標題: Titlebook: Real-Time Recursive Hyperspectral Sample and Band Processing; Algorithm Architectu Chein-I Chang Book 2017 Springer International Publishin [打印本頁] 作者: GUAFF 時間: 2025-3-21 19:38
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書目名稱Real-Time Recursive Hyperspectral Sample and Band Processing讀者反饋
書目名稱Real-Time Recursive Hyperspectral Sample and Band Processing讀者反饋學科排名
作者: ADJ 時間: 2025-3-21 22:06 作者: 清洗 時間: 2025-3-22 04:02 作者: BAN 時間: 2025-3-22 06:19 作者: Commonplace 時間: 2025-3-22 10:29 作者: 信任 時間: 2025-3-22 14:56
Target-Specified Virtual Dimensionality for Hyperspectral Imagerywer Academic/Plenum Publishers, New York, 2003) and later written about with details in Chang and Du (IEEE Transactions on Geoscience and Remote Sensing 42:608–619, 2004). It was originally developed for the purpose of finding an appropriate number of signatures required by linear spectral mixture a作者: 磨坊 時間: 2025-3-22 19:58
Real-Time Recursive Hyperspectral Sample Processing for Active Target Detection: Constrained Energy 016) hyperspectral target detection can be generally performed in two completely opposite modes, active hyperspectral target detection and passive hyperspectral target detection. Active hyperspectral target detection requires specific prior knowledge that can be used to detect targets of interest as作者: 射手座 時間: 2025-3-22 21:16
Real-Time Recursive Hyperspectral Sample Processing for Passive Target Detection: Anomaly Detectioneloped for its real-time and causal implementation. Rather than CEM, this chapter focuses on passive hyperspectral target detection and investigates a commonly used passive target detection technique, anomaly detection (AD), especially for real-time and causal processing capabilities that are develo作者: Misgiving 時間: 2025-3-23 03:34
Recursive Hyperspectral Sample Processing of Automatic Target Generation Processsed in a wide range of applications in hyperspectral image analysis to find unknown targets and endmembers. Since it is a pixel-based technique, it can be very easily implemented in real time. In addition, because it is also unsupervised, it can be used to find unknown targets automatically without 作者: 序曲 時間: 2025-3-23 09:25 作者: Musket 時間: 2025-3-23 09:48 作者: 吃掉 時間: 2025-3-23 16:53
Recursive Hyperspectral Sample Processing of Maximum Likelihood Estimationxing model (ALMM) that can adapt to the signatures, referred to as virtual signatures (VSs), generated directly from data in an unsupervised and recursive manner. This chapter considers an alternative approach to RHSP-LSMA, called recursive hyperspectral sample processing of maximal likelihood estim作者: Apraxia 時間: 2025-3-23 21:52 作者: Nuance 時間: 2025-3-24 00:28
Recursive Hyperspectral Sample Processing of Geometric Simplex Growing Algorithmonsquare matrix, which involves excessive computing time in calculating the matrix determinant. This type of SV calculation is referred to as a determinant-based SV (DSV) calculation (Chap. .). Therefore, several preprocessing steps are suggested for DSV calculation in Chap. . to ease computational 作者: ACRID 時間: 2025-3-24 03:42 作者: 多節(jié) 時間: 2025-3-24 08:06
Recursive Hyperspectral Band Processing for Passive Target Detection: Anomaly Detectiond anomaly detection, New York, 2016), where the main focus of AD is on the design and development of AD algorithms for causal processing, which is a prerequisite for real-time processing. Chapter . in this book makes use of causality to further develop various real-time processing versions of AD so 作者: ovation 時間: 2025-3-24 12:11 作者: Aerate 時間: 2025-3-24 18:53 作者: 使出神 時間: 2025-3-24 22:29 作者: Embolic-Stroke 時間: 2025-3-25 02:20
Introduction,003)], removal of highly correlated interband information by data compression or data reduction, data communication, and transmission once hyperspectral imaging sensors are deployed in space. One effective means of dealing with these issues is to develop real-time hyperspectral imaging algorithms th作者: mosque 時間: 2025-3-25 03:44
Simplex Volume Calculationdata dimensionality reduction to make the matrix of full rank. The drawback of this method is that the original volume has been shrunk and the found volume of a dimensionality-reduced simplex is not the true original SV. The other is to use singular value decomposition to find singular values for ca作者: Abrupt 時間: 2025-3-25 10:22 作者: CYT 時間: 2025-3-25 12:34
Target-Specified Virtual Dimensionality for Hyperspectral Imageryrent conclusions have been drawn about VD. This issue was realized by Chang (Hyperspectral data processing: algorithm design and analysis, Wiley, Hoboken, 2013), where VD was defined by two types of criteria, data characterization-driven criteria and data representation-driven criteria. This chapter作者: 哪有黃油 時間: 2025-3-25 19:33
Real-Time Recursive Hyperspectral Sample Processing for Active Target Detection: Constrained Energy s yet to be visited should be involved in data processing. Such a property is generally called ., which has unfortunately received little attention in real-time hyperspectral data processing in recent years. This chapter investigates one of the well-known active hyperspectral target detection techni作者: Prophylaxis 時間: 2025-3-25 23:26
