PCA-based sea-ice image fusion of optical data by HIS transform and SAR data by wavelet transform

Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has become the primary means for sea-ice research. Optical images contain abundant sea-ice multi-spectral in-formation, whereas SAR images contain rich sea-ice texture information. If the characteristic advantages of SAR and optical images could be combined for sea-ice study, the ability of sea-ice monitoring would be im-proved. In this study, in accordance with the characteristics of sea-ice SAR and optical images, the transfor-mation and fusion methods for these images were chosen. Also, a fusion method of optical and SAR images was proposed in order to improve sea-ice identification. Texture information can play an important role in sea-ice classification. Haar wavelet transformation was found to be suitable for the sea-ice SAR images, and the texture information of the sea-ice SAR image from Advanced Synthetic Aperture Radar （ASAR） loaded on ENVISAT was documented. The results of our studies showed that, the optical images in the hue-intensi-ty-saturation （HIS） space could reflect the spectral characteristics of the sea-ice types more efficiently than in the red-green-blue （RGB） space, and the optical image from the China-Brazil Earth Resources Satellite （CBERS-02B） was transferred from the RGB space to the HIS space. The principal component analysis （PCA） method could potentially contain the maximum information of the sea-ice images by fusing the HIS and texture images. The fusion image was obtained by a PCA method, which included the advantages of both the sea-ice SAR image and the optical image. To validate the fusion method, three methods were used to evaluate the fused image, i.e., objective, subjective, and comprehensive evaluations. It was concluded that the fusion method proposed could improve the ability of image interpretation and sea-ice identification.

Compressed Sensing Based on the Single Layer Wavelet Transform for Image Fusion

In this paper, a new method of combination single layer wavelet transform and compressive sensing is proposed for image fusion. In which only measured the high-pass wavelet coefficients of the image but preserved the low-pass wavelet coefficient. Then, fuse the low-pass wavelet coefficients and the measurements of high-pass wavelet coefficient with different schemes. For the reconstruction, by using the minimization of total variation algorithm (TV), high-pass wavelet coefficients could be recovered by the fused measurements. Finally, the fused image could be reconstructed by the inverse wavelet transform. The experiments show the proposed method provides promising fusion performance with a low computational complexity.

A LOSSLESS COMPRESSION ALGORITHM OF REMOTE SENSING IMAGE FOR SPACE APPLICATIONS

A simple and adaptive lossless compression algorithm is proposed for remote sensing image compression, which includes integer wavelet transform and the Rice entropy coder. By analyzing the probability distribution of integer wavelet transform coefficients and the characteristics of Rice entropy coder, the divide and rule method is used for high-frequency sub-bands and low-frequency one. High-frequency sub-bands are coded by the Rice entropy coder, and low-frequency coefficients are predicted before coding. The role of predictor is to map the low-frequency coefficients into symbols suitable for the entropy coding. Experimental results show that the average Comprcssion Ratio （CR） of our approach is about two, which is close to that of JPEG 2000. The algorithm is simple and easy to be implemented in hardware. Moreover, it has the merits of adaptability, and independent data packet. So the algorithm can adapt to space lossless compression applications.

Entropy of images after wavelet transform

We studied the variation of image entropy before and after wavelet decomposition, the optimal number of wavelet decomposition layers, and the effect of wavelet bases and image frequency components on entropy. Numerous experiments were done on typical images to calculate (using Matlab) the entropy before and after wavelet transform. It was verified that, to obtain minimal entropy, a three-layer decomposition should be adopted rather than higher orders. The result achieved by using biorthogonal wavelet decomposition is better than that of the orthogonal wavelet decomposition. The results are not directly proportional to the vanishing moment, however.

