XLI-B7 ◽  
pp. 235-239 ◽  
Author(s):  
P. Karakus ◽  
H. Karabork
Keyword(s):  
Spatial Resolution ◽  
Spectral Resolution ◽  
Classification Accuracy ◽  
Multispectral Image ◽  
Classification Method ◽  
Support Vector ◽  
Classification Methods ◽  
Panchromatic Image ◽  
Object Based ◽  
Spot 5

Classification is the most important method to determine type of crop contained in a region for agricultural planning. There are two types of the classification. First is pixel based and the other is object based classification method. While pixel based classification methods are based on the information in each pixel, object based classification method is based on objects or image objects that formed by the combination of information from a set of similar pixels. Multispectral image contains a higher degree of spectral resolution than a panchromatic image. Panchromatic image have a higher spatial resolution than a multispectral image. Pan sharpening is a process of merging high spatial resolution panchromatic and high spectral resolution multispectral imagery to create a single high resolution color image. The aim of the study was to compare the potential classification accuracy provided by pan sharpened image. In this study, SPOT 5 image was used dated April 2013. 5m panchromatic image and 10m multispectral image are pan sharpened. Four different classification methods were investigated: maximum likelihood, decision tree, support vector machine at the pixel level and object based classification methods. SPOT 5 pan sharpened image was used to classification sun flowers and corn in a study site located at Kadirli region on Osmaniye in Turkey. The effects of pan sharpened image on classification results were also examined. Accuracy assessment showed that the object based classification resulted in the better overall accuracy values than the others. The results that indicate that these classification methods can be used for identifying sun flower and corn and estimating crop areas.

On-Orbit Modulation Transfer Function Detection of High Resolution Optical Satellite Sensor Based on Reflected Point Sources

2017 ◽  
Vol 37 (7) ◽  
pp. 0728001 ◽  
Author(s):  
徐伟伟 Xu Weiwei ◽  
张黎明 Zhang Liming ◽  
司孝龙 Si Xiaolong ◽  
李 鑫 Li Xin ◽  
杨宝云 Yang Baoyun ◽  
...  
Keyword(s):  
High Resolution ◽  
Transfer Function ◽  
Modulation Transfer Function ◽  
Point Sources ◽  
Modulation Transfer ◽  
Satellite Sensor
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