Real-Time Recursive Hyperspectral Sample Processing for Passive Target Detection: Anomaly Detectiondetection is a major task in hyperspectral image analysis and has been studied extensively in the literature. Applications of passive target detection include surveillance and monitoring, where no knowledge is required .. Of particular interest in passive target detection is AD, which is generally p作者: Conflict 時間: 2025-3-26 00:14
Recursive Hyperspectral Sample Processing of Automatic Target Generation Process, which are augmented by newly found targets one at a time. This process requires a significant amount of computing time, which will grow exponentially as the number of targets is increased. Another is that it does not have an automatic stopping rule to terminate the process in real time. This chapt作者: Emg827 時間: 2025-3-26 05:21 作者: 簡略 時間: 2025-3-26 12:12 作者: 協(xié)迫 時間: 2025-3-26 14:22 作者: superfluous 時間: 2025-3-26 18:12
Recursive Hyperspectral Sample Processing of Geometric Simplex Growing Algorithmuntered in finding SVs using a matrix determinant calculation, in addition to another issue: the calculated SV may not be a true SV, as pointed out in Chap. .. To resolve this dilemma, Chap. . developed an orthogonal projection (OP)-based growing simplex volume analysis (GSVA) approach, called ortho作者: TOXIC 時間: 2025-3-26 23:31
Recursive Hyperspectral Band Processing for Active Target Detection: Constrained Energy Minimizationand in real time. The RHBP-CEM presented in this chapter allows CEM to perform target detection progressively and recursively whenever bands are available without waiting for the completion of band collection. With such an advantage RHBP-CEM has potential in data transmission and communication, spec作者: DEBT 時間: 2025-3-27 01:19
Recursive Hyperspectral Band Processing for Passive Target Detection: Anomaly Detectionwn and cannot be inspected by prior knowledge, their presence can only be detected by an unsupervised means. Also, because different anomalies respond to certain specific bands in terms of their own unique spectral characteristics, finding anomalies via band processing becomes a necessity. In partic作者: irritation 時間: 2025-3-27 07:47
Chein-I Change illnesses. This chapter examines the limited but growing evidence for the role of the environment in the development and progression of autoimmune diseases, the specific exposures that have been suspected of being involved, the possible mechanisms by which these agents may induce and sustain autoi作者: 指數(shù) 時間: 2025-3-27 10:31
Chein-I Change illnesses. This chapter examines the limited but growing evidence for the role of the environment in the development and progression of autoimmune diseases, the specific exposures that have been suspected of being involved, the possible mechanisms by which these agents may induce and sustain autoi作者: 輕浮女 時間: 2025-3-27 17:18
Chein-I Changin interpreting data from surrogate tissues is necessary and cellular heterogeneity can also complicate interpretation of the data. In addition, DNA methylation within the body of genes can influence the response of the genome to the environment. Hypomethylation of repetitive elements can lead to ge作者: GEN 時間: 2025-3-27 19:35 作者: multiply 時間: 2025-3-27 23:12
Chein-I Changin interpreting data from surrogate tissues is necessary and cellular heterogeneity can also complicate interpretation of the data. In addition, DNA methylation within the body of genes can influence the response of the genome to the environment. Hypomethylation of repetitive elements can lead to ge作者: 收到 時間: 2025-3-28 02:42
Real-Time Recursive Hyperspectral Sample and Band ProcessingAlgorithm Architectu作者: 清唱劇 時間: 2025-3-28 06:46 作者: 改良 時間: 2025-3-28 11:22
Chein-I Changectious diseases, inflammation and rheumatoid arthritis, asthma, autism and other neurodevelopmental disorders, psychiatric disorders, diabetes, obesity and metabolic disorders, and atherosclerosis..With contri978-94-007-2495-2作者: 美麗的寫 時間: 2025-3-28 18:04 作者: 變形詞 時間: 2025-3-28 20:42 作者: 螢火蟲 時間: 2025-3-29 01:43
Chein-I Changs directed against self structures (autoantigens). While these often incurable disorders appear to be rapidly increasing in recognition throughout the world, their rarity, heterogeneity and complex etiologies have limited our understanding of their pathogeneses. The precise mechanisms for the develo作者: HEDGE 時間: 2025-3-29 04:47 作者: 職業(yè) 時間: 2025-3-29 09:42 作者: 反感 時間: 2025-3-29 14:40 作者: 慷慨不好 時間: 2025-3-29 15:44
Chein-I Changetics in humans.A guide how to design a study on epigenetics.The exploding field of epigenetics is challenging the dogma of traditional Mendelian inheritance. Epigenetics plays an important role in shaping who we are and contributes to our prospects of health and disease.? While early epigenetic res作者: WATER 時間: 2025-3-29 21:30 作者: 夾死提手勢 時間: 2025-3-30 00:59
Chein-I ChangExplores recursive structures in algorithm architecture.Implements algorithmic recursive architecture in conjunction with progressive sample and band processing.Derives Recursive Hyperspectral Sample 作者: aplomb 時間: 2025-3-30 07:35 作者: 反感 時間: 2025-3-30 09:46 作者: neutralize 時間: 2025-3-30 12:59 作者: audiologist 時間: 2025-3-30 17:28
Mihai Lupu,Evangelos Kanoulas,Fernando LoizidesState-of-the-art research.Fast-track conference proceedings.Unique visibility