REMOTE SENSING IMAGE CODING METHOD COMBINING WAVELET TRANSFORM WITH CLASSIFIED VECTOR QUANTIZATION

A new remote sensing image coding scheme based on the wavelet transform and classified vector quantization (CVQ) is proposed. The original image is first decomposed into a hierarchy of 3 layers including 10 subimages by DWT. The lowest frequency subimage is compressed by scalar quantization and ADPCM. The high frequency subimages are compressed by CVQ to utilize the similarity among different resolutions while improving the edge quality and reducing computational complexity. The experimental results show that the proposed scheme has a better performance than JPEG, and a PSNR of reconstructed image is 31～33 dB with a rate of 0.2 bpp.

Meaningful image encryption algorithm based on compressive sensing and integer wavelet transform

A new meaningful image encryption algorithm based on compressive sensing(CS)and integer wavelet transformation(IWT)is proposed in this study.First of all,the initial values of chaotic system are encrypted by RSA algorithm,and then they are open as public keys.To make the chaotic sequence more random,a mathematical model is constructed to improve the random performance.Then,the plain image is compressed and encrypted to obtain the secret image.Secondly,the secret image is inserted with numbers zero to extend its size same to the plain image.After applying IWT to the carrier image and discrete wavelet transformation(DWT)to the inserted image,the secret image is embedded into the carrier image.Finally,a meaningful carrier image embedded with secret plain image can be obtained by inverse IWT.Here,the measurement matrix is built by both chaotic system and Hadamard matrix,which not only retains the characteristics of Hadamard matrix,but also has the property of control and synchronization of chaotic system.Especially,information entropy of the plain image is employed to produce the initial conditions of chaotic system.As a result,the proposed algorithm can resist known-plaintext attack(KPA)and chosen-plaintext attack(CPA).By the help of asymmetric cipher algorithm RSA,no extra transmission is needed in the communication.Experimental simulations show that the normalized correlation(NC)values between the host image and the cipher image are high.That is to say,the proposed encryption algorithm is imperceptible and has good hiding effect.

Fast Adaptive Wavelet for Remote Sensing Image Compression

Remote sensing images are hard to achieve high compression ratio because of their rich texture. By analyzing the influence of wavelet properties on image compression, this paper proposes wavelet construction rules and builds a new biorthogonal wavelet construction model with parameters. The model parameters are optimized by using genetic algorithm and adopting energy compaction as the optimization object function. In addition, in order to resolve the computation complexity problem of online construction, according to the image classification rule proposed in this paper we construct wavelets for different classes of images and implement the fast adaptive wavelet selection algorithm （FAWS）. Experimental results show wavelet bases of FAWS gain better compression performance than Daubechies9/7.

Visual remote sensing image fusion using local correlation moment

In this paper a fusion method is proposed for merging a high-resolution panchromatic image and a lowresolution multispectral image. The algorithm is based on discrete wavelet transform (DWT). It uses correlation moment rule to the low frequency bands and local deviation rule to the high frequency bands separately. Experimental results indicate that the proposed approach outperforms the traditional methods.

Wavelet-Based Mixed-Resolution Coding Approach Incorporating with SPT for the Stereo Image

With the advances of display technology, three-dimensional(3-D) imaging systems are becoming increasingly popular. One way of stimulating 3-D perception is to use stereo pairs, a pair of images of the same scene acquired from different perspectives. Since there is an inherent redundancy between the images of a stereo pairs, data compression algorithms should be employed to represent stereo pairs efficiently. The proposed techniques generally use block-based disparity compensation. In order to get the higher compression ratio, this paper employs the wavelet-based mixed-resolution coding technique to incorporate with SPT-based disparity-compensation to compress the stereo image data. The mixed-resolution coding is a perceptually justified technique that is achieved by presenting one eye with a low-resolution image and the other with a high-resolution image. Psychophysical experiments show that the stereo image pairs with one high-resolution image and one low-resolution image provide almost the same stereo depth to that of a stereo image with two high-resolution images. By combining the mixed-resolution coding and SPT-based disparity-compensation techniques, one reference (left) high-resolution image can be compressed by a hierarchical wavelet transform followed by vector quantization and Huffman encoder. After two level wavelet decompositions, for the low-resolution right image and low-resolution left image, subspace projection technique using the fixed block size disparity compensation estimation is used. At the decoder, the low-resolution right subimage is estimated using the disparity from the low-resolution left subimage. A full-size reconstruction is obtained by upsampling a factor of 4 and reconstructing with the synthesis low pass filter. Finally, experimental results are presented, which show that our scheme achieves a PSNR gain (about 0.92dB) as compared to the current block-based disparity compensation coding techniques.

