Showing papers by "Heng-Da Cheng published in 1997"

Monitoring land-surface snow conditions from SSM/I data using an artificial neural network classifier : Remote sensing for a sustainable future

Abstract: Previously developed Special Sensor Microwave/ Imager (SSM/I) snow classification algorithms have limitations and do not work properly for terrain where forests overlie snow cover. In this study, we applied unsupervised cluster analysis to separate SSM/I brightness temperature (T B ) observations into groups. Six desired snow conditions were identified from the clusters; both sparse- and medium-vegetated region scenes were assessed. Typical SSM/I T B signatures for each snow condition were determined by calculating the mean T B value of observations for each channel in the corresponding cluster. A single-hidden-layer artificial neural network (ANN) classifier was designed to learn the SSM/I T B signatures. An error backpropagation training algorithm was applied to train the ANN. After training, a winner-takes-all method was used to determine the snow condition. Results showed that the ANN classifier was able to outline not only the snow extent but also the geographical distribution of snow conditions. This study confirms the potential of using cluster means in ANN supervised learning, and suggests a nonlinear retrieval method for inferring land-surface snow conditions from SSM/I data over varied terrain.

Monitoring land-surface snow conditions from SSM/I data using an artificial neural network classifier

Abstract: Previously developed Special Sensor Microwave/Imager (SSM/I) snow classification algorithms have limitations and do not work properly for terrain where forests overlie snow cover. In this study, the authors applied unsupervised cluster analysis to separate SSM/I brightness temperature (T/sub B/) observations into groups. Six desired snow conditions were identified from the clusters; both sparse- and medium-vegetated region scenes were assessed. Typical SSM/I T/sub B/ signatures for each snow condition were determined by calculating the mean T/sub B/ value of observations for each channel in the corresponding cluster. A single-hidden-layer artificial neural network (ANN) classifier was designed to learn the SSM/I T/sub B/ signatures. An error backpropagation training algorithm was applied to train the ANN. After training, a winner-takes-all method was used to determine the snow condition. Results showed that the ANN classifier was able to outline not only the snow extent but also the geographical distribution of snow conditions. This study confirms the potential of using cluster means in ANN supervised learning, and suggests a nonlinear retrieval method for inferring land-surface snow conditions from SSM/I data over varied terrain.

Application of artificial neural networks to the measurement of ultrashort laser pulses

Abstract: Frequency-resolved optical grating (FROG) is a technique for measuring the intensity and phase of ultrashort laser pulses. In FROG, a spectrogram of the pulse is produced from which the intensity and phase of the pulse's electric field is then retrieved using an iterative algorithm. This iterative algorithm performs well for all types of pulses, but it sometimes requires more than a minute to converge, and faster retrieval is desired for many applications. As a faster alternative, we therefore employed a neural network to invert the function that relates the pulse intensity and phase to its FROG trace. In previous work, we showed that a neural network can retrieve simple pulses, described by four or six parameters, rapidly and directly. In this contribution, we discuss our latest attempts to train an artificial neural network for more complex pulse shapes.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.