k-NN Based Bypass Entropy and Mutual Information Estimation for Incremental Remote-Sensing Image Compressibility Evaluation

Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still to be evaluated quantitatively for effi cient compression scheme designing. In this paper, we present a k-nearest neighbor(k-NN) based bypass image entropy estimation scheme, together with the corresponding mutual information estimation method. Firstly, we apply the k-NN entropy estimation theory to split image blocks, describing block-wise intra-frame spatial correlation while avoiding the curse of dimensionality. Secondly, we propose the corresponding mutual information estimator based on feature-based image calibration and straight-forward correlation enhancement. The estimator is designed to evaluate the compression performance gain of using priori information. Numerical results on natural and remote-sensing images show that the proposed scheme obtains an estimation accuracy gain by 10% compared with conventional image entropy estimators. Furthermore, experimental results demonstrate both the effectiveness of the proposed mutual information evaluation scheme, and the quantitative incremental compressibility by using the priori remote-sensing frames.

Robustness Evaluation of Remote-Sensing Image Feature Detectors with TH Priori-Information Data Set

In this paper,we build a remote-sensing satellite imagery priori-information data set,and propose an approach to evaluate the robustness of remote-sensing image feature detectors.The building TH Priori-Information(TPI)data set with 2297 remote sensing images serves as a standardized high-resolution data set for studies related to remote-sensing image features.The TPI contains 1)raw and calibrated remote-sensing images with high spatial and temporal resolutions(up to 2 m and 7 days,respectively),and 2)a built-in 3-D target area model that supports view position,view angle,lighting,shadowing,and other transformations.Based on TPI,we further present a quantized approach,including the feature recurrence rate,the feature match score,and the weighted feature robustness score,to evaluate the robustness of remote-sensing image feature detectors.The quantized approach gives general and objective assessments of the robustness of feature detectors under complex remote-sensing circumstances.Three remote-sensing image feature detectors,including scale-invariant feature transform(SIFT),speeded up robust features(SURF),and priori information based robust features(PIRF),are evaluated using the proposed approach on the TPI data set.Experimental results show that the robustness of PIRF outperforms others by over 6.2%.

A New Method of Profiling Remote-Sensing Reflectance of Coastal Waters

The profiling method is the first method to select in measuring the remote-sensing reflectance. In the light of the characteristics of China’s coastal waters, we develop a new method to compensate the environment’s effects with the downwelling (λ)rsi r radiance’s profile and to estimate the underwater remote-sensing reflectance r. (λ)rsT he result indicates that the relative deviation of repetitious r in one station is around 10 %.

Shadow Detection Method Based on HMRF with Soft Edges for High-Resolution Remote-Sensing Images

Shadow detection is a crucial task in high-resolution remote-sensing image processing. Various shadow detection methods have been explored during the last decades. These methods did improve the detection accuracy but are still not robust enough to get satisfactory results for failing to extract enough information from the original images. To take full advantage of various features of shadows, a new method combining edges information with the spectral and spatial information is proposed in this paper. As known, edge is one of the most important characteristics in the high-resolution remote-sensing images. Unfortunately, in shadow detection, it is a high-risk strategy to determine whether a pixel is the edge or not strictly because intensity values on shadow boundaries are always between those in shadow and non-shadow areas. Therefore, a soft edge description model is developed to describe the degree of each pixel belonging to the edges or not. Sequentially, the soft edge description is incorporating to a fuzzy clustering procedure based on HMRF (Hidden Markov Random Fields), in which more appropriate spatial contextual information can be used. More concretely, it consists of two components: the soft edge description model and an iterative shadow detection algorithm. Experiments on several remote sensing images have shown that the proposed method can obtain more accurate shadow detection results.

REMOTE-SENSING TECHNOLOGY IN FLOOD CONTROL IN CHINA

This article expounds the vital role undertaken by telemetry in monitoring and assessment in flood control in China. By analyzing the data from their surveys, the authors give an all-round exposition of problems in the country’s drive for disaster reduction and propose countermeasures in the fight against the possible flood problems in the future.

The Application of Satellite Remote-sensing for Railway Line Selection

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Intercomparison of NO_x, SO_2, O_3, and Aromatic Hydrocarbons Measured by a Commercial DOAS System and Traditional Point Monitoring Techniques

A field-based Intercomparison study of a commercial Differential Optical Absorption Spectroscopy (DOAS) instrument (OPSIS AB, Sweden) and different point-sample monitoring techniques (PM, based on an air monitoring station, an air monitoring vehicle, and various chemical methods) was conducted in Beijing from October 1999 to January 2000. The mixing ratios of six trace gases including NO, NO2, SO2, O3, benzene, and toluene were monitored continuously during the four months. A good agreement between the DOAS and PM data was found for NO2 and SO2. However, the concentrations of benzene, toluene, and NO obtained by DOAS were significantly lower than those measured by the point monitors. The ozone levels monitored by the DOAS were generally higher than those measured by point monitors. These results may be attributed to a strong vertical gradient of the NO-O3-NO2 system and of the aromatics at the measurement site. Since the exact data evaluation algorithm is not revealed by the manufacturer of the DOAS system, the error in the DOAS analysis can also not be excluded.

