Coastline detection in Synthetic Aperture Radar (SAR) images plays a significant role in various sorts of marine application such as autonomous navigation and geographic mapping. Several methods used in recent years for applying SAR imagery to coastline detection and edge detection are reviewed, summarized and compared with each other in this paper. In addition, future research and development trend is predicted.

 Due to inevitable manufacture defects, there exist a lot of malfunction pixels in the CCD image. The traditional detecting method must require a series of sample images for standard objects taken under a specific condition. Starting with an analysis of electro-optical response characteristics of the CCD pixel, this paper classifies the detected malfunction pixels and makes a statistical analysis on a series of images taken in different scenes. On such a basis, the defective pixels are detected and corrected. A study of a series of aerial CCD images has proved the efficiency of this new method.

This paper presents a neural network method for retrieving wind vectors from ERS-1/2 scatterometer data, which resolves wind directional ambiguities for scatterometer derived winds by a circular median filter algorithm. Learning data set and test data set come from ERS-1/2 scatterometer data collocated pairs with ECMWF vectors. A comparison with COMD4 and ECMWF wind vector shows that the result is good and the performance is quicker than any other methods. The good performance of the neural network method suggests the possibility of wind retrieval from ERS-1/2 scatterometer.

 The size of filtering window has obvious impact on the effect of SAR image filtering. Different sizes of filtering windows, from 3×3, 5×5, 7×7, to 9×9 (pixel×pixel),  were used in some useful filtering methods such as Kuan algorithm, Frost and Enhanced Frost algorithm, Lee and Enhanced Lee algorithm. Several cases of filtering performance were compared and studied based on different indexes. It is shown that the filtering window with 5 pixel ×5 pixel can yield better performance.

 A new method for extracting mineralization information from remote sensing image based on Support Vector Machines (SVM) is presented in this paper. According to the field measured spectral data of mineralized alteration rocks and wall rocks, the authors first extracted the training examples by Spectral Angle Mapper (SAM), and then selected the RBF as the kernel function. After that, cross-validation algorithm was applied to seek superior SVM model parameters. This model was used to extract mineralization information from remote sensing image in Mangya area, Qinghai province. Practice has proved that this method is effective in extracting mineralization information.

The measurement and accurate visualization of the value and spatial distribution of uncertainty in remotely sensed image make up one of the key problems in the field of remote sensing. In the traditional fashions, e.g., in error matrix, the measurements based on the training data are regarded as the measures of the overall accuracy of classification models. Nevertheless, we need to estimate their performance on “out-of-sample-data” - data that have not been used in constructing the models. In this paper, the authors propose a strategy for calculating and visualizing attribute uncertainty of the classified remotely sensed imagery. With the information theory and the rough sets theory, three types of indices for measuring the attribute uncertainty of remotely sensed imagery based on pixel, object and image have been proposed. These measurements could measure effectively the attribute uncertainty and trace error as well as the propagation of uncertainty in classified remotely sensed data. In addition, corresponding visualizing fashions in different types of measurements are described.

 The accurate estimation of foliar chlorophyll concentration contributes to our need for further understanding of forest ecosystem. Two approaches, namely, indices and partial lease squares regression, were used to retrieve foliar chlorophyll concentration, both of which are based on the spectra and chemical variables of main tree species in Qianyanzhou forest. Common hyperspectral indices were used to set up the regression model, and a new index, Chlorophyll Absorption Area Index (CAAI), was created. The results show that the indices TCARI and CAAI have stronger ability for predicting chlorophyll concentration, and the predicted model constructed by the PLS method can be used to explain the capability of these indices.

