Computational intelligence in optical remote sensing image processing

Highlights

• We review the computational intelligence in optical remote sensing image processing.

• Feature representation and selection based on computational intelligence are reviewed.

• Classification using in evolutional computation and neural networks are reviewed.

• Change detection based on computational intelligence are reviewed.

• The core potentials of computational intelligence for optical remote sensing image processing are discussed.

Abstract

With the ongoing development of Earth observation techniques, huge amounts of remote sensing images with a high spectral-spatial-temporal resolution are now available, and have been successfully applied in a variety of fields. In the process, they bring about great challenges, such as high-dimensional datasets (the high spatial resolution and hyperspectral features), complex data structures (nonlinear and overlapping distributions), and the nonlinear optimization problem (high computational complexity). Computational intelligence techniques, which are inspired by biological systems, can provide possible solutions to the above-mentioned problems. In this paper, we provide an overview of the application of computational intelligence technologies in optical remote sensing image processing, including: 1) feature representation and selection; 2) classification and clustering; and 3) change detection. Subsequently, the core potentials of computational intelligence for optical remote sensing image processing are delineated and discussed.

Introduction

Remote sensing is an important Earth observation technique that is able to acquire remote sensing images and obtain object information without making physical contact, through the use of sensors on satellites or aircraft [[1], [2]]. Optical remote sensing imaging, in particular, is a major branch of remote sensing, whose products have been widely used in many real-world applications, such as global land-cover mapping [3], vegetation monitoring [4], water quality monitoring [5], urban climate and environmental studies [6], detection of forest fires [7], mineral exploration [8], oil spill detection [9], and precision agriculture [10].

Optical remote sensing images can be characterized by three resolutions, namely: 1) the spatial resolution [11] (e.g. QuickBird, IKONOS, and the Chinese Gaofen-1 system); 2) the spectral resolution [11] (e.g. Hyperion, AVIRIS, HYDICE, and ROSIS); and 3) the temporal resolution [11] (e.g. MODIS). The availability of remote sensing images with high spatial-spectral-temporal resolutions provides great potential for the development of remote sensing data processing, including: 1) the feature pre-processing (e.g. feature representation and hyperspectral band selection); and 2) the specific applications (e.g. supervised and unsupervised classification, and change detection). Unfortunately, the conventional remote sensing image processing methods struggle to handle the new challenges brought by the problem complexity, including both the data complexity and the model complexity.

• “Data complexity” means that the spatial-spectral-temporal resolutions of the remote sensing images have become higher. Both the data volumes and dimensionality have significantly increased, and the data distribution in the feature space has become more complex and sparse. The commonly used Gaussian distribution cannot model such remote sensing data very well. Thus, the traditional approaches cannot work well on certain tasks in remote sensing image processing (e.g. classification [12] and change detection [13]). Because some of the traditional approaches (e.g. the maximum likelihood classifier (MLC) and support vector machine (SVM)) transform such problems into a classification or regression problem and resolve them by using known training samples to predict the corresponding attributes (e.g. the class label or change label), they can only achieve a satisfactory performance under certain assumptions (a normal or other distribution) or conditions (a small number of training samples). However, the data complexity makes it difficult for these assumptions or conditions to be satisfied. Therefore, the data complexity raises new challenges for remote sensing image processing.

• “Model complexity”. In order to deal with the data complexity, different models have been designed with both a powerful optimization capability and the ability to handle multi-objective problems. On the one hand, certain tasks of remote sensing image processing can be transformed into a continuous optimization problem (e.g. clustering [14]) or a knapsack problem (e.g. hyperspectral band selection [15], endmember extraction [16], and change detection [17]), which is a representative NP-hard problem. Although the traditional optimization methods such as the mountain climbing based methods are time-efficient in remote sensing image processing, they are sensitive to the initial values, and it is easy for them to get stuck in local optima. On the other hand, in certain remote sensing image processing tasks (e.g. clustering [18] or hyperspectral unmixing [[19], [20]]), as a result of the uncertainty of the data structure, differently designed objective functions are often conflicting and achieve different performances on different remote sensing images, which can dramatically influence the generalization capability of the traditional approaches. Therefore, the newly designed model, which needs to take multiple objective functions into consideration, becomes complex. Accordingly, different objective functions are often combined into a single objective function with the help of a regularization parameter. However, the determination of the regularization parameter is not an easy task. Therefore, the model complexity raises other new challenges for remote sensing image processing.

The remaining parts of this paper are organized as follows. Section II describes the potential of computational intelligence in optical remote sensing image processing. Section III reviews the workflow of remote sensing image processing and the applications of computational intelligence in the different fields of optical remote sensing image processing, including: 1) feature representation and hyperspectral band selection; 2) classification and clustering; and 3) change detection. Section IV provides the conclusion and specifies some future research directions.