B. Krishna Mohan

Image Matching Using SIFT Features and Relaxation Labeling Technique—A Constraint Initializing Method for Dense Stereo Matching

A probabilistic neural-network-based feature-matching algorithm for a stereo image pair is presented in this paper, which will be useful as a constraint initializing method for further dense matching technique. In this approach, scale-invariant feature transform (SIFT) features are used to detect interest points in a stereo image pair. The descriptor which is associated with each keypoint is based on the histogram of the gradient magnitude and direction of gradients. These descriptors are the preliminary input for the matching algorithm. Using disparity range computed by visual inspection, the search area can be restricted for a given stereo image pair. Reduced search area improves the computation speed. Initial probabilities of matches are assigned to the keypoints which are considered as probable matches from the selected search area by Bayesian reasoning. The probabilities of all such matches are improved iteratively using relaxation labeling technique. Neighboring probable matches are exploited to improve the probability of best match using consistency measures. Confidence measures considering the neighborhood, unicity, and symmetry are some validation techniques which are built into the technique presented here for finding accurate matches. The algorithm is found to be effective in matching SIFT features detected in a stereo image pair with greater accuracy, and these accurate correspondences can be used in finding the fundamental matrix which encodes the epipolar geometry between the given stereo image pair. This fundamental matrix can then be used as a constraint for finding inliers that are used in matching methods for deriving dense disparity map.

A New Self-Training-Based Unsupervised Satellite Image Classification Technique Using Cluster Ensemble Strategy

This letter addresses the problem of unsupervised land-cover classification of remotely sensed multispectral satellite images from the perspective of cluster ensembles and self-learning. The cluster ensembles combine multiple data partitions generated by different clustering algorithms into a single robust solution. A cluster-ensemble-based method is proposed here for the initialization of the unsupervised iterative expectation-maximization (EM) algorithm which eventually produces a better approximation of the cluster parameters considering a certain statistical model is followed to fit the data. The method assumes that the number of land-cover classes is known. A novel method for generating a consistent labeling scheme for each clustering of the consensus is introduced for cluster ensembles. A maximum likelihood classifier is henceforth trained on the updated parameter set obtained from the EM step and is further used to classify the rest of the image pixels. The self-learning classifier, although trained without any external supervision, reduces the effect of data overlapping from different clusters which otherwise a single clustering algorithm fails to identify. The clustering performance of the proposed method on a medium resolution and a very high spatial resolution image have effectively outperformed the results of the individual clustering of the ensemble.

Selection of Suitable Window Size for Speckle Reduction and Deblurring using SOFM in Polarimetric SAR Images

Classification performance of PolSAR data, when used without speckle reduction is insufficient for most applications. Thus, speckle filtering becomes an essential preprocessing step. In this study we evaluate the effectiveness of different popular speckle filters and analyse their effects on the classification accuracy. We have used L-band and C-band fully polarimetric dataset acquired over Mumbai, India. The Wishart supervised classifier algorithm is used for classification of the filtered and unfiltered data. Boxcar, Refined Lee, Lopez, IDAN, Improved Sigma and sequential filters are analysed for the improvement in classification accuracy. Further we also evaluate the effect of window size on classification accuracy in order to be able to select appropriate window for speckle suppression. Boxcar and Refined Lee filters are used to test the effect of speckle filtering on classification with varying moving window size. Boxcar filter is widely used in the SAR application domain owing to it’s simplicity. However, the indiscriminate averaging of the Boxcar filter causes a resolution loss in the vicinity of sharp edges and point targets in the image. To overcome this, we have applied Kohonens Self-Organizing Feature Map (SOFM) algorithm to deblurr the image and improve edge and target preservation performance.

Local Contrast Based Adaptive SAR Speckle Filter

In this paper, we propose an adaptive filtering technique for Synthetic Aperture Radar (SAR) images. A new windowing technique is introduced where the total window is divided into five equal sized overlapping sub-windows. The pixel to be filtered is a part of each of these sub-windows. A weighted mean of all sub-windows is computed for the pixel under consideration. The weights are accounted from a measure of heterogeneity calculated for each sub-windows. The filter is able to adapt automatically and adjust the speckle suppression strength based on local statistics. This allows the filter to preserve edges while strongly suppressing speckle over homogeneous areas. The proposed filter was compared with some well known SAR filtering techniques in terms of speckle suppression and edge preservation ability. Several experiments were performed on datasets acquired from both air-borne and space-borne SAR platforms. Some well known indices were used for quantitative comparison with other filters. Among the filters compared, the proposed filter shows good speckle suppression ability while still exhibiting reasonable edge preservation ability.

