High Spatial Resolution Satellite Images Research Articles

Radiative transfer model for simulating high-resolution satellite images

A simulator of high spatial resolution satellite images is introduced. It is based on the coupling of two radiative transfer models: discrete anisotropic radiative transfer (DART) for terrestrial landscapes and second simulation of the satellite signal in the solar spectrum (6S) for the atmosphere. It works in the visible, near infrared, and midinfrared domains. The simulation procedure involves four steps: 1) assessment of the optical properties of the atmosphere, 2) simulation of high-resolution reflectance images of the Earth surface, 3) transformation of the bottom of the atmosphere (BOA) images to top of the atmosphere (TOA) images, and 4) convolution of the TOA spectral images with the sensor spectral response. Two applications of the simulator are presented: the detection of targets and the analysis of the spatial information. This simulator is being used as a radiative transfer reference model at the Centre National d'Etudes Spatiales (CNES), Toulouse, France.

時系列低空間分解能画像からの情報を活用した複数シーンの高空間分解能画像分類

Elaborate processes are required for land cover classification using multiscene high spatial resolution satellite images, like selecting training area on each scene with sufficient a priori knowledge. The classification method proposed in this paper is assumed to use both high spatial resolution images and temporal low spatial resolution images. It can automatically produce training data set on each scene, optimized considering land cover characteristics to the scene. Moreover, it prevents from deteriorating into low classification accuracy, by referring to and checking consistency to the class candidate information derived from temporal low spatial resolution images. Experiments were conducted that used twenty-eight scenes of Landsat TM and NOAA AVHRR images as high and low spatial resolution images, respectively. Validation results by using three visually interpreted images demonstrate the optimization of training data set improved the classification accuracy from 59.0% to 66.2%, and the class candidate information improved from 61.9% to 66.2%.

Optimisation of building detection in satellite images by combining multispectral classification and texture filtering

Conventional multispectral classification methods show poor performance with respect to detection of urban object classes, such as buildings, in high spatial resolution satellite images. This is because objects in urban areas are very complicated with respect to both their spectral and spatial characteristics. Multispectral classification detects object classes only according to the spectral information of the individual pixels, while a large amount of spatial information is neglected. In this study, a technique is described which attempts to detect urban buildings in two stages. The first stage is a conventional multispectral classification. In the second stage, the classification of buildings is improved by means of their spatial information through a modified co-occurrence matrix based filtering. The direction dependence of the co-occurrence matrix is utilised in the filtering process. The method has been tested by using TM and SPOT Pan merged data for the whole area of the city of Shanghai, China. After the co-occurrence matrix based filtering, the average user accuracy increased by about 46% and the average Kappa statistic by about 57%. This result is about 26% better than the accuracy improvement through normal texture filtering. The method presented in this study is very useful for a rapid estimation of urban building and city development, especially in metropolitan areas of developing countries.

湿原植生分類図作成に有効となる観測時期の選択

Recent study has pointed out that the wetlands are the major emission source of methane, which is one of the green house gases. To produce the wetland vegetation distribution map with high spatial resolution satellite images are urgent necessity for estimating the amount of methane emission. As an initial classification step, optimal observing seasons and combinations must be selected to improve the accuracy of classification and reducing the data volume. In this study, effective combinations of observing season and band number were selected to produce a wetland distribution map with multispectral and panchromatic satellite sensor data. In the process of determine, test data were classified by maximum likelihood method by changing every combinations of spectral bands from every 6 season's and combined two seasonal data. The best classification accuracy was attained by using 10 bands of combined TM data from July and August which are middle and final growth stages of wetland vegetation.

Forest classification of Southeast Asia using NOAA AVHRR data

Methodologies using the 1 km resolution data of the NOAA AVHRR instrument were developed for tropical forest spectral discrimination and mapping at a regional scale. Tropical Southeast Asia was selected as a case study using a multitemporal AVHRR data set of 1990–1992. This study documents first the relevance of AVHRR data to assess the extent of seasonal and dense forest and, moreover, reports on the derivation of a specific fragmented/disturbed forest class. A geographically dependent methodology is developed: For continental Southeast Asia, where generally good cloud-free images are available during the dry season and seasonal vegetation formations are present, multitemporal AVHRR mosaics were produced before the classification process. For insular Southeast Asia, which is particularly affected by the cloud cover and where only humid vegetation formations are present, a multitemporal set of single-date AVHRR images was first classified, and then the classifications were mosaicked together using a combination of two criteria (image quality and maximum occurrence). Unsupervised classifications using NDVI and Channel 3 radiance were processed in both cases. The classifications were labeled using the spectral typology derived from the spectral analysis of the dataset. For evaluation of the labeled classifications, comparisons were made at a regional level with a digital forest map database. Verification of the AVHRR class assignment was carried out locally using a few high spatial resolution satellite images. It highlights the sources of misclassification.

Methods of remote sensing in geothermal regions: the geodynamic setting of the Taupo Volcanic Zone (North Island, New Zealand)

Abstract We have tested a method utilizing Landsat-TM and SPOT high resolution images to study a geodynamically active zone characterised by major geothermal activity. Analysis of the imagery permits better understanding of the patterns of spatial distribution of the geothermal fields of the Taupo Volcanic Zone (TVZ) in which some en echelon structures are linked to major crustal faults resulting from subduction. Automatic classification techniques have been developed as well as a specific study of the thermal spectral region (Landsat-TM band 6) which, despite poor spatial resolution (120m), provides information about geothermal activity at the local scale. The study reveals the value of Landsat-TM band 5 (middle reflective IR, 1·55urn- 1·75 pm), notably by the 5,4, I (red, green, blue) combination which identifies vegetation associations in geothermal areas. In addition, the images assisted mapping of volcanic deposits and structures associated with the Tarawera Rift which formed in 1886. Based on our analysis of high spatial resolution satellite images, this appears likely to be a key technique to help understanding deformation in this region.

An overview of the results of the FIFE-87 and FIFE-89 campaigns

Results of a major land surface-atmosphere exchange study, the First ISLSCP Field Experiment (FIFE) are summarized. Findings result from nearly 80 days of field measurements over a 15 kilometer square area involving 30 science teams, seven remote sensing aircraft, and 5 satellites which acquired over 1200 low resolution and 35 high spatial resolution satellite images, maps of remotely sensed temperature, vegetation index and soil moisture, surface flux measurements from more than 20 ground stations and airborne sensors, and transects of vegetation, soil moisture and soil chemistry. Relationships between in-situ, airborne, and spaceborne remote measurements and the problem of scaling will be discussed. Energy balance comparisons among sites were made. Diurnal measurements of latent heat fluxes indicated a strong correlation between the evaporative fraction at midday and the daytime average value.