Browsing by Author "Raghavendra B.S."

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CloudX-net: A robust encoder-decoder architecture for cloud detection from satellite remote sensing images
(2020) Kanu S.; Khoja R.; Lal S.; Raghavendra B.S.; CS A.
Cloud Detection is an important pre-processing step for any application involving remote sensing data. This paper presents a deep learning based CloudX-Net architecture, that can detect cloud cover with improved accuracy in comparison to the benchmark from satellite remote sensing images. The proposed CloudX-Net model reduces the number of parameters needed for accurate predictions and thus make deep learning based cloud detection method very efficient. Atrous Spatial Pyramid Pooling (ASPP) and Separable convolution are used to optimize the network. For experimentation, we have used Landsat 8 images and 38-Cloud dataset and trained the architectures using Soft Jaccard loss function. Comparing several quantifying metrics result from various recent deep learning architectures proves the efficiency and effectiveness of the proposed CloudX-Net model for cloud detection from satellite images. The source code and data are available at https://github.com/shyamfec/CloudXNet. © 2020 Elsevier B.V.
Effectiveness of Phase Correlation Spectral Similarity Measure in Distinguishing Target Signatures for Hyperspectral Data Analysis
(2020) Yadav P.P.; Shetty A.; Raghavendra B.S.; Narasimhadhan A.V.
Hyperspectral imaging is one of the most information-rich sources of remote sensing data that exists. It can capture the entire, continuous spectrum of color and light. Feature extraction techniques that are selected for identifying diagnostic features influence classification accuracy. Spectral matching algorithms like similarity measures are developed to compare spectral features of materials with their reference spec-tral signatures in identifying different earth surfaces. Similarity measures are used as simple feature extraction techniques in target identification using hyperspectral data. Though there are several similarity measures, selecting a robust similarity measure requires further investigation. Influence of similarity measures are not studied much in distinguishing spectrally distinct signatures. In this article, we propose to study the performance of similarity measures in (i) discriminating endmember signatures (ii) mixed pixel identification (iii) clustering signatures of different classes and (iv) endmember extraction. Experimental results show an effective and a robust performance of proposed phase correlation similarity measure among all other similarity measures compared for all the problems under investigation. © 2020 IEEE.
Finite rate of innovation signal reconstruction using residual neural networks
(2020) Reddy P.S.; Premkumar A.; Saikiran B.; Raghavendra B.S.; Narasimhadhan A.V.
The classical theory of sampling offers an accurate reconstruction of band-limited signals, however, not for the non band-limited. Over the previous decade, Finite Rate of Innovation (FRI) framework has emerged that has overcome this limitation. The FRI framework considers the sampling and reconstruction of particular classes of signals which are non-bandlimited and are fully specified by a finite number of degrees of freedom per unit interval. Traditional FRI algorithms, such as matrix pencil and Cadzow algorithm followed by annihilating filter methods use the singular value decomposition (SVD) for signal reconstruction. Eventhough, these algorithms achieve optimal results, however, in the low signal-to-noise ratio (SNR) they become unstable due to the larger magnitudes of the noise singular values than signal singular values. In this paper, to overcome this problem, a residual convolutional neural network approach, which estimates the signal parameters, is proposed. This network learns from the training data and gives the signal parameters from the discrete sample values. Simulation results show significant improvements on the smaller SNR values over the traditional FRI algorithms. © 2020 IEEE.
Similarity measures in generating spectrally distinct targets
(2020) Yadav P.P.; Shetty A.; Raghavendra B.S.; Narasimhadhan A.V.
In multispectral and hyperspectral remote sensing, classification of pixels is obtained by means of spectral similarity of known field or library spectra to unknown image spectra. Endmember extraction is the most decisive task in hyperspectral image analysis. Endmember initialization algorithms (EIAs) play a key role and support endmember extraction algorithms (EEAs) in extracting near optimal set of endmembers. Though there are few endmember initialization techniques available, similarity measures are not explored in detail in target generation. Hence, in this paper, it is proposed to explore similarity measures in identifying spectrally distinct signatures to use them as initial endmembers. Individual similarity measures are combined to form hybrid similarity measures to confirm their effectiveness in generating spectrally distinct targets. Initial set of endmembers extracted by proposed algorithm are used for initializing classical EEA, the NFINDR, which is sensitive to endmember initialization, and their performance in final endmembers selection is verified. Experimental results on two hyperspectral data sets show the superior performance of the similarity based endmember initialization algorithm (SMEIA). © 2020 IEEE.
Subtractive clustering and phase correlation similarity measure for endmember extraction
(2020) Palla P.Y.; Shetty A.; Raghavendra B.S.; Narasimhadhan A.V.
Target identification using Remote Sensing techniques saves time, cost and reduces difficulties in field investigation. The endmember is a reference spectral response of a pure pixel in the hyperspectral image and is used for object identification/classification from hyperspectral data. Quality of endmembers selected influences classification accuracy. Though there have been several algorithms proposed for endmember extraction, choosing a benchmark algorithm requires further investigation. To the best of our knowledge, similarity measures have not been explored much in the extraction of spectrally distinct signatures called endmembers. In this paper, we propose a similarity measures based subtractive clustering algorithm (SM-SCA) for endmember extraction. The objective of this paper is to explore the applicability of a SM-SCA and effectiveness of different similarity measures in endmember extraction and to compare it's performance with classical endmember extraction algorithms. Implementation on airborne hyperspectral (Samson data and AVIRIS data over Cuprite region) and synthetic data proves that SM-SCA is capable of extracting endmembers of all the materials identified in the data, with appropriate similarity measure. Experimental results show that (i) the similarity measures are potential not only to discriminate but also in extraction of different endmember signatures and (ii) the proposed SM-SCA with phase correlation similarity measure perform comparable to the classical endmember extraction algorithms in identifying endmembers. © 2020 Elsevier B.V.