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"Penelitian ini bertujuan untuk melakukan optimisasi proses hyperspectral unmixing dengan menggunakan norm L2,1 untuk menerapkan properti collaborative sparse pada semua piksel yang ada dalam dataset dan menggunakan regularisasi total variation untuk meningkatkan kualitas citra yang dihasilkan. Performa algoritma L2,1-TV akan dibandingkan dengan algoritma yang sudah ada yaitu SUnSAL, CLSUnSAL, dan SUnSAL-TV. Hasil eksperimen menggunakan data sintesis (DS, FR1, FR2, FR3) dan data asli (Urban) menunjukkan bahwa algoritma yang dikembangkan memiliki hasil yang lebih baik dibandingkan algoritma yang sudah ada secara kualitatif dan kuantitatif terutama pada tingkat SNR yang lebih rendah. Penelitian menggunakan dataset DS menunjukkan algoritma L2,1-TV memiliki kenaikan nilai SRE sebesar 262.42%, 92.93%, dan 74.71% serta penurunan nilai RMSE sebesar 74.23%, 67.39%, dan 14.29% pada ketiga tingkat SNR dibandingkan algoritma lainnya. Untuk dataset FR1, L2,1-TV memiliki kenaikan nilai SRE sebesar 52.81% serta penurunan nilai RMSE sebesar 46.01% pada SNR 20 dB namun memiliki nilai SRE dan RMSE yang lebih buruk pada SNR 30 dB dan 40 dB yaitu sebesar 3.59% dan 7% untuk SRE serta 9.43% dan 2.86% untuk RMSE. Untuk dataset FR2, L2,1-TV memiliki kenaikan nilai SRE sebesar 102.76% dan 1.84% pada SNR 20 dB dan 40 dB serta penurunan nilai RMSE sebesar 52.78% pada SNR 20 dB namun memiliki nilai SRE yang lebih buruk pada tingkat SNR 30 dB berupa 4.96% serta RMSE yang lebih buruk pada SNR 30 dB dan 40 dB berupa 9.8% dan 3.57%. Untuk dataset FR3, L2,1-TV memiliki kenaikan nilai SRE sebesar 96.44%, 0.97% dan 5.42% pada ketiga tingkat SNR serta penurunan nilai RMSE sebesar 61.33% dan 3.13% untuk SNR 20 dB dan 40 dB. Pada SNR 30 dB, L2,1-TV memiliki nilai RMSE yang sama dengan algoritma lainnya. Untuk dataset Urban, algoritma L2,1-TV memiliki kenaikan nilai SRE sebesar 2.6% dan penurunan nilai RMSE sebesar 1.15% dibandingkan algoritma lainnya.

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In this study, we construct an algorithm to optimize the hyperspectral unmixing problem using L2,1 norm and Total Variation regularization to reduce error. Specifically, our research aims to improve the unmixing results by applying L2,1 norm to impose collaborative sparsity on all pixels in the data set and adding Total Variation regularization to improve the smoothness of resulting image. We compared our algorithm performance to existing algorithm such as SUnSAL, CLSUnSAL, and SUnSAL-TV. Our experimental results with both synthetic and real hyperspectral data show improvement in RMSE and SRE compared to other methods especially on lower SNR. On our experiment using DS dataset, L2,1-TV algorithm showed improvement in SRE by 262.42%, 92.93%, and 74.21% as well as RMSE by 74.23%, 67.39%, dan 14.29% for SNR level 20 dB, 30 dB, and 40 dB respectively compared to other methods. On experiment using FR1 dataset, L2,1 algorithm showed improvement in both SRE and RMSE by 52.81% and 46.01% respectively for SNR level 20 dB compared to other methods but have worse results in SNR level 30 dB and 40 dB. On experiment using FR2 dataset, L2,1-TV algorithm showed improvement in SRE by 102.76% and 1.84% for SNR level 20 dB and 40 dB respectively but have worse result in SNR level 30 dB by 4.96%. L2,1-TV also showed improvement in RMSE by 52.78% for SNR level 20 dB but have worse result in SNR level 30 dB and 40 dB by 9.8% and 3.57% respectively. On experiment using FR3 dataset, L2,1-TV algorithm showed improvement in SRE by 96.44%, 0.97%, and 5.42% for SNR level 20 dB, 30 dB, and 40 dB respectively. L2,1-TV algorithm also showed improvement in RMSE by 61.33% and 3.13% for SNR level 20 dB and 40 dB respectively as well as having the same RMSE value for SNR level 30 dB compared to other methods. On experiment using real data, L2,1-TV algorithm showed improvement in both SRE and RMSE by 2.6% and 1.15% respectively compared to other methods."

