Hassan Fallah-Adl

An operational atmospheric correction algorithm for Landsat Thematic Mapper imagery over the land

An operational atmospheric correction algorithm for Thematic Mapper (TM) imagery has been developed for both sequential and parallel computer environments considering both aerosol and molecular scattering and absorption. The aerosol optical depth is estimated from the image itself using the dark object approach on a moving-window basis, and the surface reflectance is then retrieved by searching lookup tables that are created using a numerical radiative transfer code. The dark object pixels are identified and their surface reflectance estimated using TM channel 7 (2.1 μm). A variety of techniques are employed to improve computational efficiency. This method is validated by measured aerosol optical depth and extensive visual evaluations accompanied by statistical analysis. Results indicate that the approach is highly stable and useful for both qualitative imagery interpretation (haze removal) and quantitative analysis. Future research activities are also highlighted. The computer codes are available to the general scientific community.

Fast algorithms for removing atmospheric effects from satellite images

The varied features of the earths surface each reflect sunlight and other wavelengths of solar radiation in a highly specific way. This principle provides the foundation for the science of satellite based remote sensing. A vexing problem confronting remote sensing researchers, however, is that the reflected radiation observed from remote locations is significantly contaminated by atmospheric particles. These aerosols and molecules scatter and absorb the solar photons reflected by the surface in such a way that only part of the surface radiation can be detected by a sensor. The article discusses the removal of atmospheric effects due to scattering and absorption, ie., atmospheric correction. Atmospheric correction algorithms basically consist of two major steps. First, the optical characteristics of the atmosphere are estimated. Various quantities related to the atmospheric correction can then be computed by radiative transfer algorithms, given the atmospheric optical properties. Second, the remotely sensed imagery is corrected by inversion procedures that derive the surface reflectance. We focus on the second step, describing our work on improving the computational efficiency of the existing atmospheric correction algorithms. We discuss a known atmospheric correction algorithm and then introduce a substantially more efficient version which we have devised. We have also developed a parallel implementation of our algorithm.

High performance computing algorithms for land cover dynamics using remote sensing data.

Global and regional land cover studies need to apply complex models on selected subsets of large volumes of multi-sensor and multi-temporal data sets that have been derived from raw instrument measurements using widely accepted pre-processing algorithms. The computational and storage requirements of most of these studies far exceed what is possible on a single workstation environment. We have been pursuing a new approach that couples scalable and open distributed heterogeneous hardware with the development of high performance software for processing, indexing and organizing remotely sensed data. Hierarchical data management tools are used to ingest raw data, create metadata and organize the archived data so as to automatically achieve computational load balancing among the available nodes and minimize input/output overheads. We illustrate our approach with four specific examples. The first is the development of the first fast operational scheme for the atmospheric correction of Landsat Thematic Mapper scenes, while the second example focuses on image segmentation using a novel hierarchical connected components algorithm. Retrieval of the global Bidirectional Reflectance Distribution Function in the red and near-infrared wavelengths using four years (1983 to 1986) of Pathfinder Advanced Very High Resolution Radiometer (AVHRR) Land data is the focus of our third example. The fourth example is the development of a hierarchical data organization scheme that allows on-demand processing and retrieval of regional and global AVHRR data sets. Our results show that substantial reductions in computational times can be achieved by the high performance computing technology.

Fast algorithms for estimating aerosol optical depth and correcting Thematic Mapper imagery

Remotely sensed images collected by satellites are usually contaminated by the effects of atmospheric particles through the absorption and scattering of radiation from the earths surface. The objective of atmospheric correction is to retrieve the surface reflectance from remotely sensed imagery by removing the atmospheric effects, which is usually performed in two steps. First, the optical characteristics of the atmosphere are estimated and then the remotely sensed imagery is corrected by inversion procedures that derive the surface reflectance. In this paper we introduce an efficient algorithm to estimate the optical characteristics of the Thematic Mapper imagery and to remove the atmospheric effects from it. Our algorithm introduces a set of techniques to significantly improve the quality of the retrieved images. We pay particular attention to the computational efficiency of the algorithm, thereby allowing us to correct large TM images quickly. We also provide a parallel implementation of our algorithm and show its portability and scalability on three parallel machines.

Efficient Algorithms for Atmospheric Correction of Remotely Sensed Data

Remotely sensed imagery has been used for developing and validating various studies regarding land cover dynamics. However, the large amounts of imagery collected by the satellites are largely contaminated by the effects of atmospheric particles. The objective of atmospheric correction is to retrieve the surface reflectance from remotely sensed imagery by removing the atmospheric effects. We introduce a number of computational techniques that lead to a substantial speedup of an atmospheric correction algorithm based on using look-up tables. Excluding I/O time, the previous known implementation processes one pixel at a time and requires about 2.63 seconds per pixel on a SPARC-10 machine, while our implementation is based on processing the whole image and takes about 4-20 microseconds per pixel on the same machine. We also develop a parallel version of our algorithm that is scalable in terms of both computation and I/O. Experimental results obtained show that a Thematic Mapper (TM) image (36 MB per band, 5 bands need to be corrected) can be handled in less than 4.3 minutes on a 32-node CM-5 machine, including I/O time.

Efficient algorithms for estimating atmospheric parameters for surface reflectance retrieval

The objective of atmospheric correction is to retrieve the surface reflectance from remotely sensed imagery by removing the atmospheric effects. We introduce an efficient algorithm to estimate the optical characteristics of the TM imagery and to remove the atmospheric effects from it. Our algorithm introduces a set of techniques to significantly improve the quality of the retrieved images. We pay a particular attention to the computational efficiency of the algorithm thereby allowing us to correct large TM images quite fast. We also provide a parallel implementation of our algorithm and show its portability and scalability on several parallel machines.