The unmanned aerial vehicles remote sensing data compression and decompression system (UAVRSDCDS) is a key component of the UAV aerial remote sensing data transfer system. Based on the design of the data transfer, this paper puts forward three aerial remote sensing data transfer and compression schemes. The principle and implementation of data compression are described, and the key techniques in the development of the embedded remote sensing data compression system are studied. The remote sensing image compression system was achieved through four key steps, i.e., automatic segmentation, DCT, quantification and Huffman coding. The compression system was realized and optimized in both hardware and software based on characteristics of DSP. The aerial remote sensing data compression system performed well in the flying experiment, and the compression ratio of remote sensing image could reach 1︰13. The flying experiment proved the correction and robustness of the system.

 A new auto-adapted feature points extraction method based on Harris operator is proposed in this paper. An iteration strategy is used to define the Harris threshold value automatically, which is then used to determine the feature points. The tiled preprocessing and neighboring point eliminating method was employed to improve the efficiency of the results. The experiments of feature points extraction of TM bands image in Hangzhou area demonstrates that the performance is highly efficient, exact and speedy.

The thermal field of urban and suburban areas of Wuhan city was derived from TM/ETM+ thermal infrared data, and the results were qualitatively analyzed. Water bodies have stable and uniform thermal field. By normalizing the thermal infrared bands (DNs of thermal infrared band divided by the mean value of water bodies), the two bands of 1988 and 2002 become comparable with each other in thermal value. With the other bands of the same data set, the basic factors affecting the thermal field, namely buildings, greenbelts and water bodies, were identified by visional interpretation or image processing. Contours of the normalized data were calculated for qualitative analysis of the basic affecting factors. The results show that the effects upon the thermal field gradually decrease in order of buildings, greenbelts and water bodies.

 Suspended sediments constitute an important component of water color, and high concentrations of suspended particulate matter in coastal waters directly affect or control numerous water column and benthic processes. In this paper, the utility of MODIS 250 m and 1 000 m data for analyzing turbid coastal waters was examined in the Yellow Sea and the East China Sea. With a set of processing procedures, the authors used MODIS images in the 250 m model and the 1 000 m model to map the concentrations of suspended sediments. The results were compared with the in-situ data set collected in April 2003. This study demonstrates that the moderately high resolution of MODIS 250 m data is useful in examining the evolutionary process of materials in coastal environments, particularly the smaller water bodies such as bays and estuaries, and that the 1000 m data are perfect in mapping the concentrations of suspended matter in open coastal waters.

Some kinds of clay minerals such as sericite and muscovite are of great significance in determining the hydrothermal alteration mechanism and the change of geological environment related to temperature and pressure. Variations of spectral features of rocks and minerals in the Hongtan gold deposit were analyzed, and minerals were identified using imaging spectroscopic data obtained from the Huangshan Cu-Ni deposit in East Tianshan Mountain of Xinjiang. This paper also discusses the extraction of geological genesis information, which is implied by laboratory mineral and rock spectra and imaging data.

 Principal Component Analysis (PCA), an indirect ordination technique, was applied to the examination of the relationships between the community characteristics of salt marsh vegetation and their associated spectral reflectance measured at ground level using a FieldSpecTm Pro JR spectroradiometer. Paired measurements of community characteristics, including environmental factors, and spectral reflectance were collected for a range of salt marsh vegetation types on the Chongming Dongtan Natural Reserve, Shanghai. In total, 53 sample quadrats were collected along three transects within the study site. The spectral data were converted to simulate a 12-channel bandset of the Compact Airborne Spectrographic Imager (CASI) instrument. The results of the PCA applied to the simulated spectral data identified the bare mudflat, scirpus mariqueter community, spartina alterniflora community and phragmites australis community. The vegetation height and cover of the salt marsh communities were found to be most significantly related to variations in spectral reflectance.