"Lively, engaging introduction to geomorphology and earth surface processes, written by one of the world's leading experts in the field"

""An Introduction to Geological Structures &​ Maps is a concise text that leads the students in easy stages from the simplest ideas on geological structures right through to more advanced geological mapping techniques. The approach is designed to help students with little or no supervision: each new topic is simply explained and illustrated by figures, and exercises are set on successive maps. If students are unable to complete a problem, they can read on to obtain more specific instructions on how theory may be used to solve it." "This considerably enlarged seventh edition is even more user friendly and mirrors present trends in map syllabuses. It includes photographs that will add to the understanding of geological structures already illustrated by text-figures and block diagrams in the appendix. The appendix has been fully updated and now includes completed sections of all maps and solutions to the map problems, which enables the reader to check that his or her attempts have been successfully carried out."--"

"[ABSTRAKJika dilakukan observasi, bentuk permukaan bumi setiap saat selalu berubah-ubahatau mengalami deformasi. Hal ini dapat dipengaruhi oleh faktor alam, manusiaataupun gabungan dari keduanya. Beberapa perubahan bentuk ini dapatmenyebabkan bencana kalau tidak kerugian. Sehingga perlu adanya suatu metodeuntuk melakukan pengukuran dan pemetaan deformasi permukaan tanah.Pada penulisan ini dilakukan study-case pemetaan deformasi yang terjadi padaarea Bandung dan sekitarnya. Fokus dari percobaan adalah mengamati lajudeformasi pertahun pada area tersebut. Teknik PSInSAR dengan kerangka kerjaStaMPS digunakan untuk mengolah data citra PALSAR satelit ALOS menjadipeta laju deformasi.Pemetaan dengan citra radar dipilih karena kemampuan akuisisi citranya yangdapat digunakan pada hampir semua kondisi. Sinyal sensor SAR dapat menembusawan sehingga dapat mengatasi kesulitan dalam menganalisis kawasan yangdiselimuti awan, seperti Bandung. Sensor ini juga dapat bekerja siang-malam,karena sensor bekerja secara aktif dalam memancarkan dan menangkap sinyalnya.Ketelitian hingga ukuran mm per tahun dapat diperoleh dengan menggunakanteknik SAR persistent scatterrer interferometry. Selain itu dengan melakukananalisis ampitudo dan fase pada beberapa interferogram menggunakan kerangkakerja StaMPS, pengukuran juga dapat dilakukan walaupun pada area yangberkoherensi rendah. Misalnya sebagian besar area terselimuti oleh vegetasiataupun keadaan atmosfir yang menyelimuti, seperti kawasan Bandung dansekitarnya.Dari hasil pemetaan dapat diamati bahwa terjadi penurunan permukaan tanah padahampir seluruh kawasan perkotaan Bandung. Penyusutan dengan nilai tertinggiterjadi pada daerah Cimahi dan Bojong sebesar 13.5 cm per tahun. Didugapenurunan muka tanah pada area perkotaan Bandung diakibatkan oleh pemakaianair tanah yang berlebih terutama pada kawasan industri.

