Modeling of texture quantification and image classification for change prediction due to COVID lockdown using Skysat and Planetscope imagery

Abstract

This research work models two methods together to provide maximum information about a study area. The quantification of image texture is performed using the “grey level co-occurrence matrix ( )” technique. Image classification-based “object-based change detection ( )” methods are used to visually represent the developed transformation in the study area. Pre-COVID and post-COVID (during lockdown) panchromatic images of Connaught Place, New Delhi, are investigated in this research work to develop a model for the study area. Texture classification of the study area is performed based on visual texture features for eight distances and four orientations. Six different image classification methodologies are used for mapping the study area. These methodologies are “Parallelepiped classification ( ),” “Minimum distance classification ( ),” “Maximum likelihood classification ( ),” “Spectral angle mapper ( ),” “Spectral information divergence ( )” and “Support vector machine ( ).” calculations have provided a pattern in texture features contrast, correlation, , and . Maximum classification accuracy of and are obtained for pre-COVID and post-COVID image data through classification technique. Finally, a model is presented to analyze before and after COVID images to get complete information about the study area numerically and visually.

New Generation Hyperspectral Data From DESIS Compared to High Spatial Resolution PlanetScope Data for Crop Type Classification

“…Both PlanetScope and DESIS data are important new data sources and need to be evaluated for various applications. For example, the high spatial resolution of PlanetScope data has been leveraged in texture analysis to estimate pasture aboveground biomass and canopy height [46]; extract arecanut planting distribution [47]; monitor forest carbon stocks and emissions [129]; classify and detect changes in land use, land cover [130], [131]; study vegetation phenology [132]; and assess wildfire damage [133]. In addition, both hyperspectral and high spatial resolution datasets have been found useful for estimating crop water productivity, a measure of how efficiently crops use water to produce food [1], [134]- [139].…”

PVONet: point-voxel-based semi-supervision monocular three-dimensional object detection using LiDAR camera systems

“…Moreover, these algorithms may struggle to accurately identify truncated objects, which can negatively impact the performance of LiDAR camera systems. Although there are already several solutions to the issue, such as the use of more precise texture classification, 10 , 11 image classification 12 , 13 to find additional parameters in the image, and unsupervised approaches 14 to reduce the time spent on data preparation, there are still problems, such as incorrect depth estimation and improper positioning of detection labels. Therefore, the development of efficient and accurate monocular 3D object detection systems is crucial for LiDAR camera systems.…”

Spatial–spectral hyperspectral images classification based on Krill Herd band selection and edge-preserving transform domain recursive filter

“…Recently, there has been significant growth in remote sensing image sources, such as Landsat images, 1 phased array L-band synthetic aperture radar images, 2 multispectral images, 3 and hyperspectral images (HSIs) 4 . Therefore, it is necessary to effectively exploit the discriminative information of remote sensing images in various fields.…”