Effects of climate change on potential habitats of the cold temperate coniferous forest in Yunnan province, southwestern China

We built a classification tree （CT） model to estimate climatic factors controlling the cold temperate coniferous forest （CTCF） distributions in Yunnan province and to predict its potential habitats under the current and future climates, using seven climate change scenarios, projected over the years of 2070-2099. The accurate CT model on CTCFs showed that minimum temperature of coldest month （TMW） was the overwhelmingly potent factor among the six climate variables. The areas of TMW〈-4.05 were suitable habitats of CTCF, and the areas of -1.35 〈 TMW were non-habitats, where temperate conifer and broad-leaved mixed forests （TCBLFs） were distribute in lower elevation, bordering on the CTCF. Dominant species of Abies, Picea, and Larix in the CTCFs, are more tolerant to winter coldness than Tsuga and broad-leaved trees including deciduous broad-leaved Acer and Betula, evergreen broad- leaved Cyclobalanopsis and Lithocarpus in TCBLFs. Winter coldness may actually limit the cool-side distributions of TCBLFs in the areas between -1.35℃ and -4.05℃, and the warm-side distributions of CTCFs may be controlled by competition to the species of TCBLFs. Under future climate scenarios, the vulnerable area, where current potential （suitable ＋ marginal） habitats （80,749 km^2） shift to non-habitats, was predicted to decrease to 55.91% （45,053 km^2） of the current area. Inferring from the current vegetation distribution pattern, TCBLFs will replace declining CTCFs. Vulnerable areas predicted by models are important in determining priority of ecosystem conservation.

Attitudes towards acceptance of an innovative home-based and remote sensing rehabilitation protocol among cardiovascular patients in Shantou, China

Background Cardiac rehabilitation （CR） protocols have diversified to include home-based cardiac tele-rehabilitation （HBCTR） as an alternative to hospital-based or center-based CR. To adopt the use of home-based cardiac tele-rehabilitation, it is necessary to assess cardiac patients＇ attitudes towards acceptance of such e-health technology, especially in China where knowledge of such technology is deficient. Methods Interviews were conducted in the First Affiliated Hospital of Shantou University Medical College, Shantou, China. After percutaneous coronary interventional （PCI） surgery, patients completed the survey. Results Among the 150 patients, only 13% had ever heard of HBCTR. After an introduction of our HBCTR program, 60% of patients were willing to participate in the program. From our multivariate analysis of questionnaire data, age （OR： 0.92, 95% CI： 0.864）.98; P = 0.007）, average family monthly income （OR： 0.13, 95% CI： 0.05-0.341 P 〈 0.001）, education level （OR： 0.24, 95% CI： 0.10-0.59; P = 0.002） and physical exercise time （OR： 0.19, 95% CI： 0.06-0.56; P = 0.003） were independent predictors for acceptance of HBCTR. From the reasons for participation, patients selected： enhanced safety and independ ence （28.3%）, ability to self-monitor physical conditions daily （25 A%）, and having automatic and emergency alert （23.1%）. Reasons for refusal were： too cumbersome operation （34.3%） and unnecessary protocol （19.4%）. Conclusions Most patients lacked knowledge about HBCTR but volunteered to participate after they have learned about the program. Several personal and life-style factors influenced their acceptance of the program. These indicate that both improvement of technology and better understanding of the program will enhance active participation.

Suspended sediment concentration mapping based on the MODIS satellite imagery in the East China inland, estuarine, and coastal waters

The purpose of this research is to improve the retrieval accuracy for the suspended sediment concentration(SSC) from in situ and satellite remote sensing measurements in turbid East China estuarine and coastal waters. For this aim, three important tasks are formulated and solved: 1) an estimation of remote-sensing reflectance spectra R_(rs)(λ) after atmospheric correction; 2) an estimation of R_(rs)(λ) from the radiometric signals above the air-water surface; and 3) an estimation of SSC from R_(rs)(λ). Six different models for radiometric R_(rs)(λ) determination and 28 models for SSC versus R_(rs)(λ) are analyzed based on the field observations made in the Changjiang River estuary and its adjacent coastal area. The SSC images based on the above-mentioned analysis are generated for the area.

Detecting Aerosols over Land from Satellites by Measuring Far IR Radiation from the Earth-Atmospheric System

In this study, a special method is proposed for detecting aerosols over land by analyzing satellitemeasured far IR radiation at three channels, 8.7, 11.5, and 12.5 lain. Sensitivity tests revealed that the behavioral features of radiative brightness temperatures （BTs） at these three channels with increasing optical depth are different among different types of aerosols. Analyzing how BTs and their differences, xBT （BT11.5-BT12.5） and YBT （BT8.7-BT11.5）, behave with varying optical depth can help to qualitatively distinguish among aerosols and semi-quantitatively estimate their optical thicknesses. In addition, the authors found that the vertical density profile of aerosols has little impact on this method.