 With the utilization of remote sensing, Geographical Information System (GIS) and other analytical techniques and on the basis of AMFP advanced for regional geological hazards warning study, the authors designed the judgment matrix of Analytic Hierarchy Process (AHP) for working out the weight of assessment factor, and established the assessment model for geological hazards of the experimental region with the Comprehensive Index Model. Furthermore, by means of the geological hazard forecasting-warning system of the experimental region, the results of estimation of regional geo-hazards in Linzhi district were obtained. It is concluded that the results of this method are valuable, that the research method put forward in this paper and the construction of the geological hazard forecasting-warning system for the research region have not only theoretical but also realistic significance in the forecasting and avoiding of geological hazards, and that the quantification of human activities can reduce the area of geological hazards forecast and make prominent the influence of geological hazards on the environment of human survival.

This paper discusses the methods used for thematic information extraction in desert area with TM/ETM as the main data resource. The water body was extracted by combining density slicing of TM 5 or TM 7 with the terrain mask produced by DEM. NDVI was used to extract vegetation information, and desertification land and salification land information was extracted by the SAM classification method. Confusion matrix shows that the classification precision is 86.2%. The classification results of desertification land and salification land in different years were input into GIS to extract changing information by using spatial analysis. It is shown that from 1994 to 2000, desertification and salification land increased obviously, whereas from 2000 to 2002, desertification and salification land changed only insignificantly. Considering that the study area is located in an arid desert environment short of human activities, and that the main factors affecting landscape change are climate and hydrological conditions which may not change remarkably in a short period, the suitable monitoring period should be five years or so.

 With rapid urban growth in recent years, the understanding of urban biophysical composition and dynamics has become an important research topic. Remote sensing technologies constitute a potentially scientific basis for examining urban composition and monitoring its changes over the time. The vegetation-impervious surface-soil-water (V-I-S-W) model, in particular, provides a foundation for describing urban/suburban environments and also serves as a basis for further urban analyses comprising urban growth modeling, environmental impact analysis, and socioeconomic factor estimation. This paper developed a normalized spectral mixture analysis (NSMA) method for examining urban composition in Haizhu district using Landsat ETM+  data. In particular, a brightness normalization method was applied to reduce brightness variation. Through this normalization, brightness variability within each V-I-S-W component was reduced or eliminated, thus allowing a single end-member to represent each component. Furthermore, with the normalized image, four end-members, namely vegetation, impervious surface, soil, and water, were chosen to model heterogeneous urban composition using a constrained spectral mixture analysis (SMA) model. The accuracy of impervious surface estimation was assessed and compared with the other existing models. The results indicate that the proposed model is a better alternative to existing models, with a root mean square error (RMSE) of 12.6% for impervious surface estimation in the study area.

 Based on a comparative analysis of the application results of such data sources as ETM, SPOT－4, SPOT－5 and IKONOS in Tangshan, Hebei Province, the authors have summed up the application characteristics of different remote sensing data sources in the investigation of mining situation and environment of mines, and analyzed quantitatively the best and largest mapping scales for these data sources. In the end, the selection procedure of data sources has been presented.

SAR Interferometry (InSAR) is a new research branch in SAR remote sensing technology. In the past, InSAR application was mainly focused on acquiring DEM by means of extracting phase information preserved in RADAR backscatter. With the development of InSAR technology, importance has been increasingly attached to interferometric coherence, and its application has been extended to many fields. This paper presents a SAR image color composition model based on SAR interferometric coherence. With this model, different kinds of objects can be distinguished clearly by different colors in image, and it is proved that this technique is especially suitable for forest monitoring and land use application.

The Qingzang Plateau Environmental and Geological Information Monitoring System (TPEGIMS) has two working modes, C/S and B/S. According to uniformly spatial geography benchmark, TPEGIMS with multi-specialities, multi-layers and multi-functions was set up for such objects as modern glaciers, snow lines, rivers, lakes, geological disasters, desertification and geological structures. This paper mainly describes the structure, operation environment and system functions of TPEGIMS. Multiple functions of TPEGIMS include overlapping analysis and comparison between several images, interactive interpretation, vector editing, monitoring-result variation analysis, overall evaluation and web publishing. TPEGIMS is indeed a new, useful and powerful toolkit.