A novel graph matching based approach for land-cover classification of multi-temporal images

We address the problem of automatic land-cover map updating of multi-temporal and multi-spectral remotely sensed images in this paper. Given a pair of images acquired on the same geographical area at two distinct time instants, it is assumed here that the training data are available for one of the acquisitions, which is known as the source domain image. The task is to classify the other image (target domain image) for which no reliable reference map is available. It is further assumed that both the images share the same set of land-cover classes though the statistical properties of the classes may change considerably over time. Under these assumptions, a novel graph matching technique is proposed to approximate the class labels of the target domain data which is initially clustered. Given that the samples from different classes may overlap in the spectral domain, which a clustering algorithm fails to detect properly, a partially supervised Maximum Likelihood (ML) classifier coupled with the Expectation Maximization (EM) based parameter re-training scheme is used iteratively to further jointly update the class-conditional densities and the prior probabilities of the classes in the target domain image. Experimental results obtained on multi-spectral datasets confirm the the effectiveness of the proposed method.

Self-organizing feature map based polarimetric SAR data denoising

Speckle has a nature of multiplicative noise which is difficult to deal as compared to additive noise. It complicates the problem of interpretation of the image segmentation and classification. The primary goal of existing speckle filtering algorithms, which are subjective in nature is to reduce the speckle without loss of information. Various techniques have been proposed to suppress the speckle. In this paper we propose Self-Organizing Feature Map (SOFM) based polarimetric SAR speckle filter. The filter is evaluated using fully polarimetric ALOSPALSAR and Radarsat-2 data imaged over Mumbai, India. Quantitative and qualitative results revels that SOFM based approach is effective in terms of bias and speckle reduction.

Development of Agricultural Crops Spectral Library and Classification of Crops Using Hyperion Hyperspectral Data

The very high effectiveness of hyperspectral sensors in vegetation discrimination increases the applications of crop classification using hyperspectral data. However, for this capability to be exploitable, it is essential that a well-populated spectral library exists and is accessible in a user-friendly way by the user of this technology. To address this issue field hyperspectral measurement using field spectrometer have been collected and spectral library for various crops have been developed in this study. Spectral Angle Mapper (SAM) approach has been used for image classification and accuracy assessment has been carried out to test the end results. The reasonable good overall accuracy shows that the possible integration of field spectra (in-situ hyperspectral measurements) with pixel data (space-borne hyperspectral data).

Object-based analysis of WorldView-2 imagery of urban areas

In recent years, remote sensing has become an increasing data source to support urban planning and management due to availability of very high resolution (VHR) images having resolution of less than 0.5 m. Such images allow extracting of detailed information of various targets of urban areas with the help of object-based image analysis (OBIA) in contrast to traditional pixel-based methods [1]. The aim of the study is to investigate the capabilities and robustness of newly available 8-Band images of WorldView-2 (WV-2) for improving urban mapping capabilities with the help of object-based image analysis framework proposed by Rizvi and Mohan [2]. Within naturally occurring classes or within manmade categories it was found with 4-band Quickbird images that there were misclassification of certain objects like clear water and dense shadow [2]. The availability of better spectral capability of Worldview-2 offers greater discrimination capability along with object-based analysis. Therefore, main focus of this research is how this improved spatial and spectral resolution can contribute to extract urban features from the Worldview-2 image using an object-based analysis framework developed by the authors.

Effectiveness of SID as Spectral Similarity Measure to Develop Crop Spectra from Hyperspectral Image

The present study was undertaken with the objective to check effectiveness of spectral information divergence (SID) to develop spectra from image for crop classes based on spectral similarity with field spectra. In multispectral and hyperspectral remote sensing, classification of pixels is obtained by statistical comparison (by means of spectral similarity) of known field or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been placed on use of various spectral similarity measures to develop crop spectra from the image itself. Hence, in this study methodology suggested to develop spectra for crops based on SID. Absorption features are unique and distinct; hence, validation of the developed spectra is carried out using absorption features by comparing it with field spectra and finding average correlation coefficient r = 0.982 and computed SID equivalent r = 0.989. Effectiveness of developed spectra for image classification was computed by probability of spectral discrimination (PSD) and resulted in higher probability for the spectra developed based on SID. Image classification was carried out using field spectra and spectra assigned by SID. Overall classification accuracy of the image classified by field spectra is 78.30% and for the image classified by spectra assigned through SID-based approach is 91.82%. Z test shows that image classification carried out using spectra developed by SID is better than classification carried out using field spectra and significantly different. Validation by absorption features, effectiveness by PSD and higher classification accuracy show possibility of new approach for spectra development based on SID spectral similarity measure.

Effect of speckle on wide band of frequency components of an image

The aim of this paper is to study the effect of speckle on wide frequency components in synthetic aperture radar (SAR) images. In this study, the presence of speckle in SAR images is analysed using frequency domain techniques. SAR images consists of many features such as lines, edges, point targets, structural boundaries, homogeneous areas. In frequency domain analysis we observe these features occupy wide band of frequency spectra. In formulating the effective speckle reduction techniques, exact knowledge of noise like phenomenon on wide band of frequency spectra in SAR images is necessary. Qualitative and quantitative analysis using fourier transform helps to conclude this study.