Volume XLII-5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 155-160, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-155-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 155-160, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-155-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Nov 2018

19 Nov 2018

A MULTI THREADED FEATURE EXTRACTION TOOL FOR SAR IMAGES USING OPEN SOURCE SOFTWARE LIBRARIES

C. Bipin1, C. V. Rao1, P. V. Sridevi2, S. Jayabharathi1, and B. G. Krishna1 C. Bipin et al.
  • 1National Remote Sensing Center, Hyderabad, India
  • 2Department of ECE, Andhra University College of Engineering (A), Andhra Pradesh, India

Keywords: software architecture, feature extraction, SAR images, remote sensing applications, algorithms of high complexity, multi-threaded platform

Abstract. In this paper, we propose a software architecture for a feature extraction tool which is suitable for automatic extraction of sparse features from large remote sensing data capable of using higher order algorithms (computational complexity greater than O(n)). Many features like roads, water bodies, buildings etc in remote-sensing data are sparse in nature. Remote-sensing deals with a large volume of data usually not manageable fully in the primary memory of typical workstations. For these reason algorithms with higher computational complexity is not used for feature extraction from remote sensing images. A good number of remote sensing applications algorithms are based on formulating a representative index typically using a kernel function which is having linear or less computational complexity (less than or equal to O(n)). This approach makes it possible to complete the operation in deterministic time and memory.

Feature extraction from Synthetic Aparture Radar (SAR) images requires more computationally intensive algorithm due to less spectral information and high noise. Higher Order algorithms like Fast Fourier Transform (FFT), Gray Level Co-Occurrence Matrix (GLCM), wavelet, curvelet etc based algorithms are not preferred in automatic feature extraction from remote sensing images due to their higher order of computational complexity. They are often used in small subsets or in association with a database where location and maximum extent of the features are stored beforehand. In this case, only characterization of the feature is carried out in the data.

In this paper, we demonstrate a system architecture that can overcome the shortcomings of both these approaches in a multi-threaded platform. The feature extraction problem is divided into a low complexity with less accuracy followed by a computationally complex algorithm in an augmented space. The sparse nature of features gives the flexibility to evaluate features in Region Of Interest (ROI)s. Each operation is carried out in multiple threads to minimize the latency of the algorithm. The computationally intensive algorithm evaluates on a ROI provided by the low complexity operation. The system also decouples complex operations using multi-threading.

The system is a customized solution developed completely in python using different open source software libraries. This approach has made it possible to carry out automatic feature extraction from Large SAR data. The architecture was tested and found giving promising results for extraction of inland water layers and dark features in ocean surface from SAR data.