Volume III-1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-1, 129-133, 2016
https://doi.org/10.5194/isprs-annals-III-1-129-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-1, 129-133, 2016
https://doi.org/10.5194/isprs-annals-III-1-129-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  02 Jun 2016

02 Jun 2016

AUTOMATIC ASSESSMENT OF ACQUISITION AND TRANSMISSION LOSSES IN INDIAN REMOTE SENSING SATELLITE DATA

D. Roy1, B. Purna Kumari2, M. Manju Sarma2, N. Aparna2, and B. Gopal Krishna2 D. Roy et al.
  • 1Space Applications Centre, Indian Space Research Organization, Ahmedabad, India
  • 2National Remote Sensing Centre, Indian Space Research Organization, Hyderabad, India

Keywords: Remote Sensing, Transmission Data Losses, Anomaly Detection, Impulse Noise, Line Losses

Abstract. The quality of Remote Sensing data is an important parameter that defines the extent of its usability in various applications. The data from Remote Sensing satellites is received as raw data frames at the ground station. This data may be corrupted with data losses due to interferences during data transmission, data acquisition and sensor anomalies. Thus it is important to assess the quality of the raw data before product generation for early anomaly detection, faster corrective actions and product rejection minimization. Manual screening of raw images is a time consuming process and not very accurate. In this paper, an automated process for identification and quantification of losses in raw data like pixel drop out, line loss and data loss due to sensor anomalies is discussed. Quality assessment of raw scenes based on these losses is also explained. This process is introduced in the data pre-processing stage and gives crucial data quality information to users at the time of browsing data for product ordering. It has also improved the product generation workflow by enabling faster and more accurate quality estimation.