Compressing and Coding Method of Seismic Data

Aiming at the characteristics of the seismic exploration signals, the paper studies the image coding technology, the coding standard and algorithm, brings forward a new scheme of admixing coding for seismic data compression. Based on it, a set of seismic data compression software has been developed.

Investigation of Image Fusion Between High-Resolution Image and Multi-spectral Image

On the basis of a thorough understanding of the physical characteristics of remote sensing image, this paper employs the theories of wavelet transform and signal sampling to develop a new image fusion algorithm. The algorithm has been successfully applied to the image fusion of SPOT PAN and TM of Guangdong province, China. The experimental results show that a perfect image fusion can be built up by using the image analytical solution and re-construction in the image frequency domain based on the physical characteristics of the image formation. The method has demonstrated that the results of the image fusion do not change spectral characteristics of the original image.

ECW、MrSID、eYaImage三种压缩技术对比与分析

介绍了ECW、MrSID和eYaImage三种常用的遥感影像压缩技术的主要特点,详细总结了他们之间的异同点。同时,选择天津某一区域的100幅1∶2 000比例尺的正射影像分别使用三种压缩技术进行压缩及解压数据实验。实验采取了20∶1、50∶1、100∶1三种压缩比例进一步对比分析三种压缩技术在影像压缩效率、压缩后的影像质量、压缩影像大小以及远程访问响应速度等方面的差异。最后通过实验总结三种压缩技术的适用方向。

气象遥感图像去噪预处理方法研究

针对静止轨道遥感卫星上多通道扫描型载荷成像、传输与存储过程中,存在数据质量下降等问题,本文在经典三维块匹配算法(Block Matching 3D,BM3D)基础上,提出一种基于多层级小波分解的并行执行策略。首先,使用小波变换对原始气象遥感图像分解,得到4个图像分量;其次,将所得图像分量进一步进行3级分解,并选择其中的10个图像分量;最后,每个分量并行执行BM3D滤波器去噪,并重构10个分量的输出图像。与传统BM3D去噪算法相比,改进BM3D算法的计算量可有效降低20%以上。通过与中值滤波、均值滤波、NL-Bayes、BM3D四种降噪算法进行实验对比,所提算法的峰值信噪比平均增益在0.39~4.45 dB之间,特别是在高斯白噪声和脉冲噪声的混合噪声去噪方面要显著优于选取的四种对比算法。

基于离散小波域深度残差学习的矿区遥感图像增强算法

实现矿区遥感图像增强处理,有助于提升后续图像判别以及相关监测分析效率。以往矿区遥感图像增强一般采用滤波、灰度变换等方法,往往会导致图像大量细节信息丢失,在很大程度上影响了后续判读分析。近年来,深度学习方法逐步应用于图像增强处理,但该方法很大程度上依赖于模型设计和参数合理取值,需要进行大量的试验和优化方可取得理想效果。将深度学习方法(Deep Learning,DL)与离散小波变换(Discrete Wavelet Transform,DWT)相结合,提出了一种基于离散小波域深度残差学习的矿区遥感图像增强算法。首先将图像进行单级二维离散小波变换,得到4个子带;然后将4个子带系数输入深度学习残差网络,预测相应的残差图像增加4个子带图像和残差图像作为二维小波变换的新子带;最后通过二维离散小波逆变换得到增强图像。试验结果表明:所提算法相对于直方图均衡化和超分辨率重建等方法而言,无论在图像视觉效果以及峰值信噪比、结构相似性、均方误差等评价指标上都具有较好优势,反映出将离散小波变换与深度学习方法相结合,有助于提升矿区遥感图像视觉效果,方便后续图像解译判读工作。

