Spatial Cloud Detection and Retrieval System for Satellite Images

Abstract

Abstract-In last the decade we witnessed a large increase in data generated by earth observing satellites. Hence, intelligent processing of the huge amount of data received by hundreds of earth receiving stations, with specific satellite image oriented approaches, presents itself as a pressing need. One of the most important steps in earlier stages of satellite image processing is cloud detection. Satellite images having a large percentage of cloud cannot be used in further analysis. While there are many approaches that deal with different semantic meaning, there are rarely approaches that deal specifically with cloud detection and retrieval. In this paper we introduce a novel approach that spatially detect and retrieve clouds in satellite images using their unique properties .Our approach is developed as spatial cloud detection and retrieval system (SCDRS) that introduce a complete framework for specific semantic retrieval system. It uses a Query by polygon (QBP) paradigm for the content of interest instead of using the more conventional rectangular query by image approach. First, we extract features from the satellite images using multiple tile sizes using spatial and textural properties of cloud regions. Second, we retrieve our tiles using a parametric statistical approach within a multilevel refinement process. Our approach has been experimentally validated against the conventional ones yielding enhanced precision and recall rates in the same time it gives more precise detection of cloud coverage regions.

An Automatic Accurate High-Resolution Satellite Image Retrieval Method

“…Zhu Z and Woodcock C. E (2012) proposed an Fmask cloud detection method by combining with Landsat Top of Atmosphere (TOA) reflectance and Brightness Temperature (BT) [5]. Laban N, Nasr A et al (2012) developed the multi-scale cloud extraction of remote-sensing images using spatial and texture features [6]. Surya S. R and Simon P (2013) used color space transform and Fuzzy C-means clustering to extract cloud in Landsat images [7].…”

“…In recent years, some studies have discussed the cloud distribution expression of remote-sensing images. Laban N, Nasr A et al (2012) developed a spatial cloud detection and retrieval system (SCDRS) to retrieve the cloud distribution of remote-sensing images, and the cloud distribution information is expressed using tiling grids in their system [6]. Feng A and Shu S (2014) proposed a model of index of cloud in images based on GeoSOT, which is cloud distribution information organized by a global discrete grid system and saved in a particular file [15].…”