Lossy compression is preferred for many of applications; however, it is not preferred in the remote sensing community, because the use of lossy compression may change the features of remote sensing data. In this paper, we study the effect of lossy compression on two of the most common indices for vegetation feature extraction; Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). The study is performed over several Landsat ETM+ images, and our experimental results show that the different transformations used in lossy compression techniques exhibit different impacts on the reconstructed NDVI and/or NDWI. We have also observed that, for certain compression techniques, a low PSNR may represent more vegetation features. This work shows the recommended compression techniques related to Landsat image vegetation quantity. Results and discussion provide helpful guidelines for joint classification and compression of remote sensing images.
A new technique for denoising and compression of multispectral satellite images to remove the effect of noise on the compression process is presented. One type of multispectral images has been considered: Landsat Enhanced Thematic Mapper Plus. The discrete wavelet transform (DWT), the dual-tree DWT, and a simple Huffman coder are used in the compression process. Simulation results show that the proposed technique is more effective than other traditional compression-only techniques.
The utilization of adaptive equalization in the design of atmospheric laser link transceiver architectures that can be used for television and broadcast signal interconnect between the external place of event and the master control room is suggested. At the transmitter side of the proposed transceiver; an array of atmospheric laser sources, digital signal processing, and optical radiators are used to send light waves in free space. At the receiver side, an adaptive finite impulse response least mean square (LMS) equalizer with activity detection guidance (ADG) and tap decoupling (TD) is used to mitigate the effect of channel impairments. The performance of the suggested adaptive equalizer is compared with that of the conventional adaptive equalizer based only on the standard LMS algorithm. The simulation results revealed that the adaptive LMS equalizer with ADG and TD is a promising solution for the inter-symbol interference problem in optical wireless communication systems.
This paper presents a new homomorphic image cryptosystem. The idea of this system is based on encrypting the reflectance component after the homomorphic transform and embedding the illumination component as a least significant bit watermark into the encrypted reflectance component. A comparison study is held between the RC6 block cipher algorithm and the chaotic Baker map algorithm for the encryption of the reflectance component. We present a security analysis for the proposed cryptosystem against the entropy, brute-force, statistical, and differential attacks from a strict cryptographic viewpoint. Experimental results verify and prove that the proposed homomorphic image cryptosystem is highly secure from the cryptographic viewpoint. The results also prove that this cryptosystem has a very powerful diffusion mechanism (a small change in the plain text makes a great change in the cipher image). The homomorphic encryption using RC6 algorithm is more secure than that using the chaotic Baker map algorithm but not robust to noise. Thus, the proposed homomorphic cryptosystem can be used in different applications, depending on the core algorithm used.
A regularized wavelet-based image super-resolution reconstruction approach is presented. The super-resolution image reconstruction problem is an ill-posed inverse problem. Several iterative solutions have been proposed, but they are time-consuming. The suggested approach avoids the computational complexity limitations of existing solutions. It is based on breaking the problem into four consecutive steps: a registration step, a multichannel regularized restoration step, a wavelet-based image fusion and denoising step, and finally a regularized image interpolation step. The objective of the wavelet fusion step is to integrate all of the data obtained from the multichannel restoration step into a single image. The wavelet denoising is performed for the low-SNR cases to reduce the noise effect. The obtained image is then interpolated using a regularized interpolation scheme. The paper explains the implementation of each of these steps. The results indicate that the proposed approach has succeeded in obtaining a high-resolution image from multiple degraded observations with a high peak SNR. The performance of the proposed approach is also investigated for degraded observations with different SNRs. The proposed approach can be implemented for large-dimension low-resolution images, which is not possible in most published iterative solutions.