Ocean Colour Remote Sensing Of Case 2 Waters Using An Optimised Neural Network

• Main Author: Anwar, Saumi Syahreza
• Format: Thesis
• Language: English
• Published: 2016
• Subjects: QC1-999 Physics
• Online Access: http://eprints.usm.my/32007/1/SAUMI_SYAHREZA_24%28NN%29.pdf; http://eprints.usm.my/32007/
• Institution: Universiti Sains Malaysia

Kajian ini memberi tumpuan pada pembangunan algoritma baru untuk mendapatkan produk warna laut di kawasan perairan kes 2 menggunakan model rangkain neural (NN) dari pelbagai jenis data penderiaan jauh sebagai input. Model NN dan parameter latihan dioptimumkan dengan input yang dipilih berdasarkan analisis korelasi (CA) dan analisis komponen utama (PCA). Di pesisiran pantai Kelantan, penggunaan data spektra pantulan in situ dan simulasi penderiaan jauh satelit telah dikaji untuk menganggar dua parameter kejelasan iaitu kekeruhan (TURB) dan cakera kedalaman Sechhi (SDD). Data simulasi Landsat TM dan AVNIR-2 diuji berdasarkan pengukuran spektra pantulan in situ menggunakan ASD spectroradiometer. Keputusan menunjukkan bahawa data simulasi Landsat TM dan AVNIR-2 membenarkan tafsiran TURB dan SDD. Di kawasan pesisiran pantai Pulau Pinang, penggunaan data satelit penderiaan jauh tunggal dan gabungan pelbagai tarikh telah dikaji untuk menganggar sediment terampai (Cs) dan kepekatan klorofil (Cchl). Pengukuran sampel air pelbagai tarikh telah dibuat selari dengan perolehan data satelit Landsat TM dan AVNIR-2 di lokasi terpilih dari Februari 1999 hingga Mac 2011. This study focused on the development of the new algorithm for retrieving ocean colour products of Case 2 water types using the neural network (NN) model and multiple types of remotely sensed data as inputs. The NN model architecture and training parameters were optimised, with inputs being selected based correlation analysis (CA) and principal component analysis (PCA). In Kelantan coastal waters, the use of in situ reflectance spectra and simulated satellite data for estimation of two water clarity parameters namely turbidity (TURB) and Secchi disk depth (SDD) have been studied. The simulated Landsat TM and AVNIR-2 data were tested against in situ reflectance spectra measurements using ASD Spectroradiometer. The results show that the simulated Landsat TM and AVNIR-2 data enables the interpretation of TURB and SDD. In Penang coastal area, the use of single and multitemporal remote sensing data for estimation of Cs and Cchl has been studied. Multidate in-situ water sample measurements concurrent with Landsat TM and AVNIR-2 satellite data were obtained in selected locations from February 1999 to March 2011.