Multi-temporal Cloud Removing Technique via Adaptive Kalman Filter
Kobchai DEJHAN, Sompong WISETPHANICHKIJ and Chaiwut ARIYAPATTANAKUL
Faculty of Engineering and Research Center for Communications and Information Technology
King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
Tel: +66-2-3264238, +66-2-326-4242, Fax: +66-2-326-4554
e-mail: {sompong, kobchai}@telecom.kmitl.ac.th
Email: sompong@telecom.kmitl.ac.th,
kobchai@telecom.kmitl.ac.th
Chanchai Pienvijarnpong
Geo-Informatiocs and Space Technology Developemnt Agency (GISTDA)
Chalongkrug Road, Ladkrabang, Bangkok 10520, Thailand
e-mail: chanchai@gistda.or.th
Email: chanchai@gistda.or.th
ABSTRACT This paper proposes cloud removing technique by adaptive Kalman filter. The
multi-temporal images, cloud-cover image and reference image are applied to the filter. This
recursive nature of filter is one of the very appealing features which is designed to operate on all
of the data directly for each estimate and instead recursively conditions the current estimate on
all of the past measurement. The output of filters with 3 different raster scans then combined
together to achieve the cloud removed result.
1. INTRODUCTION
The image distortion due to cloud cover and cloud shadow is a classical problem of
visible band of remote sensing imagery. Especially, for non-stationary satellite that commonly
found in earth resource observation application. The long recurrent period that can up to 44 days
for JERS-1, 26 days for SPOT or 16 days for LANDSAT turn to be a trouble for monitoring
(change detection) problem when the continuity of acquired scene was broken by cloud cover
problem. To recovery the hidden area under cloud and its shadow, the various techniques were
proposed in the previous papers. The histogram-based technique is a classical method that can
be found in previous paper [1]. The fusion techniques [2] are also adopted for this task [3]. The
key of these techniques is to find out the hidden area on corrupted image and then replace with
clearer or reference image. The effectiveness of these methods depends on how to extract the
cloud over or hidden area efficiently. This paper proposes the alternative way to solve this
problem by reconstruct the distort area. By feeding these two images, cloud-covered image and
reference image to the well known recursive adaptive filter, the discrete Kalman’s filter [4]. The
recovering of hidden area can be accomplished within 5 steps for each pixel,
The Kalman filter estimates a process by using a form of feedback control. It estimates
the process state at some time and then obtains the feedback in the form of (noisy)
measurements. As such, the equations for the Kalman filter fall into groups, time update
equations and measurement update equations. The time update equations are responsible for
projecting forward (in time) the current state and error covariance estimates to obtain the a priori
estimates for the next time step. The measurement update equations are responsible for the
feedback i.e., for incorporating a new measurement into the a priori estimate to obtain an
improved posteriori estimate. The time update equations can also be thought of as predictor
equations, while the measurement update equations can be thought of as corrector equations.
The estimated output of the filters then combined to achieve the cloud removing. All
details of proposed scheme are mentioned in section 2. The subsequence section shows some
samples of cloud removing result by proposed scheme.
