Hyperspectral data compression is expected to play a crucial role in remote sensing applications. Most available approaches have largely overlooked the impact of mixed pixels and subpixel targets, which can be accurately modeled and uncovered by resorting to the wealth of spectral information provided by hyperspectral image data. In this paper, we develop an FPGA-based data compression techniqu...

HJ-1A satellite is often used to monitor environmental disaster and plays an important role in environmental changes. Because of the affection of various factors, certain band of HJ-1A hyperspectral remote sensing data is severe loss or distortion, which brings great difficulties for subsequent quantitative processing. A novel adaptive window matching algorithm, which can adjust intelligently s...

Knowledge of the distribution of invasive plants and early detection of these species is critical for both short and long-term management of ecological systems. This study compared the quality of products derived from various multi-spectral (Landsat 7, SPOT, and Quickbird) and hyperspectral (HyMap) imaging platforms for early detection of the invasive plant leafy spurge. For each platform, the ...

The researcher will be required to work on vegetation monitoring using hyperspectral airborne or space borne data in both mountainous and urban areas. The research will require the definition and the implementation of a full processing chain of hyperspectral datasets that is able to start with radiometrically calibrated data and ending with maps of vegetated species and tree status in various e...

Onboard image processing systems for a hyperspectral sensor have been developed in order to maximize image data transmission efficiency for large volume and high speed data downlink capacity. Since more than 100 channels are required for hyperspectral sensors on Earth observation satellites, fast and small-footprint lossless image compression capability is essential for reducing the size and we...

In this article, two learning classifier system based classification techniques are described to classify remote sensing images. Usually, these images contain voluminous, complex, and sometimes erroneous and noisy data. The first approach implements ICU, an evolutionary rule discovery system, generating simple and robust rules. The second approach applies the real-valued accuracy-based classifi...

In the research of remote sensing for precision farming applications, the data quality of the aerial hyperspectral imaging system suffers from geometric distortions. Some of the distortions are caused by aircraft attitude change during the current pushbroom type image scanning. These distortions must be corrected before image data can be geo-referenced and used for field pattern identifications...