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Remote sensing has been one of the most common approaches to acquire relevant information for Earth observation, using both active and passive devices. With different sensors and platforms, various aspects of the Earth's surface can be observed for analysis. Facing the considerable multimodal heterogeneous remote sensing data, how to effectively extract the key information to support the varying needs from different applications has become a big challenge. Due to different characteristics of the remote sensing datasets acquired from various sensors and conditions, effective technologies are demanded for accurate and efficient data interpretations. In this thesis, the object identification and pixel level classification task on the multimodal remote sensing data is particularly focused, especially the Hyperspectral imagery (HSI), Multispectral Instrument (MSI) and the Syntenic Aperture Radar (SAR) to facilitate different Earth observation tasks. Although HSI has rich spectral information to enable the high discrimination ability for subtle spectral differences on materials, it suffers from different sources of noise and highly correlated spectral information. Despite of various approaches which have been proposed for denoising/smoothing and data reduction, the efficacy is still affected even using the corrected dataset (the data with the water absorption bands and noise bands discarded), especially when the training samples are limited and unbalanced. In this thesis, A signal decomposing technology is introduced into HSI for more effective feature extraction and improved data classification, where a superpixelwise multiscale Prophet model (SMP) is proposed for noise-robust feature extraction and effective classification of the HSI. The prophet model can deeply dig into the complex latent structures of HSI and extract features with enlarged interclass diversity and improved intraclass similarity. Firstly, the first three principal components of the HSI are extracted for implementing the superpixelwise segmentation, where pixels are grouped into regions with adaptively determined sizes and shapes. Secondly, a multiscale prophet model is utilized to extract the multiscale informative trend components from the average spectrum of each superpixel. Taking the multiscale trend signal as the input feature, the HSI data are further classified superpixelwisely, which is further refined by majority voting based decision fusion. Experiments on three publicly available datasets have fully validated the efficacy and robustness of the proposed approach, when benchmarked with several state-of-the-art classifiers, including some typical spatial-spectral methods and deep learning classifiers. In addition, both quantitative and qualitative assessment has validated the efficacy of our approach in noise-robust classification of HSI even with limited training samples, especially in classifying uncorrected data (without pre-filtering the water-absorption and noisy bands). The proposed SMP method focuses on the multiscale noise removal in spectral domain, which shows limited performance in spatial noise filter. In order to explore spatial noise robust features whilst reducing the data dimension, a novel effective and efficient feature extraction framework is proposed for the HSI, namely Multiscale 2D singular spectrum analysis (2D-SSA) with principal component analysis (2D-MSSP). This method investigates the multiscale strategy in the spatial domain by combining the dimension reduction in the spectral domain. First, multiscale 2D-SSA is applied to exploit the multiscale spatial features in each spectral band of HSI via extracting the varying trends in different windowing scales. Taking the extracted trend signals at each scale level as the input features, the principal component analysis (PCA) is employed to the spectral domain for dimensionality reduction and spatial-spectral feature extraction. The derived spatial-spectral features in each scale are separately classified and then fused at the decision level for efficacy. As our 2D-MSSP method can extract features whilst removing the noise simultaneously in both the spatial and spectral domains, it is proven to be particularly noise-robust for data classification of HSI, when being benchmarked with several state-of-the-art classifiers, including effectively classifying of uncorrected data with limited training samples. The MSI and SAR are also increasingly used in Earth observation and remote sensing. The capabilities of all-weather and all-day operation of the SAR enable it to be compatible with MSI, which usually suffers from severe weather conditions, e.g., cloud-covering but benefits from high spatial and spectral resolutions. Based on this, combination of SAR and MSI data is applied for the detection of offshore infrastructure, which is particularly challenging due to the noisy and vast ocean surface. In this thesis, we propose an automatic method for the geolocation and size evaluation of offshore infrastructure through the combination of Sentinel-1 SAR data and Sentinel-2 MSI imagery. Specifically, three strategies, transformed median composite, 2D-Singular Spectrum Analysis (SSA) filtering and threshold segmentation, are applied to first extract the 'guide area' of the infrastructure in the Sentinel-1 images, followed by applying morphological operations on a cloud free Sentinel-2 true color image of the 'guide area' to obtain the precise location as well as estimating the size of each structure. For each scene, five time-series Sentinel-1 data and one Sentinel-2 image are used for automatic identification. With validation against ground truth data of Scottish waters from the baseline and closing bays, to the limit of the Exclusive Economic Zone of Scotland, an area of 371,915 km2, our method automatically identifies 329 objects with an omission error of 1.20% and a commission error of 0%. For the size evaluation of wind turbine, oil/gas platform and semi-permanent object, the achieved size errors are around 1, 2 and 13 pixels, respectively in Sentinel-2 image. The method provides an effective technique for the identification of offshore infrastructure.