Conversion of Landsat 8 multispectral data through modified private content‐based image retrieval technique for secure transmission and privacy

Abstract

In this research work, we have developed a new image encryption-based algorithm for Landsat 8 satellite images. Image statistical parameters related to first-order image statistics, that is, mean (μ), SD (σ), and variance (σ 2) are used to obtain the feature values for both original images and their encrypted versions. Multispectral satellite images generally contain a wide range of bands, which is 9 to 11 specifically for the Landsat 8 satellite. Hence, the information content of these images is richer than typical images. Through this algorithm, we aim to reduce the image content by randomly selecting only 10% of the total image pixels. These pixels are used to create new images, the so-called encrypted images. The visual presentation of the encrypted image resembles a noisy picture. First-order image statistics are calculated for both original images and their encrypted counterparts. These parameters show a similarity in their numerical values for both original and encrypted images. Thus, noisy pictures can be used for transmission in place of the original image when an outcome is decided through statistical image features. The visual similarity in the original images and encrypted images is obtained from the histogram signature plot for both original and encrypted images. The proposed algorithm can hence be used to convert image data for secure transmission and privacy.

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.…”