Multispectral Satellite Data Classification using Soft Computing Approach

• URL: http://arxiv.org/abs/2203.11146v1
• Date: Mon, 21 Mar 2022 17:25:09 GMT
• Title: Multispectral Satellite Data Classification using Soft Computing Approach
• Authors: Purbarag Pathak Choudhury, Ujjal Kr Dutta, Dhruba Kr Bhattacharyya
• Abstract summary: We propose a grid-density based clustering technique for identification of objects. We introduce an approach to classify a satellite image data using a rule induction based machine learning algorithm.
• Score: 5.3971200250581814
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: A satellite image is a remotely sensed image data, where each pixel represents a specific location on earth. The pixel value recorded is the reflection radiation from the earth's surface at that location. Multispectral images are those that capture image data at specific frequencies across the electromagnetic spectrum as compared to Panchromatic images which are sensitive to all wavelength of visible light. Because of the high resolution and high dimensions of these images, they create difficulties for clustering techniques to efficiently detect clusters of different sizes, shapes and densities as a trade off for fast processing time. In this paper we propose a grid-density based clustering technique for identification of objects. We also introduce an approach to classify a satellite image data using a rule induction based machine learning algorithm. The object identification and classification methods have been validated using several synthetic and benchmark datasets.

Related papers

• OsmLocator: locating overlapping scatter marks with a non-training generative perspective [48.50108853199417]
  Locating overlapping marks faces many difficulties such as no texture, less contextual information, hallow shape and tiny size. Here, we formulate it as a optimization problem on clustering-based re-visualization from a non-training generative perspective. We especially built a dataset named 2023 containing hundreds of scatter images with different markers and various levels of overlapping severity, and tested the proposed method and compared it to existing methods.
  arXiv  Detail & Related papers  (2023-12-18T12:39:48Z)
• Object Detection in Hyperspectral Image via Unified Spectral-Spatial Feature Aggregation [55.9217962930169]
  We present S2ADet, an object detector that harnesses the rich spectral and spatial complementary information inherent in hyperspectral images. S2ADet surpasses existing state-of-the-art methods, achieving robust and reliable results.
  arXiv  Detail & Related papers  (2023-06-14T09:01:50Z)
• Multi-spectral Class Center Network for Face Manipulation Detection and Localization [52.569170436393165]
  We propose a novel Multi-Spectral Class Center Network (MSCCNet) for face manipulation detection and localization. Based on the features of different frequency bands, the MSCC module collects multi-spectral class centers and computes pixel-to-class relations. Applying multi-spectral class-level representations suppresses the semantic information of the visual concepts which is insensitive to manipulated regions of forgery images.
  arXiv  Detail & Related papers  (2023-05-18T08:09:20Z)
• Probabilistic Deep Metric Learning for Hyperspectral Image Classification [91.5747859691553]
  This paper proposes a probabilistic deep metric learning framework for hyperspectral image classification. It aims to predict the category of each pixel for an image captured by hyperspectral sensors. Our framework can be readily applied to existing hyperspectral image classification methods.
  arXiv  Detail & Related papers  (2022-11-15T17:57:12Z)
• All-optical image classification through unknown random diffusers using a single-pixel diffractive network [13.7472825798265]
  classification of an object behind a random and unknown scattering medium sets a challenging task for computational imaging and machine vision fields. Recent deep learning-based approaches demonstrated the classification of objects using diffuser-distorted patterns collected by an image sensor. Here, we present an all-optical processor to directly classify unknown objects through unknown, random phase diffusers using broadband illumination detected with a single pixel.
  arXiv  Detail & Related papers  (2022-08-08T08:26:08Z)
• Unsupervised Spatial-spectral Hyperspectral Image Reconstruction and Clustering with Diffusion Geometry [6.279792995020646]
  This work introduces the Spatial-Spectral Image Reconstruction and Clustering with Diffusion Geometry (DSIRC) algorithm for partitioning highly mixed hyperspectral images. DSIRC locates spectrally correlated pixels within a data-adaptive spatial neighborhood and reconstructs that pixel's spectral signature using those of its neighbors. Results indicate that incorporating spatial information through image reconstruction substantially improves the performance of pixel-wise clustering.
  arXiv  Detail & Related papers  (2022-04-28T13:42:12Z)
• Multiscale Analysis for Improving Texture Classification [62.226224120400026]
  This paper employs the Gaussian-Laplacian pyramid to treat different spatial frequency bands of a texture separately. We aggregate features extracted from gray and color texture images using bio-inspired texture descriptors, information-theoretic measures, gray-level co-occurrence matrix features, and Haralick statistical features into a single feature vector.
  arXiv  Detail & Related papers  (2022-04-21T01:32:22Z)
• A 3-stage Spectral-spatial Method for Hyperspectral Image Classification [2.28438857884398]
  We propose a novel framework that utilizes both spatial and spectral information for classifying pixels in hyperspectral images. We demonstrate the superiority of our method against three state-of-the-art algorithms on six benchmark hyperspectral data sets.
  arXiv  Detail & Related papers  (2022-04-20T08:23:05Z)
• Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images [65.74185962364211]
  We present a method for incorporating neighborhood information into distance-based dimensionality reduction methods. Based on a classification of different methods for comparing image patches, we explore a number of different approaches.
  arXiv  Detail & Related papers  (2022-02-18T13:17:43Z)
• A photosensor employing data-driven binning for ultrafast image recognition [0.0]
  Pixel binning is a technique widely used in optical image acquisition and spectroscopy. Here, we push the concept of binning to its limit by combining a large fraction of the sensor elements into a single superpixel. For a given pattern recognition task, its optimal shape is determined from training data using a machine learning algorithm.
  arXiv  Detail & Related papers  (2021-11-20T15:38:39Z)
• A Novel Spatial-Spectral Framework for the Classification of Hyperspectral Satellite Imagery [1.066048003460524]
  We present a novel framework that takes into account both the spectral and spatial information contained in the data for land cover classification. Our proposed methodology performs better than the earlier approaches by achieving an accuracy of 99.52% and 98.31% on the Pavia University and the Indian Pines datasets respectively.
  arXiv  Detail & Related papers  (2020-07-22T16:12:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.