We assess the effectiveness of a previously proposed noise reduction technology for hyperspectral imagery to examine whether it can better serve remote sensing applications after noise reduction using the technology. Target detection from hyperspectral imagery using a spectral unmixing approach is selected as an example in the assessment. A hyperspectral datacube acquired using an airborne short-wave-infrared Full Spectrum Image II with man-made targets in the scene of the datacube is tested. Three criteria are proposed and used to evaluate the detectability of the targets derived from the datacube before and after noise reduction. The evaluation results show that the detectability of the targets is significantly improved after noise reduction using the technology. The targets not detected from the original datacube are detected with high confidence after noise reduction using the technology. A noise reduction technique that is based on a smoothing approach is also evaluated for the sake of comparison to the proposed noise reduction technology. It also improves the detectability of the targets, but is less effective than the proposed noise reduction technology.
This paper assesses the effectiveness of a signal-to-noise ratio (SNR) enhancement technology for hyperspectral imagery to examine whether it can better serve remote sensing applications. A hyperspectral data set acquired using an airborne Short-wave-infrared Full Spectrum Image II with man-made targets in the scene of the data set was tested. Spectral angle mapper and end-members of different target materials were used to measure the superficies of the targets and to assess the detectability of the targets before and after applying the SNR enhancement technology to the data set. The experimental results show that small targets, which cannot be detected in the original data set due to inadequate SNR and low spatial resolution, can be detected after the SNR of the data set is enhanced.
The Canadian Space Agency (CSA) is developing a pre-operational spaceborne Hyperspectral Environment and Resource Observer (HERO). HERO will be a Canadian optical Earth observation mission that will address the stewardship of natural resources for sustainable development within Canada and globally. To deal with the challenge of extremely high data rate and the huge data volume generated onboard, CSA has developed two near lossless data compression techniques for use onboard a satellite. CSA is planning to place a data compressor onboard HERO using these techniques to reduce the requirement for onboard storage and to better match the available downlink capacity. Anomalies in the raw hyperspectral data can be caused by detector and instrument defects. This work focuses on anomalies that are caused by dead detector elements, frozen detector elements, overresponsive detector elements and saturation. This paper addresses the effect of these anomalies in raw hyperspectral imagery on data compression. The outcome of this work will help to decide whether or not an onboard data preprocessing to remove these anomalies is required before compression. Hyperspectral datacubes acquired using two hyperspectral sensors were tested. Statistical measures were used to evaluate the data compression performance with or without removing the anomalies. The effect of anomalies on compressed data was also evaluated using a remote sensing application.