Considering the problem in monitoring agricultural condition in the semi-arid areas of Northwest of China, we propose a new method for estimation of crop planting area, using the single phase optical and microwave remote sensing data collaboratively, which have demonstrated their respective advantages in the extraction of surface features. In the model, the ASAR backscatter coefficient is normalized by the incident angle at first, then the classifier based on Bayesian network is developed, and the VV, VH polarization of ASAR and all the 7 TM bands are taken as the input of the classifier to get the class labels of each pixel of the images. Moreover the crop planting areas can be extracted by the classification results. At last, the model is validated for the necessities of normalization by the incident angle and integration of TM and ASAR respectively. It results that the estimation accuracy of crop planting area of corn and other crops garden are 98.47% and 78.25% respectively using the proposed method, with an improvement of estimation accuracy of about 3.28% and 4.18% relative to single TM classification. These illustrate that synthesis of optical and microwave remote sensing data is efficient and potential in estimation crop planting area.
Water is a key variable in describing the water and energy exchanges between the land surface and atmosphere interfaces.
In this paper a classifier is presented, which is based on integration of both active and passive remote sensing data and
the Maximum Likelihood classification for inversion of soil moisture and this method is tested in Heihe river basin, a
semi-arid area in the north-west of china. In the algorithm the wavelet transform and IHS are combined to integrate TM3,
TM4, TM5 and ASAR data. The method of maximum distance substitution in local region is adopted as the fusion rule
for prominent expression of the detailed information in the fusion image, as well as the spectral information of TM can
be retained. Then the new R, G, B components in the fusion image and the TM6 is taken as the input to the Maximum
Likelihood classification, and the output corresponds to five different categories according to different grades of soil
moisture. The field measurements are carried out for validation of the method. The results show that the accuracy of
completely correct classification is 66.3%, and if the discrepancy within one grade was considered to be acceptable, the
precision is as high as 92.6%. Therefore the classifier can effectively be used to reflect the distribution of soil moisture in
the study area.
This paper research on a high definition Ship-borne radar and video monitoring system which requires multi-channel TV
video and radar video encoding and decoding ability. The real time data transferring is based on RTP/RTCP protocol with
guarantee of QoS. In this paper, we propose an effective Feedback control for real time video stream to combine with
forward error correction (FEC). In our scheme, the server multicasts the video in parallel with FEC packets and adaptive
RTCP feedback control of the video stream. On the server side, we analyze and optimize the number of streams and FEC
packets to meet a certain residual loss requirement. For every RTT round trip time, the sender sends a forward RTCP
control packet. On the receiver side, we analyze the optimal combination of FEC and packets to minimize its loss. Upon
the receipt of a backward RTCP packet with the packet loss ratio from the receiver, the output rate of the source is
adjusted. Additive increase and multiplicative decrease (AIMD) model can achieve efficient congestion preventing
when the accurate available bandwidth is estimated by the backward RTCP packet.