In this article, we propose a novel algorithm to obtain a solution to the clustering problem with an additional constraint of connectivity. This is achieved by suitably modifying K-Means algorithm to include connectivity constraints. The modified algorithm involves repeated application of watershed transform, and hence is referred to as iterated watersheds. Detailed analysis of the algorithm is performed using toy examples. Iterated watersheds is compared with several image segmentation algorithms. It has been shown that iterated watersheds performs better than methods such as spectral clustering, isoperimetric partitioning, and K-Means on various measures. To illustrate the applicability of iterated watersheds -a simple problem of placing emergency stations and suitable cost function is considered. Using real world road networks of various cities, iterated watersheds is compared with K-Means and greedy K-center methods. It is observed that iterated watersheds result in 4 -66 percent improvement over K-Means and in 31 -72 percent improvement over Greedy K-Centers in experiments on road networks of various cities.
The availability of various spectral libraries for CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) data on NASA PDS (Planetary Data System) hugely facilitated the research on the surface mineralogy of Mars, however, building supervised learning models for mineral mapping appears to be challenging due to the lack of ground-truth/training data. In this paper, an automated framework is presented that classifies the spectra in a CRISM hyperspectral image using supervised learning models, where the required training data is produced by augmenting the mineral spectra available in the MICA (Minerals Identified in CRISM Analysis) spectral library, that keeps the key absorption signatures in the mineral spectra intact while providing adequate variability. The framework contains a pre-processing pipeline that in addition to some conventional pre-processing steps includes a new feature extraction method to capture the information of the most distinguishable absorption patterns in the spectra. The proposed framework is validated on a set of CRISM images captured from different locations on the Martian surface by using different types of supervised learning models, like random forests, support vector machines, and neural networks. An uncertainty analysis of the different steps involved in the pre-processing pipeline is provided, as well as a comparison of performances with some of the previously used methods for this purpose, which shows this framework works comparably well with a mean accuracy of around 0.8. Interactive mineral maps are also provided for the detected dominant minerals.
Clustering is one of the most important steps in the data processing pipeline. Of all the clustering techniques, perhaps the most widely used technique is K-Means. However, K-Means does not necessarily result in clusters which are spatially connected and hence the technique remains unusable for several remote sensing, geoscience and geographic information science (GISci) data. In this article, we propose an extension of K-Means algorithm which results in spatially connected clusters. We empirically verify that this indeed is true and use the proposed algorithm to obtain most significant group of waterbodies mapped from multispectral image acquired by IRS LISS-III satellite.
Watershed transformation on grey-scale images has been one of the most reliable methods for image segmentation for years. To address the challenges of over-segmentation lies in watershed transformation, strategies like waterfall and P-algorithm came in the scene. Another new study involving watershed arcs, that divides an image into non-connected segments by generating a maximal vertex-cut in-between them, also was successful to overcome this challenge, where at each level some of the existing arcs are removed to merge the corresponding neighbor basins. Selecting the set of arcs to be removed in a level being determined from a arc-graph using only the existing arcs makes the run-time extremely low. In this study, we incorporate the concept of region merging using watershed arcs for multi-band images. We used a stricter criterion to determine which arcs to be removed in a level by imposing weight on an arc's dissimilarity from its neighboring arcs, a factor that was not there in the initial concept. The performance of the proposed method is evaluated on the Wiezmann dataset of color images in comparison to some of the existing methods in the literature.
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