面向多浓度非均匀云雾的双域遥感图像去雾算法

光学遥感卫星图像容易受到多浓度非均匀云雾的干扰,造成图像质量严重退化。现有的去雾算法难以有效处理多浓度非均匀云雾,于是提出了一种自注意力强化机制,即在自注意力变换中引入一种简单的无参注意力(SimAM)增强自注意力变换的建模能力,提高对非均匀云雾的感知;为进一步提高网络的纹理细节表征能力,设计了新颖的差分卷积细节增强块,利用差分卷积算子引入梯度级信息,提高去雾网络对纹理细节的恢复能力;为实现RGB域和自适应小波域联合去雾,引入深度自适应提升小波变换实现自适应小波空间,从而实现双域协同去雾。实验结果表明,提出的方法在主流的遥感图像去雾数据集上相较于骨干模型,获得了0.52 dB的PSNR总增益。

基于小波变换的机载雷达测绘遥感影像信息数据融合方法

在融合机载雷达测绘遥感影像信息数据的过程中,由于原始机载雷达测绘遥感影像反馈信号在频域分布上具有多元化的属性,导致融合后影像的质量相对较低。为此,文章提出基于小波变换的机载雷达测绘遥感影像信息数据融合方法。该方法通过在小波变换设置了以频率为基准的窗口函数,并结合遥感影像反馈信号所处频域与频率函数状态之间的关系,建立了待分析遥感影像信号与频率窗内的高频频域分布参数之间的乘积关系,从而得到所需的信号频谱特性,在进行机载雷达测绘遥感影像信息数据融合阶段,结合遥感影像反馈信号与有限区域范围之间的关系,对其进行适应性融合。测试结果表明,融合后影像在完整度、清晰度以及综合质量方面均处于较高水平。

基于单层小波变换的压缩感知图像处理

根据图像小波变换系数层的特点,提出了基于单层小波变换的压缩感知算法,保留图像低频系数,只对高频系数进行测量。重构时,利用正交匹配追踪算法(OMP)对高频系数进行恢复,再进行小波反变换重构图像。仿真结果表明,与原有压缩感知算法相比,重构图像质量得到极大提升,在相同的测量点数下,PSNR平均提高2～4dB。

一种适合星上应用的遥感图像有损压缩算法

提出了一种高质量的遥感图像有损压缩算法,在分析了遥感图像经Daubechies双正交小波基D5/3整数小波变换后各子带小波系数统计特性和能量分布的基础上,引入了人类视觉特性,用它来控制算法中的量化方案。根据能量的大小确定不同子带对于目标识别的重要程度,选择不同的量化阈值和量化步长进行量化,并对量化后的数据采用固定比特平面编码。仿真实验表明,该算法对于不同内容和纹理的图像,在一定的压缩比下,均获得了PSNR(峰值信噪比)>30dB的恢复图像,在不损失最低频信息的同时较好地保持了遥感图像中丰富的高频信息,实现了高质量的图像压缩,并且算法简便,快捷,所占用的存储容量小,易于硬件实现,适合于星上应用,减少了在遥感图像压缩中小目标的丢失。

视觉感受与Markov随机场相结合的高分辨率遥感影像分割法

鉴于视觉感受对外界强大的感知与识别能力,模拟视觉神经感知的工作机制,并结合Markov随机场模型,提出一种影像分割方法。首先,分析视觉感知系统的工作机制,将其特性归纳为等级层次性、学习能力、特征检测能力和稀疏编码特性,继而利用小波变换、非监督聚类、特征分析和Laplace分布模拟视觉工作机制,然后结合Markov随机场模型实现高分辨率遥感影像的分割。通过不同卫星的真实遥感影像进行了相关试验。试验结果表明本文提出的方法在高分辨率遥感影像分割任务中有非常良好的表现。