Geoscience & Remote sensingNO PRJ TITLE ABSTRACT DOMAIN YOP

1

Remote Sensing Image Fusion via

Sparse Representations Over Learned Dictionaries

Remote sensing image fusion can integrate the spatial detail of panchromatic (PAN) image and the spectral information of a low-resolution multispectral (MS) image to produce a fused MS image with high spatial resolution. In this paper, a remote sensing image fusion method is proposed with sparse representations over learned dictionaries. The dictionaries for PAN image and low-resolution MS image are learned from the source images adaptively. Furthermore, a novel strategy is designed to construct the dictionary for unknownF high-resolution MS images without training set, which can make our proposed method more practical. The sparse coefficients of the PAN image and low-resolution MS image are sought by the orthogonal matching pursuit algorithm. Then, the fused high-resolution MS image is calculated by combining the obtained sparse coefficients and the dictionary for the highresolution MS image. By comparing with six well-known methods in terms of several universal quality valuation indexes with or without references, the simulated and real experimental results on QuickBird and IKONOS images demonstrate the superiority of our method.

Geoscience & Remote

sensing2013

2

Evaluation of Spatial and

Spectral Effectiveness of

Pixel-Level Fusion

Techniques

Along with the launch of a number of very highresolution satellites in the last decade, efforts have been made toincrease the spatial resolution of the multispectral bands using the panchromatic information. Quality evaluation of pixel-fusion techniques is a fundamental issue to benchmark and to optimize different algorithms. In this letter, we present a thorough analysis of the spatial and spectral distortions produced by eight pan sharpening techniques. The study was conducted using real data from different types of land covers and also a synthetic image with different colors and spatial structures for comparison purposes. Several spectral and spatial quality indexes and visual information were considered in the analysis. Experimental results have shown that fusion methods cannot simultaneously incorporate the maximum spatial detail without degrading the spectral information. Atrous_IHS, Atrous_PCA, IHS, and eFIHS algorithms provide the best spatial–spectral tradeoff for wavelet-based and algebraic or component substitution methods. Finally, inconsistencies between some quality indicators were detected and analyzed.

Geoscience & Remote

sensing2013

3 Spatiotemporal Satellite Image Fusion Through One-Pair Image

Learning

This paper proposes a novel spatiotemporal fusion model for generating images with high-spatial and high-temporal resolution (HSHT) through learning with only one pair of prior images. For this purpose, this method establishes correspondence between low-spatial-resolution but high-temporal-resolution (LSHT) data and high-spatial-resolution but low-temporalresolution (HSLT) data through the superresolution of LSHT data and further fusion by using high-pass modulation. Specifically, this method is implemented in two stages. In the first stage, the spatial resolution of LSHT data on prior and prediction dates is improved simultaneously by means of sparse representation; in the second stage, the known HSLT and the superresolved LSHTs are fused via high-pass modulation to generate the HSHT data on the prediction date. Remarkably, this method forms a unified framework for blending remote sensing images with temporal reflectance changes, whether phenology change (e.g., seasonal change of vegetation) or land-cover-type change (e.g., conversion of farmland to built-up area) based on a two-layer spatiotemporal fusion strategy due to the large spatial resolution difference between HSLT and LSHT data. This method was tested on both a simulated data set and two actual data sets of Landsat Enhanced Thematic Mapper Plus–Moderate Resolution Imaging Spectroradiometer acquisitions. It was also compared with other well-known spatiotemporal fusion algorithms on two types of data: images primarily with phenology changes and images primarily with land-cover-type changes. Experimental results demonstrated that our method performed better in capturing surface reflectance changes on both types of images. A Sparse Image Fusion Algorithm With pplication to Pan-Sharpening Data provided by most optical Earth observation satellites such as IKONOS, QuickBird, and GeoEye are composed of a panchromatic channel of high spatial resolution (HR) and several multispectral channels at a lower spatial resolution (LR). The fusion of an HR panchromatic and the corresponding LR spectral channels is called “pan-sharpening.” It aims at obtaining an HR multispectral image. In this paper, we propose a new pan-sharpening method named Sparse Fusion of Images (SparseFI, pronounced as “sparsify”). SparseFI is based on the compressive

Geoscience & Remote

sensing

2013

#56, II Floor, Pushpagiri Complex, 17th Cross 8th Main, Opp Water Tank,Vijaynagar,Bangalore-560040.

Website: www.citlprojects.com, Email ID: projects@citlindia.com,hr@citlindia.comMOB: 9886173099 / 9986709224, PH : 080 -23208045 / 23207367

MATLAB – 2013((Image Processing, Wireless Sensor Network, Power Electronics, Signal Processing, Power System,

Communication, Wireless communication, Geoscience & Remote sensing)

sensing theory and explores the sparse representation of HR/LR multispectral image patches in the dictionary pairs cotrained from the panchromatic image and its downsampled LR version. Compared with conventional methods, it “learns” from, i.e., adapts itself to, the data and has generally better performance than existing methods. Due to the fact that the SparseFI method does not assume any spectral composition model of the panchromatic image and due to the super-resolution capabilityand robustness of sparse signal reconstruction algorithms, it gives higher spatial resolution and, in most cases, less spectral distortion compared with the conventional methods.

4

Evaluation of Spatial and

Spectral Effectiveness of

Pixel-Level Fusion

Techniques

Along with the launch of a number of very highresolution satellites in the last decade, efforts have been made to increase the spatial resolution of the multispectral bands using the panchromatic information. Quality evaluation of pixel-fusion techniques is a fundamental issue to benchmark and to optimizedifferent algorithms. In this letter, we present a thorough analysis of the spatial and spectral distortions produced by eight pan sharpening techniques. The study was conducted using real data from different types of land covers and also a synthetic image with different colors and spatial structures for comparison purposes. Several spectral and spatial quality indexes and visual information were considered in the analysis. Experimental results have shown that fusion methods cannot simultaneously incorporate the maximum spatial detail without degrading the spectral information. Atrous_IHS, Atrous_PCA, IHS, and eFIHS algorithms provide the best spatial–spectral tradeoff for wavelet-based and algebraic or component substitution methods. Finally, inconsistencies between some quality indicators were detected and analyzed.

Geoscience & Remote

sensing2013

5

Hybrid Pansharpening Algorithm for High Spatial Resolution

Satellite Imagery to Improve

Spatial Quality

Most pansharpened images from existing algorithms are apt to present a tradeoff relationship between the spectral preservation and the spatial enhancement. In this letter, we developed a hybrid pansharpening algorithm based on primary and secondary high-frequency information injection to efficiently improve the spatial quality of the pansharpened image. The injected high-frequency information in our algorithm is composed of two types of data, i.e., the difference between panchromatic and intensity images, and the Laplacian filtered image of high-frequency information. The extracted high frequencies are injected by the multispectral image using the local adaptive fusion parameter and postprocessing of the fusion parameter. In the experiments using various satellite images, our results show better spatial quality than those of other fusion algorithms while maintaining as much spectral information as possible.

Geoscience & Remote

sensing2013