Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

Abstract

Scene classification of high-resolution remote sensing (HRRS) imagery is an important task in the intelligent processing of remote sensing images and has attracted much attention in recent years. Although the existing scene classification methods, e.g., the bag-of-words (BOW) model and its variants, can achieve acceptable performance, these approaches strongly rely on the extraction of local features and the complicated coding strategy, which are usually time consuming and demand much expert effort. In this paper, we propose a fast binary coding (FBC) method, to effectively generate efficient discriminative scene representations of HRRS images. The main idea is inspired by the unsupervised feature learning technique and the binary feature descriptions. More precisely, equipped with the unsupervised feature learning technique, we first learn a set of optimal "filters" from large quantities of randomly-sampled image patches and then obtain feature maps by convolving the image scene with the learned filters. After binarizing the feature maps, we perform a simple hashing step to convert the binary-valued feature map to the integer-valued feature map. Finally, statistical histograms computed on the integer-valued feature map are used as global feature representations of the scenes of HRRS images, similar to the conventional BOW model. The analysis of the algorithm complexity and experiments on HRRS image datasets demonstrate that, in contrast with existing scene classification approaches, the proposed FBC has much faster computational speed and achieves comparable classification performance. In addition, we also propose two extensions to FBC, i.e., the spatial co-occurrence matrix and different visual saliency maps, for further improving its final classification accuracy.

Remote Sensing Image Scene Classification: Benchmark and State of the Art

“…To remedy the limitations of handcrafted features, learning features automatically from images is considered as a more feasible strategy. In recent years, unsupervised feature learning from unlabeled input data has become an attractive alternative to handcrafted features and has made significant progress for remote sensing image scene classification [20,26,28,33,37,54,63,87,95,[126][127][128][129][130][131][132][133][134]. Unsupervised feature learning aims to learn a set of basis functions (or filters) used for feature encoding, in which the input of the functions is a set of handcrafted features or raw pixel intensity values and the output is a set of learned features.…”

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

“…Interpretation of High-resolution Satellite Images 1) Scene Classification: Scene classification, which aims to automatically assign a semantic label to each scene image, has been an active research topic in the field of high-resolution satellite images in the past decades [68][69][70][71][72][73][74]. As a key problem in the interpretation of satellite images, it has widespread applications, including object detection [75,76], change detection [77], urban planning, land resource management, etc.…”

Multi-Task Joint Sparse and Low-Rank Representation for the Scene Classification of High-Resolution Remote Sensing Image

“…Semantic-based scene classification has been widely applied in HRS image scene interpretation [15,16]. It is usually difficult to understand and recognize scene categories because of the high complexity of spatial and structural patterns in the massive HRS satellite images [17]. Therefore, feature representation in each scene is a key step and highly demanded for accurate scene classification.…”