Development and distribution of geohazards triggered by the 5.12 Wenchuan Earthquake in China

As the Wenchuan Earthquake was of high magnitude and shallow seismic focus, it caused great dam- age and serious geohazards. By the field investigation and the interpretation of remote-sensing infor- mation after the earthquake and by using means of GIS technology, the distribution of geohazards triggered by the earthquake are analyzed and the conclusions are as follows: (1) The earthquake geo- hazards showed the feature of zonal distribution along the earthquake fault zone and linear distribution along the rivers; (2) the distribution of earthquake geohazards had a marked hanging wall effect, for the development density of geohazards in the hanging wall of earthquake fault zone was obviously higher than that in the foot wall and the width of strong development zone in the hanging wall was about 10 km; (3) the topographical slope was a main factor which controlled the development of earthquake geo- hazards and a vast majority of hazards were distributed on the slopes of 20° to 50°; (4) the earthquake geohazards had a corresponding relationship with the elevation and micro-landform, for most hazards happened in the river valleys and canyon sections below the elevation of 1500 to 2000 m, particularly in the upper segment of canyon sections (namely, the turning point from the dale to the canyon). Thin ridge, isolated or full-face space mountains were most sensitive to the seismic wave, and had a striking amplifying effect. In these areas, collapses and landslides were most likely to develop; (5) the study also showed that different lithologies determined the types of geohazards, and usually, landslides oc- curred in soft rocks, while collapses occurred in hard rocks.

On the Relationship between Local Topography and Small Glacier Change under Climatic Warming on Mt. Bogda, Eastern Tian Shan, China

Glacial features in the geological record provide essential clues about past behavior of climate. Of the numerous physical systems on earth, glaciers are one of most responsive to climate change, especially small glaciers, their direct marginal response taking only a few years or decades to be expressed. Accelerating recession of modern glaciers raises the issue of the climate＇s impact on water runoff. Data based on topographic maps and Advanced Spaceborne Thermal Emission and Radiometer （ASTER） imagery show the trends that are highly variable over time and within the region. An analysis of the local topographic settings of very small （〈0.5 km2） glaciers was conducted to investigate their influence on recent changes in these glaciers. Among 137 glaciers, 12 disappeared completely. The study reveals that glaciers situated in favorable locations had tiny relative area reduction, while those in less favorable settings generally had large area loss or even disappeared. It is suggested that most of the small glaciers studied have retreated as far as they are likely to under the climatic conditions of the late 20th century. Undoubtedly, the strong retreating of small glaciers exerts adverse effects on the hydro- logic cycle and local socioeconomic development.

ESTIMATION OF ARTIFICIAL PRECIPITATION ENHANCEMENT RESOURCE CONDITION AND CLOUD SEEDING POTENTIAL BY GROUND-BASED REMOTE SENSING DATA

In this paper,the data of continuous atmospheric vertical integral vapour and liquid water content during April-June of 1992-1994 obtained by a ground-based dual-channel microwave radiometer are used to analyse the statistical characteristics of atmospheric vapour and liquid water content,and the relative distribution characteristics of vapour and liquid water content in cloudy atmosphere,the correlative relation of integral liquid water content L and ground precipitation intensity I.and precipitation transform rate of precipitation system.Finally,the weather modification condition of precipitus stratiform clouds and seeding potential is analyzed and discussed.

LuoJiaAI:A cloud-based artificial intelligence platform for remote sensing image interpretation

The rapid processing,analysis,and mining of remote-sensing big data based on intelligent interpretation technology using remote-sensing cloud computing platforms(RS-CCPs)have recently become a new trend.The existing RS-CCPs mainly focus on developing and optimizing high-performance data storage and intelligent computing for common visual representation,which ignores remote sensing data characteristics such as large image size,large-scale change,multiple data channels,and geographic knowledge embedding,thus impairing computational efficiency and accuracy.We construct a LuoJiaAI platform composed of a standard large-scale sample database(LuoJiaSET)and a dedicated deep learning framework(LuoJiaNET)to achieve state-of-the-art performance on five typical remote sensing interpretation tasks,including scene classification,object detection,land-use classification,change detection,and multi-view 3D reconstruction.The details of the LuoJiaAI application experiment can be found at the white paper for LuoJiaAI industrial application.In addition,LuoJiaAI is an open-source RS-CCP that supports the latest Open Geospatial Consortium(OGC)standards for better developing and sharing Earth Artificial Intelligence(AI)algorithms and products on benchmark datasets.LuoJiaAI narrows the gap between the sample database and deep learning frameworks through a user-friendly data-framework collaboration mechanism,showing great potential in high-precision remote sensing mapping applications.

An airborne microwave radiometer and measurements of cloud liquid water

A single-channel (9.5 mm) airborne microwaveradiometer with one antenna is developed. The retrievalmethods and primary observation results of cloud liquidwater and super-cooled cloud liquid water are discussed. Theaircraft experiments show that the cloud liquid water andsuper-cooled liquid water can be sensitively monitored atsome level of accuracy by the radiometer. The results ofcloud liquid water content are reasonable and correspondwell with the surface radar echo intensity. The design of theairborne radiometer and its retrieval methods are feasible,giving it application value.