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Liu P.,CAS Institute of Remote Sensing | Eom K.B.,George Washington University
Optics and Lasers in Engineering

In this paper, the restoration of a degraded image with an auxiliary image from another sensor is considered. In a typical multispectral satellite imaging system, multiple images from different sensors of the same area are available. When one of those images in a multiple image set is degraded, another image in the set can be used as a prior image for restoration. A hybrid algorithm based on the total variation approach using an auxiliary image is proposed in this paper. In this approach, the cost function for regularization has two terms: error from the degraded image being restored and the error from the auxiliary image. The amount of prior information from the auxiliary image to be used in the hybrid algorithm is determined based on the similarity between the auxiliary image and the degraded image. An algorithm, based on normalized local mutual information, is developed to estimate the amount of prior information to apply. The proposed algorithm is applied to both simulated and real multispectral images, and the performance of the proposed algorithm is compared with those of other image restoration algorithms. In both quantitative and qualitative comparisons, the proposed algorithm performed better than other algorithms. © 2013 Elsevier Ltd. Source

Li X.-M.,CAS Institute of Remote Sensing
Geophysical Research Letters

A half-century ago, it was recorded that ocean swells can propagate up to halfway around the globe. However, from a global perspective, how ocean swells propagate in the global oceans has yet to be depicted. To date, synthetic aperture radar (SAR) is the only available remote sensing instrument to measure the two-dimensional information of ocean surface waves. Here a 10 year (2002–2012) global wave data set of the spaceborne advanced SAR on board the European Space Agency's satellite Envisat and the global wind data set of the WindSat were used to (1) depict the propagation routes of ocean swells in the global oceans, (2) identify four distinguished crossing swell “pools,” and (3) interpret how these pools are formed. Together, these findings yield a new insight into ocean swells propagation and the consequent occurrence of crossing swells on a global ocean scale from space, which will further deepen our understanding of nature of ocean. ©2016. American Geophysical Union. All Rights Reserved. Source

Agency: Cordis | Branch: FP7 | Program: CP | Phase: ENV.2013.6.2-6 | Award Amount: 11.20M | Year: 2013

By 2050, global agricultural productivity will need to increase with at least 70%. In order to guarantee food production for future generations, agricultural production will need to be based on sustainable land management practises. At present, earth observation based (global) crop monitoring systems focus mostly on short-term agricultural forecasts, thereby neglecting longer term environmental effects. However, it is well known that unsustainable cultivation practises may lead to a degradation of the (broader) environment resulting in lower agricultural productivity. As such, agricultural monitoring systems need to be complemented with methods to also assess environmental impacts of change in crop land and shifting cultivation practises. It is thereby important that this is addressed at the global level. SIGMA presents a global partnership of expert institutes in agricultural monitoring, with a strong involvement in GEO and the Global Agricultural Geo-Monitoring (GEO-GLAM) initiative. SIGMA aims to develop innovative methods, based upon the integration of in-situ and earth observation data, to enable the prediction of the impact of crop production on ecosystems and natural resources. The proposed project will address methods to: i. enable sharing and integration of satellite and in situ observations according to GEOSS Data CORE principles; ii. assess the impact of cropland areas and crop land change on other ecosystems; iii. understand and assess shifts in cultivation practises and cropping systems to evaluate impacts on biodiversity and the environment. Furthermore, dedicated capacity building activities are planned to increase national and international capacity to enable sustainable management of agriculture. Lastly, a strong coordinating mechanism will be put in place, through the project partners, between SIGMA and the G20 Global Agricultural Geo-Monitoring Initiative (GEOGLAM), in order to assure transparency and alignment of the SIGMA activities.

Tong Q.,CAS Institute of Remote Sensing | Xue Y.,CAS Shanghai Institute of Technical Physics | Zhang L.,CAS Institute of Remote Sensing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

This paper reviews progress in hyperspectral remote sensing (HRS) in China, focusing on the past three decades. China has made great achievements since starting in this promising field in the early 1980s. A series of advanced hyperspectral imaging systems ranging from ground to airborne and satellite platforms have been designed, built, and operated. These include the field imaging spectrometer system (FISS), the Modular Airborne Imaging Spectrometer (MAIS), and the Chang'E-I Interferometer Spectrometer (IIM). In addition to developing sensors, Chinese scientists have proposed various novel image processing techniques. Applications of hyperspectral imaging in China have been also performed including mineral exploration in the Qilian Mountains and oil exploration in Xinjiang province. To promote the development of HRS, many generic and professional software tools have been developed. These tools such as the Hyperspectral Image Processing and Analysis System (HIPAS) incorporate a number of special algorithms and features designed to take advantage of the wealth of information contained in HRS data, allowing them to meet the demands of both common users and researchers in the scientific community. © 2013 IEEE. Source

Huo L.-Z.,CAS Institute of Remote Sensing | Tang P.,CAS Institute of Remote Sensing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

In this paper, a region-partitioning active learning (AL) technique is proposed for classification of remote sensing (RS) images based on the support vector machines (SVM) classifier. In the batch-mode AL process, diversity information is required to select a batch of informative samples. A new AL technique that aims to introduce diversity information is proposed based on relative positions of candidate samples in the feature space. The proposed technique selects informative samples according to an uncertainty criterion at each iteration. These samples are selected with an extra constraint to guarantee that they are not located in the same region of the feature space. The proposed technique is compared with state-of-the-art methods adopted in the RS community. Experimental tests were performed on three data sets, including one very high spatial resolution multispectral data set and two hyperspectral data sets. The proposed algorithm displays a classification performance that is similar to or even better than the state-of-the-art methods. In addition, the proposed algorithm performs efficiently in terms of computational time. © 2008-2012 IEEE. Source

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