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ABSTRACTIf the observation is made, it is observable that earth surface is always changing orconstantly undergo a deformation. This can be caused by natural factors, human,or a combination of both. Some of these deformation can lead to disaster if notlosses. Because of that, it important to have method to measure and ultimatelymap the ground surface deformation.In this study-case, the map of deformation that occurs in Bandung and itsurrounding areas was made. The focus of the experiment was to observe thedeformation rate per year in these areas. PSInSAR technique with StaMPSframework was used to processed PALSAR images of ALOS satellite.Mapping with radar images was chosen because of radar imaging system have thecapabilities to operate at almost any condition. SAR sensor signal can penetrateclouds this can be used to overcome the difficulties in analyzing the cloudy areas,such as Bandung. This sensor can also work day and night, because it is activesensor, it emit and recieve it's own signal.Using the SAR persistent scatterrer interferometry technique, measurementaccuracy up to mm per year can be obtained. In addition, by analyzing amplitudeand phase of multiple interferograms using StaMPS framework, the measurementeven with low coherence image can be done. For example, most of the area-ofinterestis covered by vegetation or randomly high atmosphere retardation atsurrounds area, such as Bandung and it?s surrounding areas.Form the deformation map, can be observed that there was a subsidence in almostall urban areas in Bandung. The highest value of ground shrinkage occurred inCimahi and Bojong with 13.5 cm per year. Suspected, land subsidence of urbanareas in Bandung was caused by excessive use of ground water, especially in theindustrial area.;If the observation is made, it is observable that earth surface is always changing orconstantly undergo a deformation. This can be caused by natural factors, human,or a combination of both. Some of these deformation can lead to disaster if notlosses. Because of that, it important to have method to measure and ultimatelymap the ground surface deformation.In this study-case, the map of deformation that occurs in Bandung and itsurrounding areas was made. The focus of the experiment was to observe thedeformation rate per year in these areas. PSInSAR technique with StaMPSframework was used to processed PALSAR images of ALOS satellite.Mapping with radar images was chosen because of radar imaging system have thecapabilities to operate at almost any condition. SAR sensor signal can penetrateclouds this can be used to overcome the difficulties in analyzing the cloudy areas,such as Bandung. This sensor can also work day and night, because it is activesensor, it emit and recieve it's own signal.Using the SAR persistent scatterrer interferometry technique, measurementaccuracy up to mm per year can be obtained. In addition, by analyzing amplitudeand phase of multiple interferograms using StaMPS framework, the measurementeven with low coherence image can be done. For example, most of the area-ofinterestis covered by vegetation or randomly high atmosphere retardation atsurrounds area, such as Bandung and it?s surrounding areas.Form the deformation map, can be observed that there was a subsidence in almostall urban areas in Bandung. The highest value of ground shrinkage occurred inCimahi and Bojong with 13.5 cm per year. Suspected, land subsidence of urbanareas in Bandung was caused by excessive use of ground water, especially in theindustrial area., If the observation is made, it is observable that earth surface is always changing orconstantly undergo a deformation. This can be caused by natural factors, human,or a combination of both. Some of these deformation can lead to disaster if notlosses. Because of that, it important to have method to measure and ultimatelymap the ground surface deformation.In this study-case, the map of deformation that occurs in Bandung and itsurrounding areas was made. The focus of the experiment was to observe thedeformation rate per year in these areas. PSInSAR technique with StaMPSframework was used to processed PALSAR images of ALOS satellite.Mapping with radar images was chosen because of radar imaging system have thecapabilities to operate at almost any condition. SAR sensor signal can penetrateclouds this can be used to overcome the difficulties in analyzing the cloudy areas,such as Bandung. This sensor can also work day and night, because it is activesensor, it emit and recieve it's own signal.Using the SAR persistent scatterrer interferometry technique, measurementaccuracy up to mm per year can be obtained. In addition, by analyzing amplitudeand phase of multiple interferograms using StaMPS framework, the measurementeven with low coherence image can be done. For example, most of the area-ofinterestis covered by vegetation or randomly high atmosphere retardation atsurrounds area, such as Bandung and it’s surrounding areas.Form the deformation map, can be observed that there was a subsidence in almostall urban areas in Bandung. The highest value of ground shrinkage occurred inCimahi and Bojong with 13.5 cm per year. Suspected, land subsidence of urbanareas in Bandung was caused by excessive use of ground water, especially in theindustrial area.]"