Despite recent rapid advancement in remote sensing technology, accurate mapping of the urban landscape in China still faces a great challenge due to unusually high spectral complexity in many big cities. Much of this complication comes from severe spectral confusion of impervious surfaces with polluted water bodies and bright bare soils. This paper proposes a two-step land cover decomposition method, which combines optical and thermal spectra from different seasons to cope with the issue of urban spectral complexity. First, a linear spectral mixture analysis was employed to generate fraction images for three preliminary endmembers (high albedo, low albedo, and vegetation). Seasonal change analysis on land surface temperature induced from thermal infrared spectra and coarse component fractions obtained from the first step was then used to reduce the confusion between impervious surfaces and nonimpervious materials. This method was tested with two-date Landsat multispectral data in Shanghai, one of China’s megacities. The results showed that the method was capable of consistently estimating impervious surfaces in highly complex urban environments with an accuracy of R2 greater than 0.70 and both root mean square error and mean average error less than 0.20 for all test sites. This strategy seemed very promising for landscape mapping of complex urban areas.
The novel photodetector array has a specific inner multiplication mechanism at low bias voltage in the weak light environment. High current gains are achieved and accompanied by extremely low dark currents. The photocurrent spectrum of the photodetector shows excellent light absorption in visible spectrum (VIS) and near infrared spectrum (NIRS), ranging from 500nm up to 900nm. Capacitor feedback trans-impedance amplifier (CTIA) readout circuit has been designed to acquire the integration voltage of photocurrent with wide wavelength range. Weak light experiments have measured the response voltage is 4mV@0.01pW illumination and 100μs integration time at 300K. The responsivity reaches 4×1011V/W. A high sensitivity spectrometer based on the photodetector array and readout circuit is developing and will be applied to weak signal detection in the fields of the environmental monitoring and biomedicine science.
Applications of the intensity-hue-saturation (IHS) based image fusion techniques in resource inventory and environmental monitoring are usually hampered by considerable spectral distortion in the spatially enhanced image. The image transform model needs to be regulated via a proper design of the weight structure and controlling parameters to achieve a better spectral fidelity. Use of localized weight estimation and an output constraint to modify the generalized intensity-hue-saturation transform (GIHS) for the purpose of rectifying digital numbers of the fused image back to their original counterparts are proposed. The weight localization was achieved via land cover classification of the multispectral data, and the spectral constraint was constructed using a ratio between individual spectral bands stratified with each land cover type and the modified image intensity value. This method was compared both spatially and spectrally with the traditional IHS and the GIHS that has a weight structure induced from the sensor's spectral response characteristics. Experiments with WorldView-2 multispectral and panchromatic data indicated that the new image fusion approach achieved the highest level of spectral fidelity with enhancement of spatial details comparable to the other IHS-based methods.
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