A Novel Approach to Supervised Hyperspectral Image Classification

一個新穎的方法實現高光譜監督式遙測影像分類

張陽郎(Yang-Lang Chang) ； 韓欽銓(Chin-Chuan Han) ； 范國清(Kuo-Chin Fan) ； 陳錕山(K. S. Chen)

《航測及遙測學刊》 9卷4期 (2004/12) Pp. 47-70

https://doi.org/10.6574/JPRS.2004.9(4).5

摘要

「高光譜」遙測影像(Hyperspectral Imagery)為遙測影像之先進技術，遙測影像頻譜解析度由原數個頻譜解析度的一般感測器、至數十個頻譜解析度之「多頻譜感測器」(Multispectral)、到數百個頻譜解析度的「高光譜感測器」(Hyperspectral)、乃至於數千個頻譜解析度之「超高光譜感測器」(Ultraspectral) , 持續地進步演進著。「高光譜」解析度感測器已廣泛應用於衛星遙測影像之識別、醫學影像的診斷、工業產品之檢驗、飛機及其他精密機器設備之非破害性檢查等應用上，「高光譜」遙測影像技術業已成為遙測影像中一個新興且重要的研究領域。本文提出一個適用於「高光譜」遙測影像分類的新演算法，主要有兩個實現步驟，第一個步驟為「貪婪模組特徵空間」(Greedy Modular Eigenspaces)，第二為「布林濾波器」(Positive Boolean Function)。藉由校正過後的完整台灣『高光譜』遙測影像資料，以及實地測量的地表真實資料，來實際證明「貪婪模組特徵空間」的方法提供了一個絕佳的特徵抽取方式，並為一個最適合「布林濾波器」分類方法的前處理器。本文詳細討論「貪婪模組特徵空間」演算法之推導、完整描述「布林濾波器」的基礎理論，以及詳細分析他們之間的關係，並針對二者的特性加以推演，提出適用於一般「高維資料」(High-Dimensional Data)資料分類的解決方法。最後經由實驗驗證並與其他傳統「多頻譜感測器」遙測影像資料分類方法作一比較，印證了本方法非常適用於「高維資料」分類的特性。

關鍵字

主軸分析 ； 高光譜監督式分類 ； 貪婪模組特徵空間 ； 布林函數 ； 堆疊濾波器

並列摘要

This paper presents a new supervised classification technique for hyperspectral imagery, which consists of two algorithms, referred to as greedy modular eigenspace (GME) and positive Boolean function (PBF). The GME method is designed to extract features by a simple and efficient GME feature module. The GME makes use of the data correlation matrix to reorder spectral bands from which a group of feature eigenspaces can be generated to reduce dimensionality. It can be implemented as a feature extractor to generate a particular feature eigenspace for each of the material classes present in hyperspectral data. The residual reconstruction errors (RRE) are then calculated by projecting the samples into different individual GME-generated modular eigenspaces. The PBF is further developed for classification. It is a stack filter built by using the binary RRE as classifier parameters for supervised training. It implements the minimum classification error (MCE) as a criterion so as to improve classification performance. It utilizes the positive and negative sample learning ability of the MCE criteria to improve classification accuracy. The performance of the proposed method is evaluated by MODIS/ASTER airborne simulator (MASTER) images for land cover classification during the Pacrim II campaign. Experimental results demonstrate that the GME feature extractor suits the nonlinear PBF-based multi-class classifier well for classification preprocessing. The proposed approach is not only an effective method for land cover classification in earth remote sensing but also dramatically improves the eigen-decomposition computational complexity compared to the conventional principal components analysis (PCA).

並列關鍵字

principal components analysis (PCA) ； hyperspectral supervised classification ； greedy modular eigenspaces(GME) ； positive Boolean function(PBF) ； stack filter